#hadoop wikipedia

[alcoanalysis]

Intro to Web Scraping

Chances are, if you have access to the internet, you have heard of Data Science. Aside from the buzz generated by the title ‘Data Scientist’, only a few in relevant fields can claim to understand what data science is. The majority of people think, if at all, that a data scientist is a mad scientist type able to manipulate statistics and computers to magically generate crazy visuals and insights seemingly out of thin air.

Looking at the plethora of definitions to be found in numerous books and across the internet of what data science is, the layman’s image of a data scientist may not be that far off.

While the exact definition of ‘data science’ is still a work in progress, most in the know would agree that the data science universe encompasses fields such as:

Big Data

Analytics

Machine Learning

Data Mining

Visualization

Deep Learning

Business Intelligence

Predictive Modeling

Statistics

Data Source: Top keywords

Image Source – Michael Barber

Further exploration of the skillset that goes into what makes a data scientist, consensus begins to emerge around the following:

Statistical Analysis

Programming/Coding Skills: - R Programming; Python Coding

Structured Data (SQL)

Unstructured Data (3-5 top NoSQL DBs)

Machine Learning/Data Mining Skills

Data Visualization

Big Data Processing Platforms: Hadoop, Spark, Flink, etc.

Structured vs unstructured data

Structured data refers to information with a high degree of organization, such that inclusion in a relational database is seamless and readily searchable by simple, straightforward search engine algorithms or other search operation

Examples of structured data include numbers, dates, and groups of words and numbers called strings.

Unstructured data (or unstructured information) is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in fielded form in databases or annotated (semantically tagged) in documents.

Examples of "unstructured data" may include books, journals, documents, metadata, health records, audio, video, analog data, images, files, and unstructured text such as the body of an e-mail message, Web pages, or word-processor document. Source: Unstructured data - Wikipedia

Implied within the definition of unstructured data is the fact that it is very difficult to search. In addition, the vast amount of data in the world is unstructured. A key skill when it comes to mining insights out of the seeming trash that is unstructured data is web scraping.

What is web scraping?

Everyone has done this: you go to a web site, see an interesting table and try to copy it over to Excel so you can add some numbers up or store it for later. Yet this often does not really work, or the information you want is spread across a large number of web sites. Copying by hand can quickly become very tedious.

You’ve tried everything else, and you haven’t managed to get your hands on the data you want. You’ve found the data on the web, but, alas — no download options are available and copy-paste has failed you. Fear not, there may still be a way to get the data out. Source: Data Journalism Handbook

As a data scientist, the more data you collect, the better your models, but what if the data you want resides on a website? This is the problem of social media analysis when the data comes from users posting content online and can be extremely unstructured. While there are some websites who support data collection from their web pages and have even exposed packages and APIs (such as Twitter), most of the web pages lack the capability and infrastructure for this. If you are a data scientist who wants to capture data from such web pages then you wouldn’t want to be the one to open all these pages manually and scrape the web pages one by one. Source: Perceptive Analytics

Web scraping, web harvesting, or web data extraction is data scraping used for extracting data from websites. Web scraping software may access the World Wide Web directly using the Hypertext Transfer Protocol, or through a web browser. While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a bot or web crawler. It is a form of copying, in which specific data is gathered and copied from the web, typically into a central local database or spreadsheet, for later retrieval or analysis. Source: Wikipedia

Web Scraping is a method to convert the data from websites, whether structured or unstructured, from HTML into a form on which analysis can be performed.

The advantage of scraping is that you can do it with virtually any web site — from weather forecasts to government spending, even if that site does not have an API for raw data access. While this method is very powerful and can be used in many places, it requires a bit of understanding about how the web works.

There are a variety of ways to scrape a website to extract information for reuse. In its simplest form, this can be achieved by copying and pasting snippets from a web page, but this can be unpractical if there is a large amount of data to be extracted, or if it spread over a large number of pages. Instead, specialized tools and techniques can be used to automate this process, by defining what sites to visit, what information to look for, and whether data extraction should stop once the end of a page has been reached, or whether to follow hyperlinks and repeat the process recursively. Automating web scraping also allows to define whether the process should be run at regular intervals and capture changes in the data.

https://librarycarpentry.github.io/lc-webscraping/

 Web Scraping with R

Atop any data scientist’s toolkit lie Python and R. While python is a general purpose coding language used in a variety of situations; R was built from the ground up to mold statistics and data. From data extraction, to clean up, to visualization to publishing; R is in use. Unlike packages such as tableau, Stata or Matlab which are skewed either towards data manipulation or visualization, R is a general purpose statistical language with functionality cutting across all data management operations. R is also free and open source which contributes to making it even more popular.

To extend the boundaries limiting data scientists from accessing data from web pages, there are packages based on ‘Web scraping’ available in R. Let us look into web scraping technique using R.

Harvesting Data with RVEST

R. Hadley Wickham authored the rvest package for web scraping using R which will be demonstrated in this tutorial. Although web scraping with R is a fairly advanced topic it is possible to dive in with a few lines of code within a few steps and appreciate its utility, versatility and power.

We shall use 2 examples inspired by Julia Silge in her series cool things you can do with R in a tweet:

Scraping the list of districts of Uganda

Getting the list of MPs of the Republic of Rwanda

[aiandblockchainchronicles]

Building a Private LLM: A Comprehensive Guide

As artificial intelligence (AI) continues to evolve, Large Language Models (LLMs) have become powerful tools for various applications, including customer service automation, content generation, and decision support systems. However, using publicly available LLMs often raises concerns about data security, compliance, and customization. To address these challenges, businesses are increasingly exploring the option of building their own private LLMs. In this guide, we will discuss the step-by-step process of developing a private LLM that aligns with your organizational needs while ensuring privacy, security, and efficiency.

1. Why Build a Private LLM?

Enhanced Data Privacy

Publicly available LLMs process data on external servers, which can raise security risks. A private LLM development ensures that all data remains within your organization’s infrastructure, minimizing the risk of data breaches.

Regulatory Compliance

Industries such as healthcare, finance, and legal services must comply with regulations like GDPR, HIPAA, and SOC 2. A private LLM allows organizations to maintain strict compliance by controlling data access and processing.

Domain-Specific Customization

Most general-purpose LLMs are trained on vast datasets that may not include specialized knowledge relevant to your industry. Training your own LLM on domain-specific data ensures more accurate and relevant responses.

Cost Control

Relying on third-party LLM APIs can be costly, especially for organizations that require frequent queries and data processing. Building a private LLM eliminates ongoing API costs and allows for better budget management in the long run.

2. Setting Up the Infrastructure

Compute Requirements

Training and running an LLM requires significant computing power. Organizations should invest in:

High-performance GPUs or TPUs (e.g., NVIDIA A100, H100, or Google TPU v4)

Scalable cloud-based AI infrastructure (e.g., AWS, GCP, Azure)

On-premises servers for organizations prioritizing security over scalability

Storage and Data Pipelines

A large-scale LLM requires efficient data storage and management. Distributed storage solutions like Hadoop, Ceph, or cloud-based object storage (e.g., Amazon S3) can handle the vast amounts of training data needed.

Software and Frameworks

Selecting the right AI frameworks is crucial for building an effective LLM. Common frameworks include:

TensorFlow and PyTorch for deep learning model development

Hugging Face Transformers for pre-trained model fine-tuning

JAX for high-performance computing optimizations

3. Data Collection and Preprocessing

Sourcing Data

A high-quality dataset is essential for training an effective LLM. Organizations can source data from:

Internal proprietary documents, reports, and customer interactions

Open-source datasets like Wikipedia, Common Crawl, and arXiv

Synthetic data generation when real-world data is limited

Cleaning and Structuring

Raw data often contains noise, inconsistencies, or missing values. Preprocessing steps include:

Removing duplicates and irrelevant text

Standardizing formats (e.g., lowercasing, tokenization)

Filtering biased or low-quality content

Annotation and Labeling

For supervised learning, annotation tools like Prodigy, Label Studio, or Snorkel can help label datasets with relevant tags and classifications.

4. Model Selection and Training

Pretraining vs. Fine-Tuning

Pretraining from scratch: This requires extensive compute resources and massive datasets but allows for full customization.

Fine-tuning existing models: Using pre-trained models like LLaMA, Falcon, or Mistral significantly reduces training costs and time.

Training Strategy

To optimize training efficiency:

Use distributed training across multiple GPUs or TPUs

Implement mixed precision training to reduce memory consumption

Employ gradient checkpointing to manage large-scale model training

Hyperparameter Tuning

Fine-tuning hyperparameters can significantly impact model performance. Key parameters to optimize include:

Learning rate and batch size

Dropout rate to prevent overfitting

Activation functions for improving accuracy

5. Security and Privacy Measures

Federated Learning

Federated learning allows decentralized training by keeping data on local devices while only sharing model updates. This approach enhances privacy without compromising performance.

Differential Privacy

Adding noise to data during training prevents the model from memorizing and exposing sensitive information, making it more secure against attacks.

Encryption & Access Controls

Implement end-to-end encryption for data storage and model communication.

Set up role-based access controls (RBAC) to ensure that only authorized users can access the model.

6. Evaluation and Testing

Benchmarking Performance

To ensure the model meets performance expectations, evaluate it using:

Perplexity: Measures how well the model predicts text sequences

BLEU Score: Evaluates the model’s translation accuracy

ROUGE Score: Assesses text summarization capabilities

Bias & Fairness Testing

AI models can unintentionally develop biases based on their training data. Testing for fairness ensures that the model does not reinforce harmful stereotypes.

Adversarial Testing

Attackers may try to manipulate the LLM’s outputs through adversarial prompts. Running stress tests helps detect vulnerabilities and improve robustness.

7. Deployment Strategies

On-Premises vs. Cloud Deployment

On-premises: Provides full control over security and compliance but requires significant infrastructure investments.

Cloud-based: Offers scalability and lower upfront costs but may pose security risks if not properly managed.

API Integration

Deploy the LLM as an API service to enable seamless integration with existing business applications. REST and gRPC APIs are common choices for connecting AI models with enterprise software.

Latency Optimization

To improve response times, organizations can:

Use model quantization and distillation to reduce model size

Implement caching mechanisms for frequently accessed queries

8. Continuous Monitoring and Updates

Drift Detection

Model performance may degrade over time as language and business requirements evolve. Monitoring for data drift ensures timely updates and retraining.

Retraining and Fine-Tuning

Regularly updating the LLM with fresh data helps maintain accuracy and relevance. Techniques like reinforcement learning with human feedback (RLHF) can further refine model responses.

User Feedback Loops

Implementing a feedback system allows users to report incorrect or biased outputs, enabling continuous improvement through iterative learning.

Conclusion

Building a private LLM empowers organizations with control over data privacy, customization, and compliance while reducing long-term reliance on external AI providers. Although the process requires significant investment in infrastructure, data collection, and model training, the benefits of enhanced security and domain-specific optimizations make it a worthwhile endeavor.

By following this guide, businesses can develop a robust private LLM tailored to their unique needs, ensuring scalability, efficiency, and compliance with industry regulations. As AI technology continues to advance, organizations that invest in private LLMs will be well-positioned to harness the full potential of artificial intelligence securely and effectively.

[dinut699]

DATA SCIENCE… THE UNTOLD STORY...

A few years ago, Joel Grus defined data science in terms of interdisciplinary, mathematical, and statistical fields capable of dealing with the extraction and analysis of huge amounts of data in his book Data Science From Scratch. Wikipedia thus holds that since 2001, the term data science has differentially been ascribed to statistical inquiry, which has been evolved over the years with the fields of computer science and its derivatives. The business today is researching the most effective way of analyzing lots of data obtained across many levels including organization, businesses, or operations. An organization can create large data sets regarding customer behaviors, such as customer transactions, social media interactions, operations, or sensor readings. Data science helps organizations to transform this data into actionable insights that go into driving decisions, strategies, and innovations such as in the following sectors: healthcare, finance, marketing, e-commerce, and many others.

The steps that generally constitute the data science pipeline are cross-functional and include collection, cleaning, processing, analysis, modeling, and interpretation towards the outcome whereby data is transformed into information for decision making. Various techniques applied by professionals include data mining, data visualization, predictive analysis, and machine learning to extract patterns, trends, and relationships among data sets. Data science aspires to assist in data-driven decisions on how to solve complex issues by clear, evidence-based pathways into tangible outcomes.

It is the purpose of the Data Science course in Kerala to bring the students' practical exposure into a fine blend with theoretical knowledge and technical skills, which will ultimately help them excel in this competitive field. It addresses a wider audience-from students to working professionals and busy executives who want to build next-level data-driven decision-making capabilities. These days Kerala fast becomes one of the destinations in technology and innovations these courses have also become relevant yet lucrative for industry opportunities that advance skills quite pertinent to the field. The courses cover a wide array of subjects across topics generally listed:

Introduction to Data Science and Analytics

Methods of Data Collection, Cleaning, and Preprocessing

Statistical Analysis and Exploratory Data Analysis (EDA)

Programming Languages such as Python and R

Machine Learning Algorithms and Model Building

Big Data Technologies (Hadoop, Spark)

Data Visualization Tools (Tableau, Power BI, Matplotlib)

Case Studies and Real-Life Projects

Thus, this is an ordinary Data Science course which is going to impart theoretical concepts with practical observation to apply that knowledge in real-time datasets and situations. Most programs also embed critical thinking, ethical handling of data, and effective communication of analytical results to non-technical stakeholders. 

Competencies with tools and frameworks widely used, such as Pandas, NumPy, Scikit-learn, TensorFlow, and SQL, are further sharpened in these programs. Extensive practical exposure is provided through Capstone projects or from industry assignments that facilitate portfolio creation for the students. 

Data Science course completion opens doors into hundreds of other opportunities that skilled professionals seek within different industries, such as hiring a Data Analyst, Machine Learning Engineer, BI Analyst, or Data Scientist. So whether you are entering the data science career or interested in upgrading your skills to stay current with the industry, a good data science course will equip you with the theory and support to excel in this exciting and impactful area.

[hadooptpoint]

Hadoop Definition and Ecosystems

Hadoop Definition and Ecosystems

Hadoop

Hadoop is one of the powerful technology in today Market,Hadoop is an open source and specially designed for commodity hardware and it is open source,java based frame work  that supports both storage purpose and processing purpose. Hadoop is especially designed for large data sets in distributed computing environment. Apache Hadoop is a part of Apache foundation.

In this Internet world…

[mahiworld-blog1]

Important libraries for data science and Machine learning.

Python has more than 137,000 libraries which is help in various ways.In the data age where data is looks like the oil or electricity .In coming days companies are requires more skilled full data scientist , Machine Learning engineer, deep learning engineer, to avail insights by processing massive data sets.

Python libraries for different data science task:

Python Libraries for Data Collection

Beautiful Soup

Scrapy

Selenium

Python Libraries for Data Cleaning and Manipulation

Pandas

PyOD

NumPy

Spacy

Python Libraries for Data Visualization

Matplotlib

Seaborn

Bokeh

Python Libraries for Modeling

Scikit-learn

TensorFlow

PyTorch

Python Libraries for Model Interpretability

Lime

H2O

Python Libraries for Audio Processing

Librosa

Madmom

pyAudioAnalysis

Python Libraries for Image Processing

OpenCV-Python

Scikit-image

Pillow

Python Libraries for Database

Psycopg

SQLAlchemy

Python Libraries for Deployment

Flask

Django

Best Framework for Machine Learning:

1. Tensorflow :

If you are working or interested about Machine Learning, then you might have heard about this famous Open Source library known as Tensorflow. It was developed at Google by Brain Team. Almost all Google’s Applications use Tensorflow for Machine Learning. If you are using Google photos or Google voice search then indirectly you are using the models built using Tensorflow.

Tensorflow is just a computational framework for expressing algorithms involving large number of Tensor operations, since Neural networks can be expressed as computational graphs they can be implemented using Tensorflow as a series of operations on Tensors. Tensors are N-dimensional matrices which represents our Data.

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2. Keras :

Keras is one of the coolest Machine learning library. If you are a beginner in Machine Learning then I suggest you to use Keras. It provides a easier way to express Neural networks. It also provides some of the utilities for processing datasets, compiling models, evaluating results, visualization of graphs and many more.

Keras internally uses either Tensorflow or Theano as backend. Some other pouplar neural network frameworks like CNTK can also be used. If you are using Tensorflow as backend then you can refer to the Tensorflow architecture diagram shown in Tensorflow section of this article. Keras is slow when compared to other libraries because it constructs a computational graph using the backend infrastructure and then uses it to perform operations. Keras models are portable (HDF5 models) and Keras provides many preprocessed datasets and pretrained models like Inception, SqueezeNet, Mnist, VGG, ResNet etc

3.Theano :

Theano is a computational framework for computing multidimensional arrays. Theano is similar to Tensorflow , but Theano is not as efficient as Tensorflow because of it’s inability to suit into production environments. Theano can be used on a prallel or distributed environments just like Tensorflow.

4.APACHE SPARK:

Spark is an open source cluster-computing framework originally developed at Berkeley’s lab and was initially released on 26th of May 2014, It is majorly written in Scala, Java, Python and R. though produced in Berkery’s lab at University of California it was later donated to Apache Software Foundation.

Spark core is basically the foundation for this project, This is complicated too, but instead of worrying about Numpy arrays it lets you work with its own Spark RDD data structures, which anyone in knowledge with big data would understand its uses. As a user, we could also work with Spark SQL data frames. With all these features it creates dense and sparks feature label vectors for you thus carrying away much complexity to feed to ML algorithms.

5. CAFFE:

Caffe is an open source framework under a BSD license. CAFFE(Convolutional Architecture for Fast Feature Embedding) is a deep learning tool which was developed by UC Berkeley, this framework is mainly written in CPP. It supports many different types of architectures for deep learning focusing mainly on image classification and segmentation. It supports almost all major schemes and is fully connected neural network designs, it offers GPU as well as CPU based acceleration as well like TensorFlow.

CAFFE is mainly used in the academic research projects and to design startups Prototypes. Even Yahoo has integrated caffe with Apache Spark to create CaffeOnSpark, another great deep learning framework.

6.PyTorch.

Torch is also a machine learning open source library, a proper scientific computing framework. Its makers brag it as easiest ML framework, though its complexity is relatively simple which comes from its scripting language interface from Lua programming language interface. There are just numbers(no int, short or double) in it which are not categorized further like in any other language. So its ease many operations and functions. Torch is used by Facebook AI Research Group, IBM, Yandex and the Idiap Research Institute, it has recently extended its use for Android and iOS.

7.Scikit-learn

Scikit-Learn is a very powerful free to use Python library for ML that is widely used in Building models. It is founded and built on foundations of many other libraries namely SciPy, Numpy and matplotlib, it is also one of the most efficient tool for statistical modeling techniques namely classification, regression, clustering.

Scikit-Learn comes with features like supervised & unsupervised learning algorithms and even cross-validation. Scikit-learn is largely written in Python, with some core algorithms written in Cython to achieve performance. Support vector machines are implemented by a Cython wrapper around LIBSVM.

Below is a list of frameworks for machine learning engineers:

Apache Singa is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users.

Amazon Machine Learning  is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology.  It connects to data stored in Amazon S3, Redshift, or RDS, and can run binary classification, multiclass categorization, or regression on said data to create a model.

Azure ML Studio allows Microsoft Azure users to create and train models, then turn them into APIs that can be consumed by other services. Users get up to 10GB of storage per account for model data, although you can also connect your own Azure storage to the service for larger models. A wide range of algorithms are available, courtesy of both Microsoft and third parties. You don’t even need an account to try out the service; you can log in anonymously and use Azure ML Studio for up to eight hours.

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.  Models and optimization are defined by configuration without hard-coding & user can switch between CPU and GPU. Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU.

H2O makes it possible for anyone to easily apply math and predictive analytics to solve today’s most challenging business problems. It intelligently combines unique features not currently found in other machine learning platforms including: Best of Breed Open Source Technology, Easy-to-use WebUI and Familiar Interfaces, Data Agnostic Support for all Common Database and File Types. With H2O, you can work with your existing languages and tools. Further, you can extend the platform seamlessly into your Hadoop environments.

Massive Online Analysis (MOA) is the most popular open source framework for data stream mining, with a very active growing community. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems.

MLlib (Spark) is Apache Spark’s machine learning library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.

mlpack, a C++-based machine learning library originally rolled out in 2011 and designed for “scalability, speed, and ease-of-use,” according to the library’s creators. Implementing mlpack can be done through a cache of command-line executables for quick-and-dirty, “black box” operations, or with a C++ API for more sophisticated work. Mlpack provides these algorithms as simple command-line programs and C++ classes which can then be integrated into larger-scale machine learning solutions.

Pattern is a web mining module for the Python programming language. It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and  visualization.

Scikit-Learn leverages Python’s breadth by building on top of several existing Python packages — NumPy, SciPy, and matplotlib — for math and science work. The resulting libraries can be used either for interactive “workbench” applications or be embedded into other software and reused. The kit is available under a BSD license, so it’s fully open and reusable. Scikit-learn includes tools for many of the standard machine-learning tasks (such as clustering, classification, regression, etc.). And since scikit-learn is developed by a large community of developers and machine-learning experts, promising new techniques tend to be included in fairly short order.

Shogun is among the oldest, most venerable of machine learning libraries, Shogun was created in 1999 and written in C++, but isn’t limited to working in C++. Thanks to the SWIG library, Shogun can be used transparently in such languages and environments: as Java, Python, C#, Ruby, R, Lua, Octave, and Matlab. Shogun is designed for unified large-scale learning for a broad range of feature types and learning settings, like classification, regression, or explorative data analysis.

TensorFlow is an open source software library for numerical computation using data flow graphs. TensorFlow implements what are called data flow graphs, where batches of data (“tensors”) can be processed by a series of algorithms described by a graph. The movements of the data through the system are called “flows” — hence, the name. Graphs can be assembled with C++ or Python and can be processed on CPUs or GPUs.

Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). Using Theano it is possible to attain speeds rivaling hand-crafted C implementations for problems involving large amounts of data. It was written at the LISA lab to support rapid development of efficient machine learning algorithms. Theano is named after the Greek mathematician, who may have been Pythagoras’ wife. Theano is released under a BSD license.

Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community.

Veles is a distributed platform for deep-learning applications, and it’s written in C++, although it uses Python to perform automation and coordination between nodes. Datasets can be analyzed and automatically normalized before being fed to the cluster, and a REST API allows the trained model to be used in production immediately. It focuses on performance and flexibility. It has little hard-coded entities and enables training of all the widely recognized topologies, such as fully connected nets, convolutional nets, recurent nets etc.

[clintonpaquin]

What makes a Senior developer a Senior developer?

I've been programming in PHP/MySQL since early 2006 (that's over 5 years experience) and have had the privilege of working at startups and at some very established big brand/recognition environments (check my resume for more).   I've always been a go-getter with a serious 'can-do' attitude.  I've been a team player, a leader, and driver of technology.  So naturally I felt I was a Senior/Lead developer.

Recently I started exploring the job market and found myself wondering,  what makes a Senior developer a Senior developer?  I did some searching around, but it's harder to find a concrete answer than I thought.  I looked through wikipedia, salary.com, monster.com, and several other job related websites with no avail.  So, where are the descriptions of the levels of programmer and how do I fit on the spectrum?  I finally found a great answer over at stackoverflow in an article titled  What is your definition of a Entry Level/Junior/Mid/Senior Developer?

Senior: Someone who knows a wide range of the business arena or is a specialist in an area. Expert in language. Can work on most levels of code unsupervised and requires minimal guidance. Can guide lower grades. Interested in furthering product and practices as well as 'doing the job'. Uses initiative. Team Leader: For those wanting to branch out into management and leave the coal-face behind.

I was finding that a lot of the interviews I was getting were with companies that not only wanted me to be a rockstar programmer, team leader, and driver of technology they also wanted me to be an algorithm theorist, a systems operations master, and a technology strategy guru.  

I was rocking the coding exams and personality screenings but found myself behind the curve in systems architecture and technology strategy implementation.  Which DB engine should we use?  How about which data caching strategy to apply?  Should we use Hadoop, Redis, Doctrine?  The simple answer is, I'm not sure!  I've looked at all of these technologies, but have never implemented any of them in practice!  What it boils down to is that in all of my industry experience I've always had a head of technology or a CTO person driving these decisions.  

I'm a PHP/MySQL guy at heart and the sysops decisions have never really been in my wheelhouse.  Command of the language (check), ability to develop large projects (check), deal with with customers and ability to guide others (check).  I realized that in reality, I should be marketing/labeling myself as more of a 'Mid-Senior' level developer than a Senior.

Mid-Level->Senior This one is the difficult one - the person must show command of the language, be able to develop larger projects, deal with customers and be starting to guide others. In simple terms, this person is showing signs of being a guru. Senior level is an elite status.

I've never been one for titles, heck call me Janitor if want, as long as I'm filling a role that help a team move forward.  I excel in mentoring to others, writing bullet proof PHP/MySQL, and working on large scale projects.  I guess I'm more of a Mid-Senior talent (with lead experience) than a Senior developer.  Where do you fall on the spectrum?

[trustzy]

Kiwix downloadable contest zim files

Kiwix downloadable contest zim files install#

Kiwix downloadable contest zim files archive#

Kiwix downloadable contest zim files software#

SQL files for the pages and links are also available.

all-titles-in-ns0.gz – Article titles only (with redirects).

2 – Current revisions only, all pages (including talk).

2 – Current revisions only, no talk or user pages this is probably what you want, and is over 19 GB compressed (expands to over 86 GB when decompressed).

Download the data dump using a BitTorrent client (torrenting has many benefits and reduces server load, saving bandwidth costs).

English Wikipedia dumps in SQL and XML: dumps.

Dumps from any Wikimedia Foundation project: dumps.

Where do I get it? English-language Wikipedia Some of them are mobile applications – see " list of Wikipedia mobile applications".

Wikipedia on rockbox: § Wikiviewer for Rockbox.

Selected Wikipedia articles as a printed document: Help:Printing.

BzReader: § BzReader and MzReader (for Windows).

Some of the many ways to read Wikipedia while offline:

13.4 BzReader and MzReader (for Windows).

13 Dynamic HTML generation from a local XML database dump.

12 Static HTML tree dumps for mirroring or CD distribution.

9.1 Doing Hadoop MapReduce on the Wikipedia current database dump.

9 Help to parse dumps for use in scripts.

7.2 Doing SQL queries on the current database dump.

7 Why not just retrieve data from at runtime?.

4 Where are the uploaded files (image, audio, video, etc.)?.

The user interface also provides options for opening a new tab or window. For browsing offline sources such as Wikipedia, WebArchive provides a search feature, bookmarks, history, zoom controls, as well as a basic night mode. The application features a clean interface that respects the GNOME HIG, with a simple list for recent, local and remote sources. This way you get to decide if you need pictures and videos and download the appropriate archive. However, WebArchives provides download links for multiple versions, which include or exclude media like pictures or videos. Some of these sources require a lot of hard disk space to download. The application contains links to these sources and offers to start the download in either a web browser or using a BitTorrent client.įrom the WebArchive interface you can select the language for the sources to download, but as expected, you'll find less sources for languages other than English. WebArchives doesn't directly download Wikipedia and other sources (it doesn't have a built-in download manager).

Kiwix downloadable contest zim files software#

The software supports reading ZIM files, an open file format that stores wiki content for offline usage, and it offers download links for a large number of sources, including Wikipedia, Stack Exchange sites (including Code Review, Super User, AskUbuntu, Bitcoin, etc.), ArchWiki, RationalWiki, TED talks, Vikidia, WikiMed Medical Encyclopedia, Wikinews, Wikisource, and many others. After downloading a source, no Internet connection is needed to read, search and browse Wikipedia.

Kiwix downloadable contest zim files install#

No problem, install WebArchives and download the Wikipedia source on your laptop before you go. Or maybe you want to do some research somewhere up in the mountains where there's no Internet. The application is useful for those without a permanent Internet connection or those using metered connections - the offline sources can be downloaded at a friend's house, copied on a USB stick, and imported into WebArchives.

Kiwix downloadable contest zim files archive#

WebArchives is a web archive reader for Linux desktops which provides the ability to browse articles offline from websites such as Wikipedia or Wikisource, in multiple languages.

[hoclaptrinhtuso0]

Lap trinh java la gi? Co nen hoc lap trinh java khong?

Lập trình java là gì? Ứng dụng của lập trình Java trong cuộc sống là gì? Có nên học Java không? Học Java có khó không?… Đây là những câu hỏi thường gặp của các bạn đang tìm hiểu về lập trình. Trong bài viết này, Rikkei Academy sẽ giúp bạn tìm được lời giải đáp cho các vấn đề này nhé!

1. Java là gì?

Java được khởi đầu bởi James Gosling tại Sun Microsystems. Sau đó Oracle đã mua lại để tiếp tục phát triển và Java được phát hành vào năm 1995. Java được xem như là một thành phần cốt lõi trong nền tảng Java của Sun Microsystems.

Ngôn ngữ Java hoạt động độc lập với nền tảng. Java không có bất kỳ giới hạn đối với phần cứng hay hệ điều hành cụ thể nào. Được tạo ra với tiêu chí “Viết (code) một lần, thực thi khắp nơi” – Write Once, Run Anywhere (WORA). Các chương trình được viết bằng Java có thể chạy được trên mọi nền tảng khác nhau. Thông qua một môi trường thực thi. Với điều kiện cần có môi trường thực thi thích hợp hỗ trợ cho nền tảng đó.

2. Ứng dụng của lập trình Java

Thành quả của quá trình lập trình bằng Java được sử dụng rất nhiều trong cuộc sống. Từ các ứng dụng tài chính đến các ứng dụng khoa học. Hay từ các trang web thương mại điện tử đến các ứng dụng dành do hệ điều hành Android. Hoặc các trò chơi và các ứng dụng cho máy tính,… Chúng ta hãy cùng điểm qua một số ví dụ sau đây nhé!

Phát triển ứng dụng mobile (điển hình là Android)

Bạn chỉ cần mở điện thoại có sử dụng hệ điều hành Android và vào bất kỳ ứng dụng nào có trên thiết bị. Chúng đều có thể được viết bằng Java. Với việc sử dụng API Android của Google tương tự như JDK. Ứng dụng Android sử dụng máy ảo JVM và các gói khác. Tuy nhiên, phần code vẫn được viết bằng Java.

Xây dựng các ứng dụng trên máy tính ( như hệ điều hành Windows, Ubuntu,…)

Java được sử dụng chủ yếu để viết các ứng dụng ở phía máy chủ. Java có đóng góp rất lớn đối với các dịch vụ tài chính, thương mại điện tử. Nhiều ngân hàng đầu tư toàn cầu. Như: Barclays, Citigroup, Goldman Sachs,… sử dụng Java để viết các hệ thống giao dịch điện tử. Hệ thống xác nhận và kiểm toán. Hoặc được dùng để xử lý các dữ liệu và một số công việc quan trọng khác.

Xây dựng website bằng lập trình Java

Có rất nhiều trang web được tạo nên bởi Java. Đồng thời, Java cũng được sử dụng khá phổ biến trong các dự án của chính phủ. Nhiều tổ chức quốc phòng, chính phủ, giáo dục, y tế, bảo hiểm. Và một số tổ chức hành chính khác đều có các ứng dụng web được xây dựng bằng Java.

Tạo ra trò chơi (game)

Rất nhiều game được tạo ra từ Java như: Minecraft,…

Không gian nhúng

Java cũng được sử dụng rất nhiều trong lĩnh vực lập trình nhúng. Trên thực tế, đây là một phần của “viết một lần, chạy ở bất kỳ đâu” mà ban đầu Java xây dựng.

Công nghệ dữ liệu lớn

Mặc dù Java là ngôn ngữ lập trình không chiếm được ưu thế trong lĩnh vực này. Bởi có những công nghệ như MongoDB được viết bằng C ++ khá nổi tiếng. Tuy nhiên Java vẫn có tiềm năng chiếm được thị phần lớn trong lĩnh vực này nếu Hadoop hoặc ElasticSearch trở nên lớn hơn. Hadoop và các công nghệ dữ liệu lớn khác cũng đang sử dụng Java theo cách này hay cách khác. Ví dụ: HBase, Accumulo (mã nguồn mở) và ElasticSearch của Apache đều dựa trên Java.

Ứng dụng khoa học

Ngày nay, Java vẫn được xem là lựa chọn mặc định của các ứng dụng khoa học. Lý do chính là vì Java bảo mật, di động, dễ bảo trì. Và đi kèm với các công cụ tốt hơn so với ngôn ngữ lập trình C ++. Hoặc bất kỳ ngôn ngữ nào khác.

3. Có nên học lập trình Java không?

Với những ưu điểm và lợi ích cũng như ứng dụng rộng rãi của Java như hiện nay. Nhiều bạn yêu thích công nghệ và lập trình đều băn khoăn không biết có nên học Java không? Học Java để làm gì? Và lợi ích của việc học Java là gì?

Java không chỉ độc lập nền tảng mà Java còn là ngôn ngữ lập trình hướng đối tượng (OOP). Sử dụng các đối tượng được định nghĩa rõ ràng. Kết hợp cùng các mối quan hệ giữa các đối tượng với nhau để thực hiện các tác vụ khác nhau.

Theo Wikipedia, tính đến ngày 21 tháng 1 năm 2021. Java là ngôn ngữ lập trình phổ biến thứ hai trên thế giới. Với tỉ lệ 11,96% chỉ xếp sau ngôn ngữ C. Trong nhiều năm qua Java và C là vẫn luôn 2 giữ 2 vị trí đầu tiên trong bảng xếp hạng các ngôn ngữ lập trình phổ biến nhất suốt 20 năm.

Java vẫn luôn giữ được tỉ lệ trên 10% mặc cho sự phát triển chóng mặt của thế giới công nghệ. Java thể hiện đẳng cấp một ngôn ngữ lập trình chất lượng của nhân loại

Với những ưu điểm nổi bật và độ phổ biến của Java. Lập trình sử dụng ngôn ngữ Java trở thành kỹ năng được mong muốn nhất của các nhà tuyển dụng ngành công nghệ. Ngoài ra. mức lương trung bình của lập trình viên Java cũng ở mức khó cao. Mức lương trung bình của một lập trình viên Java ở Mỹ rơi vào khoảng $88 nghìn đô-la/năm (tương đương hơn 1,8 tỷ VNĐ/năm).

4. Làm sao để có thể học ngôn ngữ lập trình Java?

Cũng như việc học bất kỳ các ngôn ngữ lập trình nào khác. Để học được ngôn ngữ lập trình Java, bạn cần đáp ứng được yêu cầu về kiến thức và theo sát một lộ trình học đòi hỏi sự đầu tư nghiêm túc. Java là một ngôn ngữ lập trình không quá khó khi bạn có dành thời gian và công sức đầu tư cho nó. Nhưng chắc chắn Java cũng không phải là một ngôn ngữ lập trình dễ học. Java là ngôn ngữ lập trình bậc cao. Để học Java cơ bản, trước tiên bạn cần có kiến ​​thức cơ bản về ngôn ngữ java là gì. Bất cứ khi nào học một ngôn ngữ lập trình, bạn cũng phải biết những điều sau:

Toán tử, giải thuật

Khai báo biến.

Các kiểu dữ liệu.

Cấu trúc rẽ nhánh

Cấu trúc vòng lặp

…

Một trong những điều bạn cần chú ý khi học lập trình đó là việc bạn phải tự học rất nhiều. Sau mỗi bài học các bạn nên hệ thống lại những gì đã học và áp dụng kiến thức vào bài tập thực tế. Việc này sẽ giúp các bạn ghi nhớ kiến thức lâu hơn. Đồng thời, việc làm nhiều bài tập sẽ tạo cho bạn thói quen và đáp ứng tốt các bài toán khó lập trình.

Trên đây là những chia sẻ của Rikkei Academy về lập trình Java. Hy vọng bài viết sẽ giúp bạn giải quyết được những vấn đề khúc mắc. Nếu bạn còn bất kỳ câu hỏi hay đóng góp nào, hãy để lại bình luận bên dưới nhé!

Xem thêm: https://rikkei.edu.vn/lap-trinh-java-la-gi-co-nen-hoc-lap-trinh-java-khong/

[wewilllikeit]

Choosing New Tools and Technology for Your Web Projects

Are you planning to create a website or mobile app for your business but don’t know how to start? Choosing a technology is simultaneously one of the most exciting and dreaded tasks when building a software product.

Creating a product is about stability, security, and maintainability. In order to select the right technology, you should answer the following questions:

Who will use my product and in what context?

Who will buy my product and what will they pay?

What third party systems will my product need to interoperate with?

Selecting an appropriate technology for your software is critical. You must understand the implications of that technology landscape to make relevant decisions.

So, with no further ado, let’s get straight to the point. How do you figure out which technology is the most suitable for you? To make your choice a little easier, here some factors to consider when choosing your tools.

Requirements of your project

Technologies are heavily dependent on each other. The type of app you’re developing influences the technology you should select.

It’s common practice to rely on your developers for technology suggestions. However, it’s important to take into account all the important features that will be implemented.

Project size

The complexity of your project will affect the choice of technology.

Small projects include single page sites, portfolios, presentations, and other small web solutions

Medium-sized projects, such as online stores, financial, and enterprise apps require more complex tools and technologies.

Large projects, such as marketplaces require much more scalability, speed, and serviceability.

Time to market

It's all about being the first to hit the market. Use technologies that can help you to get your web solution to the market in the shortest time.

Security of the tools

Security is crucial for web applications. Ensure that you're using technologies with no known vulnerabilities.

Maintenance

When selecting technologies for your web app, think about how you’ll support the app in the long run. Your team must be able to maintain the application after it is released.

Cost

Cost is important as a constraint, not as an objective. There is a delicate balance between price and value. Thriving in today’s competitive environment requires understanding the trends in software development. There are open-source IT frameworks and tools that are free. However, some tech stacks come with subscription fees and demand a high salary for developers.

Scalability

When you're choosing the technology, ensure that the components are scalable.

Top 6 technology stacks

Just like building a house, there are different “building materials” and tools, to build a solid ground for your software you need a finely selected technology stack.

What is a technology stack? It is a set of tools, programming languages, and technologies that work together to build digital products. A technology stack consists of two equally important elements:

Frontend (client-side) is the way we see web pages.

Backend (server-side) is responsible for how web and mobile applications function and how the internal processes are interconnected.

It’s absolutely essential to choose the right technology stack that will let you build solid ground for your software. For example, Facebook’s tech stack includes PHP, React, GraphQL, Cassandra, Hadoop, Swift, and other frameworks.

So, what are the leading stacks of software development in 2021?

1. The MEAN stack

MEAN stack is a combination of four major modules, namely:

MongoDB

Express.js

AngularJS

Node.js

Being an end-to-end JavaScript stack, you use a single language throughout your stack. You can reuse code across the entire application and avoid multiple issues that usually occur with the client and server-side matching.

Companies using MEAN stack: YouTube, Flickr, Paytm, Tumblr

2. The MERN stack

MERN is nearly identical to MEAN with a bit of technological change – the difference is that React is used instead of Angular.js. React is known for its flexibility in developing interactive user interfaces.

Companies using MERN stack: Facebook, Instagram, Forbes, Tumblr

3. The MEVN Stack

MEVN is another variation of MEAN. It uses Vue.js as a front-end framework instead of Angular.js.

Vue.js combines the best features of Angular and React and offers a rich set of tools. It is fast and easy to learn.

Companies using MEVN stack: Alibaba, Grammarly, Behance, TrustPilot

4. The LAMP stack

LAMP is industry standard when it comes to tech stack models. It is a time-tested stack of technologies that includes:

Linux

Apache

MySQL

PHP

Apps developed using the LAMP stack run smoothly on multiple operating systems. LAMP is the preferred choice for large-scale web applications.

Companies using LAMP stack: Facebook, Google, Wikipedia, and Amazon

5. The Serverless Stack

Developing applications on cloud infrastructure is a popular web development trend. Up to hundreds of thousands of users can quickly be scaled overnight with serverless computing systems. AWS Lambda and Google cloud are among significant providers of serverless services.

Companies using Serverless stack: Coca-Cola, Netflix

6. Flutter

Flutter is a revolutionary stack for cross-platform development. Flutter employs the same UI and business logic on all platforms.

Companies using Flutter stack: Delivery Hero, Nubank

What is left is finding the right assistant on this journey

The choice of the right tool depends on what requirements you face. When selecting a technology, consider the short and long-term goals of your project. For instance, web applications need different sets of tools from mobile apps. Even within mobile applications, you need different technologies for Android and iOS development.

While the prospect of a new project can create a buzz around your team like nothing else can, it can also cause a lot of anxiety. There is no one-size-fits-all solution in web development. Finally, when in doubt, it’s always better to consult a web design company near you for a second opionion.

We feel you. The technological building blocks of your software product are of course fundamentally important. Technology selection could be overwhelming, but you have to keep up to keep ahead.

[mmorellm]

Open Source Definitely Changed Storage Industry With Linux and other technologies and products, it impacts all areas. By Philippe Nicolas | February 16, 2021 at 2:23 pm It’s not a breaking news but the impact of open source in the storage industry was and is just huge and won’t be reduced just the opposite. For a simple reason, the developers community is the largest one and adoption is so wide. Some people see this as a threat and others consider the model as a democratic effort believing in another approach. Let’s dig a bit. First outside of storage, here is the list some open source software (OSS) projects that we use every day directly or indirectly: Linux and FreeBSD of course, Kubernetes, OpenStack, Git, KVM, Python, PHP, HTTP server, Hadoop, Spark, Lucene, Elasticsearch (dual license), MySQL, PostgreSQL, SQLite, Cassandra, Redis, MongoDB (under SSPL), TensorFlow, Zookeeper or some famous tools and products like Thunderbird, OpenOffice, LibreOffice or SugarCRM. The list is of course super long, very diverse and ubiquitous in our world. Some of these projects initiated some wave of companies creation as they anticipate market creation and potentially domination. Among them, there are Cloudera and Hortonworks, both came public, promoting Hadoop and they merged in 2019. MariaDB as a fork of MySQL and MySQL of course later acquired by Oracle. DataStax for Cassandra but it turns out that this is not always a safe destiny … Coldago Research estimated that the entire open source industry will represent $27+ billion in 2021 and will pass the barrier of $35 billion in 2024. Historically one of the roots came from the Unix – Linux transition. In fact, Unix was largely used and adopted but represented a certain price and the source code cost was significant, even prohibitive. Projects like Minix and Linux developed and studied at universities and research centers generated tons of users and adopters with many of them being contributors. Is it similar to a religion, probably not but for sure a philosophy. Red Hat, founded in 1993, has demonstrated that open source business could be big and ready for a long run, the company did its IPO in 1999 and had an annual run rate around $3 billion. The firm was acquired by IBM in 2019 for $34 billion, amazing right. Canonical, SUSE, Debian and a few others also show interesting development paths as companies or as communities. Before that shift, software developments were essentially applications as system software meant cost and high costs. Also a startup didn’t buy software with the VC money they raised as it could be seen as suicide outside of their mission. All these contribute to the open source wave in all directions. On the storage side, Linux invited students, research centers, communities and start-ups to develop system software and especially block storage approach and file system and others like object storage software. Thus we all know many storage software start-ups who leveraged Linux to offer such new storage models. We didn’t see lots of block storage as a whole but more open source operating system with block (SCSI based) storage included. This is bit different for file and object storage with plenty of offerings. On the file storage side, the list is significant with disk file systems and distributed ones, the latter having multiple sub-segments as well. Below is a pretty long list of OSS in the storage world. Block Storage Linux-LIO, Linux SCST & TGT, Open-iSCSI, Ceph RBD, OpenZFS, NexentaStor (Community Ed.), Openfiler, Chelsio iSCSI, Open vStorage, CoprHD, OpenStack Cinder File Storage Disk File Systems: XFS, OpenZFS, Reiser4 (ReiserFS), ext2/3/4 Distributed File Systems (including cluster, NAS and parallel to simplify the list): Lustre, BeeGFS, CephFS, LizardFS, MooseFS, RozoFS, XtreemFS, CohortFS, OrangeFS (PVFS2), Ganesha, Samba, Openfiler, HDFS, Quantcast, Sheepdog, GlusterFS, JuiceFS, ScoutFS, Red Hat GFS2, GekkoFS, OpenStack Manila Object Storage Ceph RADOS, MinIO, Seagate CORTX, OpenStack Swift, Intel DAOS Other data management and storage related projects TAR, rsync, OwnCloud, FileZilla, iRODS, Amanda, Bacula, Duplicati, KubeDR, Velero, Pydio, Grau Data OpenArchive The impact of open source is obvious both on commercial software but also on other emergent or small OSS footprint. By impact we mean disrupting established market positions with radical new approach. It is illustrated as well by commercial software embedding open source pieces or famous largely adopted open source product that prevent some initiatives to take off. Among all these scenario, we can list XFS, OpenZFS, Ceph and MinIO that shake commercial models and were even chosen by vendors that don’t need to develop themselves or sign any OEM deal with potential partners. Again as we said in the past many times, the Build, Buy or Partner model is also a reality in that world. To extend these examples, Ceph is recommended to be deployed with XFS disk file system for OSDs like OpenStack Swift. As these last few examples show, obviously open source projets leverage other open source ones, commercial software similarly but we never saw an open source project leveraging a commercial one. This is a bit antinomic. This acts as a trigger to start a development of an open source project offering same functions. OpenZFS is also used by Delphix, Oracle and in TrueNAS. MinIO is chosen by iXsystems embedded in TrueNAS, Datera, Humio, Robin.IO, McKesson, MapR (now HPE), Nutanix, Pavilion Data, Portworx (now Pure Storage), Qumulo, Splunk, Cisco, VMware or Ugloo to name a few. SoftIron leverages Ceph and build optimized tailored systems around it. The list is long … and we all have several examples in mind. Open source players promote their solutions essentially around a community and enterprise editions, the difference being the support fee, the patches policies, features differences and of course final subscription fees. As we know, innovations come often from small agile players with a real difficulties to approach large customers and with doubt about their longevity. Choosing the OSS path is a way to be embedded and selected by larger providers or users directly, it implies some key questions around business models. Another dimension of the impact on commercial software is related to the behaviors from universities or research centers. They prefer to increase budget to hardware and reduce software one by using open source. These entities have many skilled people, potentially time, to develop and extend open source project and contribute back to communities. They see, in that way to work, a positive and virtuous cycle, everyone feeding others. Thus they reach new levels of performance gaining capacity, computing power … finally a decision understandable under budget constraints and pressure. Ceph was started during Sage Weil thesis at UCSC sponsored by the Advanced Simulation and Computing Program (ASC), including Sandia National Laboratories (SNL), Lawrence Livermore National Laboratory (LLNL) and Los Alamos National Laboratory (LANL). There is a lot of this, famous example is Lustre but also MarFS from LANL, GekkoFS from University of Mainz, Germany, associated with the Barcelona Supercomputing Center or BeeGFS, formerly FhGFS, developed by the Fraunhofer Center for High Performance Computing in Germany as well. Lustre was initiated by Peter Braam in 1999 at Carnegie Mellon University. Projects popped up everywhere. Collaboration software as an extension to storage see similar behaviors. OwnCloud, an open source file sharing and collaboration software, is used and chosen by many universities and large education sites. At the same time, choosing open source components or products as a wish of independence doesn’t provide any kind of life guarantee. Rremember examples such HDFS, GlusterFS, OpenIO, NexentaStor or Redcurrant. Some of them got acquired or disappeared and create issue for users but for sure opportunities for other players watching that space carefully. Some initiatives exist to secure software if some doubt about future appear on the table. The SDS wave, a bit like the LMAP (Linux, MySQL, Apache web server and PHP) had a serious impact of commercial software as well as several open source players or solutions jumped into that generating a significant pricing erosion. This initiative, good for users, continues to reduce also differentiators among players and it became tougher to notice differences. In addition, Internet giants played a major role in open source development. They have talent, large teams, time and money and can spend time developing software that fit perfectly their need. They also control communities acting in such way as they put seeds in many directions. The other reason is the difficulty to find commercial software that can scale to their need. In other words, a commercial software can scale to the large corporation needs but reaches some limits for a large internet player. Historically these organizations really redefined scalability objectives with new designs and approaches not found or possible with commercial software. We all have example in mind and in storage Google File System is a classic one or Haystack at Facebook. Also large vendors with internal projects that suddenly appear and donated as open source to boost community effort and try to trigger some market traction and partnerships, this is the case of Intel DAOS. Open source is immediately associated with various licenses models and this is the complex aspect about source code as it continues to create difficulties for some people and entities that impact projects future. One about ZFS or even Java were well covered in the press at that time. We invite readers to check their preferred page for that or at least visit the Wikipedia one or this one with the full table on the appendix page. Immediately associated with licenses are the communities, organizations or foundations and we can mention some of them here as the list is pretty long: Apache Software Foundation, Cloud Native Computing Foundation, Eclipse Foundation, Free Software Foundation, FreeBSD Foundation, Mozilla Foundation or Linux Foundation … and again Wikipedia represents a good source to start.

Open Source Definitely Changed Storage Industry - StorageNewsletter

[outsourcebigdata]

Best Data Management Service Provider Online

Data management plays a major role in the success of an organisation. According to Wikipedia “Data management comprises all the disciplines related to managing data as a valuable resource”. As the name defines that it is the management of data. These days data is playing a crucial role in business. Especially in ecommerce or retail sector companies use data insight for each and every department for the better improvement in their services as well as improvement in company. They use data management to generate revenue, cost optimization and risk analysis. Hadoop helps the ecommerce business in many ways. Companies are using Hadoop for their data management and leveraging data to find better insight which they are applying in decision making. Nowadays Hadoop has become an integrated part of a successful ecommerce business. In other words ‘Hadoop is playing an important role in ecommerce data management’. We offer comprehensive data processing in the form of data entry, data mining, data processing and data conversion across a number of different industries and business types.  For more details visit https://outsourcebigdata.com/

[siva3155]

300+ TOP CloverETL Interview Questions and Answers

CloverETL Interview Questions for freshers experienced :-

1. What is CloverETL? CloverETL is a Java-based data integration ETL platform for rapid development and automation of data transformations, data cleansing, data migration and distribution of data into applications, databases, cloud and data warehouses. The product family starts with an open source runtime engine and limited Community edition of visual data transformation Designer. 2. What is ETL? ETL stands for Extract-Transform-Load – a data processing operation that performs data manipulations, usually on-the-fly, while getting (extracting) data from a source or sources, transforming it, and storing into target(s. For more information, see the Wikipedia page for ETL. 3. What is data integration? Data integration is a broad term used for any effort of combining data from multiple sources into a more unified and holistic view. It usually involves several operations, such as ETL, orchestration, automation, monitoring and change management. 4. What's the difference between ETL and data integration? ETL is a form of data integration where data is transformed during transport between sources and targets. While "pure" ETL is focused on the actual transport, data integration usually refers to a broader task of managing ETL tasks, scheduling, monitoring, etc. 5. Why use an ETL tool and why CloverETL in particular? ETL or data integration tools replace ad hoc scripts that you would use to transport data between databases, files, web services etc. Over time, these become very difficult to manage and are prone to errors. ETL tools provide you with visual tools to manage, monitor, and update data transformations with ease. CloverETL in particular is a rapid data integration tool oriented to get your job done quickly. 6. What is CloverETL Designer? CloverETL Designer is a visual tool for developing, debugging, and running data transformations. 7. What is CloverETL Server? CloverETL Server is an automation, orchestration and monitoring enterprise platform for data integration. 8. What is CloverETL Cluster? CloverETL Cluster allows multiple instances of the CloverETL Server to run on different HW nodes and form a computer cluster. It allows for high availability through fail-over capabilities, scaling via load balancing, and processing of Big Data through a massively-parallel approach. 9. Which platforms or operating systems does CloverETL run on? CloverETL runs on any platform/operating system where Java 1.6 or later is supported. This includes Windows, OSX, Linux, various UNIX systems and others. 10. Is there a free option for CloverETL? Yes. There is a 45-day fully featured trial for CloverETL Designer and a trial CloverETL Server (contact sales@cloveretl.com. There is also a completely free, but feature-limited CloverETL Community Edition.

CloverETL Interview Questions 11. When and how are new versions released? There are two major production releases every year. Before each production release, there are two milestone releases that allow early access to new features from the upcoming production version. Production releases are sometimes replaced with bugfix releases that come as needed. 12. What are milestone (M1, M2) releases? Milestone releases provide early public access to features that we're working on for the upcoming production release. You can use milestones and their new features to develop, test, and provide us with feedback. However, milestone releases are not covered by CloverCARE support, so we do not recommend putting them into a mission critical deployment. Major changes that can affect existing transformations are usually published in early milestone versions so that you have plenty of time to adapt to possible incompatibilities. 13. Do I need to renew CloverCARE? Your CloverCARE support is covered by an 20% annual maintenance fee that grants you access to product updates and standard CloverCARE support. To continue receiving upgrades and support, you need to renew your maintenance every year. 14. Are there any discounts (academic, non profit, volume) available? We can offer discounts for various types of organizations and businesses. We can also offer volume deals. Please contact our Sales at sales@cloveretl.com or via this Contact Us form. 15. What makes CloverETL stand out against SSIS/Talend/Pentaho? CloverETL is a rapid data integration tool. Our main goal is to provide our users with a tool that helps them achieve results quickly, without having to spend time on training, learning, etc. Starting from our examples, you can begin building data transformations quickly. CloverETL is also sharply focused on data integration – it’s a light-footed, dedicated tool. 16. What is CloverCARE and what does it include? CloverCARE is our support package included in every commercial deal. Members of our support team are professional experts who are using CloverETL themselves – no outsourcing, no frustrating phone calls. We also support evaluating users during their trial period. CloverCARE offers email, phone, and WebEx support at various SLAs. Please refer to our CloverCARE Support page for more details. 17. Which versions of application servers does CloverETL Server support? Currently CloverETL supports Apache Tomcat 6.0.x, Glassfish 2.1, JBoss 5.1 or JBoss 6.0, Jetty 6.1.x, WebLogic 11g (10.3.6), WebLogic 12c (12.1.1), Websphere 7.0 18. Can CloverETL be embedded in my product? The short answer is yes. CloverETL technology can be embedded in various ways. You can embed CloverETL Designer, CloverETL Server or even just the data processing engine running under the hood. Some of our customers also use white-labeled CloverETL technology in their product offerings. For additional details, please read our OEM section. 19. How scalable is CloverETL? CloverETL technology scales really well. You can start with the CloverETL Designer running on your laptop processing thousands of records then move onto the CloverETL Server with its automation capabilities to crunch millions of records. If you happen to hit any Big Data problems, then the CloverETL Cluster is able to cope with any data volume through its massively-parallel data processing capabilities. 20. Does CloverETL support Big Data? CloverETL technology naturally fits the processing of Big Data. Its inherent pipeline-parallelism and massively-parallel processing facilitated by CloverETL Cluster allows you to cope with Big Data problems. It’s also able to cooperate with other Big Data related technologies like Hadoop, Hive, and others. 21. What kind of data can I process in CloverETL? CloverETL can process any structured or semi-structured data whether stored in a database, file, or other system. Data sources and data targets alike can be a combination of various independent databases and files. 22. How do I get my newly purchased licenses? You'll receive an email with your account information (email and password) that you can use to Sign In here. From there, navigate to Licenses & Downloads where you can get both license keys and download all the software. 23. How do I transition from Designer to Server? There is a direct upgrade path from the desktop Designer to the Server environment. Your already existing work can be transferred to the Server without any additional effort. Designer manages projects in workspaces on your local drive. You can simply export these to Server sandboxes (via File > Export > CloverETL > Export to CloverETL Server sandbox) and continue working remotely on the Server. 24. How do I transition from Server to Cluster? CloverETL Cluster is basically a bunch of Server instances connected together into a single cluster. When you move into Cluster, we recommend reading about various types of sandboxes and how to process data in parallel 25. I purchased CloverETL, but my license is set to expire in two months. Why? If you feel there's been an error, please contact our Sales at sales@cloveretl.com or via this Contact Us form. Usually we issue temporary licenses immediately once a Purchase Order is received. We then replace the temporary licenses with unlimited ones once the payment is processed. 26. My evaluation license expired. Is it possible to extend the evaluation period? Yes, you can ask for trial extension here. 27. Do you have any plans for selling the company or being taken over? Our mission is to be a leader in data integration and stay true to providing high quality product and services. You can read more in this CloverETL Manifesto. 28. What files are supported? You can process virtually any file containing data, including delimited files, fixed-length record files, binary files or mix of these. Popular file formats are also supported: Excel XLS/XLSX, XML, JSON, dBase DBF, emails, Lotus Notes Domino. 29. What databases are supported? CloverETL supports standard relational databases via JDBC. Others include Oracle, Informix, Microsoft SQL Server, Access, MySQL, Postgres, Sybase, etc. Also, some modern NoSQL or columnar databases are supported too, e.g. MongoDB, Exasol, HP Vertica, HDFS or S3. 30. Can I read and write remote files (FTP, SFTP, HTTP/S, etc.)? Yes. Please refer to Supported File URL Formats for Readers and Supported File URL Formats for Writers. 31. Can I read and write data using Web Services or REST APIs? Yes. There are dedicated components for that: WebServiceClient and HTTPConnector. Also, many components support remote data - please refer to Supported File URL Formats for Readers and Supported File URL Formats for Writers. 32. Do you support Apache Hadoop and/or Hive? Yes, Hadoop is supported for both HDFS storage, as well as running MapReduce jobs. Hive is also supported. Please refer to Hadoop connections, Hive connection. 33. Can I use data from cloud providers such as Amazon S3? Yes, you can access data on Amazon S3. For more please read Supported File URL Formats for Readers and Supported File URL Formats for Writers. 34. How do I use Designer to develop on the Server? Do I need to deploy? The Designer connects directly to Server sandboxes so you're working live on the Server. There is no need to deploy your local edits or anything. Whenever you're connected to a Server sandbox and run a transformation or jobflow, it is executed on the Server, not locally. 35. Can I run a transformation without Designer? How? Yes, CloverETL Server provides numerous automation functions, including scheduled execution, web services, event listeners, etc. 36. Can CloverETL Server be deployed to Amazon EC2? Yes, there are several projects running CloverETL hosted on Amazon EC2 servers. As data transformations are heavy on I/O, make sure you pick a "high I/O" instances. The installation does not require any additional tricks. 37. Can CloverETL handle secure data transfers (HTTPS, SFTP, FTPS, etc.)? Yes, you can access all of these protocols. For more please read Supported File URL Formats for Readers and Supported File URL Formats for Writers. 38. Can sensitive information, such as passwords in connections, be securely hidden? Yes, CloverETL Server supports encrypted secure parameters so that sensitive information are not stored in plain-text readable form in graphs, connections etc. 39. Can I use projects developed in Trial (or Community) in commercial an vice versa? Yes, everything that you create in Community or Trial can be opened and further developed in any commercial edition of CloverETL. However, CloverETL Community cannot run all transformations created using the Trial or commercial products due to its limitations. 40. Can I create my own custom component or function via a plugin? Yes, there are two nice articles you can read on our blog to help you do so: Creating your own component and Custom CTL functions. 41. How do I upgrade CloverETL Designer to the latest version? We recommend uninstalling the old version and performing a fresh install of the new one. Don’t worry, all your work is safe – it’s always stored outside the installation files. 42. Do I need an application server to run CloverETL Server? If yes, which one? We provide a default, easy-to-start bundled package of CloverETL Server with pre-configured Apache Tomcat and Derby database. It's a good, simple start. However, if you wish to use an application container of your own, CloverETL supports a number of industry standard J2EE application servers such as Apache Tomcat, GlassFish, Weblogic, WebSphere, JBoss and Jetty. CloverETL Questions and Answers Pdf Download Read the full article

[sturkki-blog1]

Digi 5.6 BIG DATA  ja avoin data

BIG DATA

Big Data or termi, jolla tarkoitetaan erittäin suuria tietomassoja. Tiedostojen koko on niin suuri, että niitä ei pystytä käsittelemään perinteisillä datahallintojärjestelmillä. Tiedosto voi myös olla poikkeavan tyyppinen,  jolloin tavalliset käsittely järjestelmät eivät osaa sitä käsitellä. Usein big data kerätään laitteilla useasta eri lähteestä samanaikaisesti, reaaliajassa ja erittäin laajasti.            

Miten big dataa sitten käsitellään?

Nykyisin laitteet mahdollistavat laajan tiedon keruun, joka aiheuttaa myös sen, että tietoa kertyy liikaakin ja osa siitä on turhaa. Niimpä ensimmäiseksi tieto tulee puhdistaa liiallisesta melusta, josta ei ole hyötyä analyysin kannalta. Sen järkeen tietoja käsitellään ETL-processin mukaisesti.

ETL tulee sanoista Extract, Transform and Load. Suomeksi: poiminta, muunos ja lataaminen. Tämä kolmiportainen prosessi mahdollistaa tiedon varastoinnin sellaisessa muodossa, että sitä voidaan edelleen analysoida ja vertailla perinteisillä datahallintojärjestelmillä.

Kuva 1: Big Data ZDnet

Avoin Data

Avoin Data sen sijaan on termi, jolla tarkoitetaan vapaasti ja maksutta jaettavaa tietoa. Usein julkinen sektori tai kansalaisjärjestot (NGOs) tuottavat tietoa, joka on vapaasti tarjolla kaikkien käytettäväksi. Usein tälläisillä tiedoilla on yleishyödyllinen tarkoitus, ja tekijöitä kiinnostaa sen että tieto saavuttaisi mahdollisimman laajan yleisön.

Kuva 2 Avoin Data: Muuttuva media Blogit Haaga-Helia

”Helsinki Region infoshare” ovat sivustot, jossa jaetaan pääkaupunkiseudun avointa dataa. Siellä on myös vino pino erilaisia sovelluksia, joita kuka tahansa voi kehittää hyödyntämään avointa dataa. Usat sovellukset liittyvät joko (joukkoliikenteen) liikennejärjestelyihin tai alueen statistiikkaan.

Palveluiden ja tuotteiden valmistajille voisi olla hyödyllistä seurata sovellusta nimeltäi ”Kuntien ostot”, jossa tiedotetaan Helsingin, Espoo ja Kauniaisten julkisista ostoista vuosittain, Tai ”Hilmappi” -sovellus, joka helpottaa julkisenhallinnon tarjouspyyntöpalvelu HILMAan jätettyjen tarjousten seuraamista.

Asiakasyrityksemme TinyAppin kannalta mielenkiintoista tietoa voisi löytyä sovelluksesta jossa tiedoitetaan pääkaupunkiseudun lasten ja perheiden tapahtumista. Niihin osallistuminen, voisi olla mukavaan vaihtelua päiväkodin rutiineihin. Toinen hyödyllinen sovellus voisi olla pääkaupunki seudun vihreät alueet. Joillain päiväkodeilla omaa pihaa on rajoitetusti ja siksi varsinkin vanhemmat lapset nauttivat vierailuista muihin puistoihin.

Lisäksi avoimesta datasta löytyy statistiikkaa koskien eri alueiden demograafista kehitystä ja kaupunkien tulevaisuuden suunnittelua, mistä voisi olla hyötyä TinyAppille.

Lähteet:

IBM: https://www.ibm.com/analytics/hadoop/big-data-analytics

WIKIPEDIA: https://fi.wikipedia.org/wiki/Big_data

tech target: http://searchdatamanagement.techtarget.com/definition/extract-transform-load

[rpatools]

RPA or Robotic Process Automation involves the computerization of tasks that are typically mundane rules-based business processes carried out manually. These tasks are often data-heavy and include, but aren’t limited to, data entry, transactions, and compliance. Wikipedia defines it as, “an emerging form of business process automation technology based on the notion of software robots or artificial intelligence (AI) workers.”

Research conducted at Hadoop reveals that the potential savings that companies can hope to experience with the adoption of RPA by 2025 can be anywhere between $5 trillion and $7 trillion. They also forecasted that RPA software will be able to perform tasks equal to the output of 140 million Full-Time Employees (FTEs) by the same year.

Further, as per Statista, the RPA industry is estimated to be worth $3.1 billion by 2019 and $4.9 billion by 2020. According to Forrester, this figure is likely to hover around $2.9 billion by 2021.

Of course, employees are the backbone of any startup. And employee performance is critical to the flawless functioning of business processes. While this aspect remains crucial, the role of technology is undeniable. And that’s why businesses are adopting Artificial Intelligence to streamline operations and reduce costs.

B2B businesses can certainly harness this technology to serve their clients better.

As B2B firms grow, they will want to:

Automate their day-to-day processes

Remove repetition

Enable quick data entry and calculations

Maintain records and transactions

…and last but not least, replace human effort with software solutions to reduce the scope of errors.

Analysts forecast that in a few years, 40% of large global organizations will use RPA to automate work activities.

Players in highly-regulated industries such as finance, insurance, banking, healthcare, and manufacturing can particularly benefit from RPA. It also provides a cheap and quick way of dealing the problem of and adhering to the necessary industry compliance to maintain standards.

Every business wants a bright future, and RPA is the way to go ahead if you’re serious about achieving this. Several companies have realized this and are offering suitable solutions to startups.

Want to know which ones are changing the way startups function? Read on.

1. UiPath

Headquarters: New York

Established in: 2012

UiPath already enjoys a global presence and has plans to keep growing. One of the biggest names in RPA, it says, “Versatile and scalable, the UiPath robot automates >99%, while most robots automate 70%, and some, around 80%.”

UiPath caters to several industries including finance, insurance, healthcare, and manufacturing. It leverages third-parties like SAP, Oracle, Citrix, and Mainframe automation to give their robot intelligent “eyes” to “see” how objects relate, just like us humans. It lets them find screen elements contextually, and instantly adjust to screen changes.

UiPath also says its robots are about four times faster than other robots due to their ability to process screen changes in less than 100 milliseconds. It is on its way to becoming a leader in enabling back-office automation.

The company has been named the RPA provider with the strongest current offering in The Forrester Wave

2018 Report for Robotic Process Automation.

2. Blue Prism

Headquarters: United Kingdom

Established in: 2001

Blue Prism’s RPA software enables businesses to respond to changes quickly and cost-effectively by automating manual, rule-based, administrative tasks and processes. This goes a long way in enhancing the accuracy of the outcome.

Blue Prism also creates virtual workforce comprising the operational teams or certified partners that use their robotic automation technology to enable processes. IT-governed frameworks and complex arrangements are harnessed to manage automation.

Organizations such as The Co-operative Banking Group, Shop Direct, RWE npower, Fidelity Investments, the NHS, and O2 employ Blue Prism’s technology to respond promptly to business changes through agile back office and administrative operations.

3. IntelliCog Technologies

Headquarters: New Delhi

Established in: 2016

IntelliCog is an end-to-end consulting and outsourcing firm that uses RPA and AI to provide solutions. Their offerings include RPA consulting and integration capabilities with the help of their proprietary frameworks.

They’re still a budding company that’s working towards ensuring zero-downtime and business continuity by deploying their knowledge, know-how, experience, and methodologies related to RPA and AI.

4. Kryon Systems

Headquarters: New York

Established in: 2009

Kryon Systems boasts of four offerings namely process discovery, unattended automation, attended automation, and hybrid automation, all of which are related to RPA. The level of automation, obviously, varies in each offering with unattended automation requiring the least human input. The hybrid level involves a human starting a job which is carried forward to completion by robots.

In the future, Kryon Systems aspires to create its own platform to be able to identify and automate a higher number of tasks and business processes and produce zero-error mistakes for immediate gains.

5. Automation Anywhere

Headquarters: San Jose, California

Established in: 2003

Automation Anywhere is the global leader in RPA technology. It employs both, software bots and human effort to get a lot of the repetitive work done across industries. It has empowered over 1,000 organizations with the aid of high-end technologies such as RPA, cognitive and embedded analytics. This, in turn, has led to reduced operational costs and errors, and better scaling.

The company has been the leading AI-enabled solutions provider of automation requirements in industries such as finance, insurance, healthcare, manufacturing, technology, telecom, and logistics.

Recent reports about Automation Anywhere and its “historic $1.8 billion valuation” after spending 15 years in the industry have been encouraging. It has also been recognized as a leader in RPA by The Forrester Wave

: Robotic Process Automation, Q2 2018 Report.

6. Autologyx

Headquarters: United Kingdom

Established in: 2011

Formerly known as NowWeComply Limited, it changed its name to Autologyx in 2017. Autologyx provides “Automation as a Service” with a view to enabling clients to automate their business processes easily. Whether it is about performing repetitive tasks or sorting advanced processes that require expertise, Autologyx has its cloud platform for process automation ready.

The company has recently bagged a major player, global law firm Eversheds Sutherland Ignite as a client. The law firm is harnessing Autologyx’s robotic process automation platform to produce 3,000 leases that entailed going through numerable mails.  Other noteworthy clients they boast of are T-Mobile, Boeing, Luxottica, and Adecco.

7. LarcAI

Headquarters: South Africa

Established in: 2015

Another young company that’s making waves in the global RPA industry is LarcAI. It relies heavily on UiPath as its RPA platform of preference for providing its services. This is because UiPath’s open architecture makes it possible to scale up, opening up new horizons. Also, business processes and third-party technologies can be integrated; but most importantly, UiPath is affordable.

LarcAI leverages top technologies from organizations like IBM Watson, Microsoft Cognitive Services, ABBYY, and Merlyn TOM for creating the best performing solutions and to gain competitive advantage.

8. RapidRPA

Headquarters: New York

Established in: 2016

RapidRPA, also known as Echelon|RPA worked in “stealth mode” for a while since starting operations. It employs “the nexus of Lean Six Sigma, Big Data and Artificial Intelligence” to deliver “unique and powerful capabilities to automate a range of mission-critical business processes, empowering the next-generation workforce to focus on more core activities that deliver greater value.”

The company claims that it is different from other solution providers as it makes use of intuitive user experiences to improve productivity instantly. Their cloud-based solutions involve the use of robots that work with multiple vendors and offer quick, easy and low-cost delivery. It also promises 500% ROI on productivity from Day 1 of its use.

9. Daythree Business Services

Headquarters: Malaysia

Established in: Information not available

Daythree transforms repetitive service processes, day-to-day manual work, and rule-based tasks into automated digital work with the help of software robots. The robots help in redesigning business processes to keep them simple and sound. The company claims that “We deliver benefits quickly where ROIs between 300–700% are common.”

Their RPA technology betters existing company software applications instead of replacing them, thereby working in harmony with the ongoing business processes.

They also offer IT and Knowledge Process services to help you stay head and shoulders above your competition. They have been the recipients of the GBS ISKANDAR Avant-Garde Award in 2017.

10. Sanbot

Headquarters: China

Established in: 2012

QIHAN Technology’s cloud-enabled intelligent service robots enable customizable applications across industries like healthcare, education, hospitality, security, and retail. These robots are powered by IBM Watson’s AI and feature Android SDK for open customization to improve customer experience and business growth.

CIO Advisor magazine named QIHAN Technology as one of the Top 10 APAC Robotic Process Automation Companies of 2017.

Sanbot Innovation is all set with more than 200 patents in technology including Machine Vision Recognition, Multi-axis Automatic Control, Big Data Analysis, and Cloud service to create abundant artificial intelligence solutions.

11. Softomotive

Headquarters: United Kingdom

Established in: 2005

This world-class RPA technology solutions provider claims to offer the most reliable and scalable automation, thereby combining the benefits of the best technology and constant innovation for optimal business transformation. Softomotive does this by providing a potent automation platform that empowers businesses to develop, manage and track their digital performers.

Sofotmotive’s RPA solutions help reduce business costs by 90% for regulated industries. They support all compliance regulation processes such as PCI-DSS, GLBA, FISMA, Joint Commission and HIPAA.

12. Cinnamon

Headquarters: Japan

Established in: 2016

Erstwhile Spicy Cinnamon and a photo-sharing app, the company decided to turn to robotic process automation and renamed itself, Cinnamon. It was reported that the startup was successful in raising large funds from several renowned angel investors.

Cinnamon’s main offering is a smart scanner called the Flax Scanner, which can mine information from documents like emails and agreements. It has the ability to decipher the data and digitize it. It can then fill up a database or other systems automatically with significant accuracy.

13. Kofax

Headquarters: California

Established in: 1985

Kofax was established with the aim to automate and transform manual processes across front and back operations, thereby resulting in improved customer engagement, reduced operating costs, meeting compliance requirements, and accelerating business growth.

It offers an array of software and solutions related to robotic process automation, business process management, multichannel capture and other important features that can be used on the cloud and on-premise.

Kofax boasts of over 20,000 customers across industries such as finance, insurance, healthcare, supply chain, government, BPOs, among others. Its products are available in over 70 countries. Kofax has been named as a Strong Performer in Forrester Wave

: Robotic Process Automation Q2 2018 Report.

14. Pegasystems

Headquarters: Massachusetts

Established in: 1983

Pegasystems is a cloud-based unified platform that promises “321% ROI in less than 12 months. 75% cost savings. 75% productivity improvements.” It is powered by RPA and AI. They produce software bots that automate menial jobs that go on forever and call them “productivity bots.” These bots help simplify and enhance employee experiences and focus on increasing the business’s ROI.

Their solutions also help businesses deal with unforeseen industry changes, new applications, process re-engineering, and collaboration. Further, they automatically find processes to optimize and mitigate problems even before they arise.

15. WorkFusion

Headquarters: New York

Established in: 2010

WorkFusion merges together on one platform the main capabilities of business process management, robotic process automation, workforce orchestration, and AI-powered cognitive automation, workflow, intelligent conversational agents, crowdsourcing, and analytics. These help digitize complex business processes and transform them into world-class products built to simplify operations, increase productivity and improve service delivery.

WorkFusion has been named a Strong Performer in The Forrester Wave

: Robotic Process Automation Q2 2018 Report.

Conclusion

RPA is yet another outcome of advancing technology. It is mainly used to automate business processes based on logic and controlled inputs. RPA tools can help companies capture and understand applications for communicating with digital systems, triggering the desired responses, modifying data, and processing transactions. The above RPA startups are progressive in that they’re continually advancing and disrupting existing business practices.

Over To You

Are you excited about RPAs? Is this something you see yourself including in your business in the short to medium term? Are there any other RPA companies that you feel deserve a mention? (Note: if you include a link in your comment, it will be queued for moderation rather than auto-published).

You may also want to read: How Artificial Intelligence and Machine Learning Can Be Used for Marketing

4 Ways Artificial Intelligence Will Impact The B2B Industry

Featured image: https://www.pexels.com/photo/photo-of-white-and-brown-cardboard-box-toy-figure-678308/

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Bio

Latest Posts

Avinash Nair is a digital marketer at E2M, India’s premium

content marketing agency

. He specializes in SEO and Content Marketing activities.

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[freshcodeit-blog]

Introduction to message brokers. Part 1: Apache Kafka vs RabbitMQ

The growing amount of equipment, connected to the Net has led to a new term, Internet of things (or IoT). It came from the machine to machine communication and means a set of devices that are able to interact with each other. The necessity of improving system integration caused the development of message brokers, that are especially important for data analytics and business intelligence. In this article, we will look at 2 big data tools: Apache Kafka and RabbitMQ.

Why did message brokers appear?

Can you imagine the current amount of data in the world? Nowadays, about 12 billion “smart” machines are connected to the Internet. Considering about 7 billion people on the planet, we have almost one-and-a-half device per person. By 2020, their number will significantly increase to 200 billion, or even more. With technological development, building of “smart” houses and other automatic systems, our everyday life becomes more and more digitized.

Message broker use case

As a result of this digitization, software developers face the problem of successful data exchange. Imagine you have your own application. For example, it’s an online store. So, you permanently work in your technological scope, and one day you need to make the application interact with another one. In previous times, you would use simple “in points” of the machine to machine communication. But nowadays we have special message brokers. They make the process of data exchange simple and reliable. These tools use different protocols that determine the message format. The protocols show how the message should be transmitted, processed, and consumed.

Messaging in a nutshell

Wikipedia asserts that a message broker “translates a message from the formal messaging protocol of the sender to the formal messaging protocol of the receiver”.

Programs like this are essential parts of computer networks. They ensure transmitting of information from point A to point B.

When a message broker is needed?

If you want to control data feeds. For example, the number of registrations in any system.

When the task is to put data to several applications and avoid direct usage of their API.

The necessity to complete processes in a defined order like a transactional system.

So, we can say that message brokers can do 4 important things:

divide the publisher and consumer

store the messages

route messages

check and organize messages

There are self-deployed and cloud-based messaging tools. In this article, I will share my experience of working with the first type.

Message broker Apache Kafka

Pricing: free

Official website: https://kafka.apache.org/

Useful resources: documentation, books

Pros:

Multi-tenancy

Easy to pick up

Powerful event streaming platform

Fault-tolerance and reliable solution

Good scalability

Free community distributed product

Suitable for real-time processing

Excellent for big data projects

Cons:

Lack of ready to use elements

The absence of complete monitoring set

Dependency on Apache Zookeeper

No routing

Issues with an increasing number of messages

What do Netflix, eBay, Uber, The New York Times, PayPal and Pinterest have in common?  All these great enterprises have used or are using the world’s most popular message broker, Apache Kafka.

THE STORY OF KAFKA DEVELOPMENT

With numerous advantages for real-time processing and big data projects, this asynchronous messaging technology has conquered the world. How did it start?

In 2010 LinkedIn engineers faced the problem of integration huge amounts of data from their infrastructure into a lambda architecture. It also included Hadoop and real-time event processing systems.

As for traditional message brokers, they didn’t satisfy Linkedin needs. These solutions were too heavy and slow. So, the engineering team has developed the scalable and fault-tolerant messaging system without lots of bells and whistles. The new queue manager has quickly transformed into a full-fledged event streaming platform.

APACHE KAFKA CAPABILITIES

The technology has become popular largely due to its compatibility. Let’s see. We can use Apache Kafka with a wide range of systems. They are:

web and desktop custom applications

microservices, monitoring and analytical systems

any needed sinks or sources

NoSQL, Oracle, Hadoop, SFDC

With the help of Apache Kafka, you can successfully create data-driven applications and manage complicated back-end systems. The picture below shows 3 main capabilities of this queue manager.

 As you can see, Apache Kafka is able to:

publish and subscribe to streams of records with excellent scalability and performance, which makes it suitable for company-wide use.

durably store the streams, distributing data across multiple nodes for a highly available deployment.

process data streams as they arrive, allowing you aggregating, creating windowing parameters, performing joins of data within a stream, etc.

APACHE KAFKA KEY TERMS AND CONCEPTS

First of all, you should know about the abstraction of a distributed commit log. This confusing term is crucial for the message broker. Many web developers used to think about "logs" in the context of a login feature. But Apache Kafka is based on the log data structure. This means a log is a time-ordered, append-only sequence of data inserts. As for other concepts, they are:

topics (the stored streams of records)

records (they include a key, a value, and a timestamp)

APIs (Producer API, Consumer API,  Streams API, Connector API)

The interaction of the clients and the servers are implemented with easy to use and effective TCP protocol. It’s language agnostic standard. So, the client can be written in any language that you want.

KAFKA WORKING PRINCIPLE

There are 2 main patterns of messaging:

queuing

publish-subscribe

Both of them have some pros and cons. The advantage of the first pattern is the opportunity to easily scale the processing. On the other hand, queues aren't multi-subscriber. The second model provides the possibility to broadcast data to multiple consumer groups. At the same time, scaling is more difficult in this case.

Apache Kafka magically combines these 2 ways of data processing, getting benefits of both of them. It should be mentioned that this queue manager provides better ordering guarantees than a traditional message broker.

KAFKA PECULIARITIES

Combining the functions of messaging, storage, and processing, Kafka isn’t a common message broker. It’s a powerful event streaming platform capable of handling trillions of messages a day. Kafka is useful both for storing and processing historical data from the past and for real-time work. You can use it for creating streaming applications, as well as for streaming data pipelines.

If you want to follow the steps of Kafka users, you should be mindful of some nuances:

the messages don’t have separate IDs (they are addressed by their offset in the log)

the system doesn’t check the consumers of each topic or message

Kafka doesn’t maintain any indexes and doesn’t allow random access (it just delivers the messages in order, starting with the offset)

the system doesn’t have deletes and doesn’t buffer the messages in userspace (but there are various configurable storage strategies)

CONCLUSION

Being a perfect open-source solution for real-time statistics and big data projects, this message broker has some weaknesses. The thing is it requires you to work a lot. You will feel a lack of plugins and other things that can be simply reused in your code.

I recommend you to use this multiple publish/subscribe and queueing tool, when you need to optimize processing really big amounts of data ( 100 000 messages per second and more). In this case, Apache Kafka will satisfy your needs.

Message broker RabbitMQ

Pricing: free

Official website: https://www.rabbitmq.com

Useful resources: tools, best practices

Pros:

Suitable for many programming languages and messaging protocols

Can be used on different operating systems and cloud environments

Simple to start using and to deploy

Gives an opportunity to use various developer tools

Modern in-built user interface

Offers clustering and is very good at it

Scales to around 500,000+ messages per second

Cons:

Non-transactional (by default)

Needs Erlang

Minimal configuration that can be done through code

Issues with processing big amounts of data

The next very popular solution is written in the Erlang. As it’s a simple, general-purpose, functional programming language, consisted of many ready to use components, this software doesn’t require lots of manual work. RabbitMQ is known as a “traditional” message broker, which is suitable for a wide range of projects. It is successfully used both for development of new startups and notable enterprises.

The software is built on the Open Telecom Platform framework for clustering and failover. You can find many client libraries for using the queue manager, written on all major programming languages.

THE STORY OF RABBITMQ DEVELOPMENT

One of the oldest open source message brokers can be used with various protocols. Many web developers like this software, because of its useful features, libraries, development tools, and instructions.

 In 2007, Rabbit Technologies Ltd. had developed the system, which originally implemented AMQP. It’s an open wire protocol for messaging with complex routing features. AMQP ensured cross-language flexibility of using message broking solutions outside the Java ecosystem. In fact, RabbitMQ perfectly works with Java, Spring, .NET, PHP, Python, Ruby, JavaScript, Go, Elixir, Objective-C, Swift and many other technologies. The numerous plugins and libraries are the main advantage of the software. 

RABBITMQ CAPABILITIES

Created as a message broker for general usage, RabbitMQ is based on the pub-sub communication pattern. The messaging process can be either synchronous or asynchronous, as you prefer. So, the main features of the message broker are:

Support of numerous protocols and message queuing, changeable routing to queues, different types of exchange.

Clustering deployment ensures perfect availability and throughput. The software can be used across various zones and regions.

The possibilities to use Puppet, BOSH, Chef and Docker for deployment. Compatibility with the most popular modern programming languages.

The opportunity of simple deployment in both private and public clouds.  

Pluggable authentication, support of  TLS and LDAP, authorization.

Many of the proposed tools can be used for continuous integration, operational metrics, and work with other enterprise systems.

RABBITMQ WORKING PRINCIPLE

Being a broker-centric program, RabbitMQ gives guarantees between producers and consumers. If you choose this software, you should use transient messages, rather than durable.

The program uses the broker to check the state of a message and verify whether the delivery was successfully completed. The message broker presumes that consumers are usually online.

As for the message ordering, the consumers will get the message in the published order itself. The order of publishing is managed consistently.

RABBITMQ PECULIARITIES

The main advantage of this message broker is the perfect set of plugins, combined with nice scalability. Many web developers enjoy clear documentation and well-defined rules, as well as the possibility of working with various message exchange models. In fact, RabbitMQ is suitable for 3 of them:

Direct exchange model (individual exchange of topic one be one)

Topic exchange model (each consumer gets a message which is sent to a specific topic)

Fanout exchange model (all consumers connected to queues get the message).

Here you can see the gap between Kafka and RabbitMQ. If a consumer isn’t connected to a fanout exchange in RabbitMQ, the message will be lost. At the same time, Kafka allows avoiding this, because any consumer can read any message.

CONCLUSION

As for me, I like RabbitMQ due to the opportunity to use many plugins. They save time and speed-up work. You can easily adjust filters, priorities, message ordering, etc. Just like Kafka, RabbitMQ requires you to deploy and manage the software. But it has convenient in-built UI and allows using SSL for better security. As for abilities to cope with big data loads, here RabbitMQ is inferior to Kafka.

To sum up, both Apache Kafka and RabbitMQ truly worth the attention of skillful software developers. I hope, my article will help you find suitable big data technologies for your project. If you still have any questions, you are welcome to contact Freshcode specialists. In the next review we will compare other powerful messaging tools, ActiveMQ and Redis Pub/Sub.

The original article Introduction to message brokers. Part 1: Apache Kafka vs RabbitMQ was published at freshcodeit.com.