#spark sql vs hive

[dataplusweb-blog]

Dataiku : tout savoir sur la plateforme d'IA "made in France"

Dataiku :

tout savoir sur la plateforme d'IA "made in France"

Antoine Crochet-Damais

JDN

 

Dataiku est une plateforme d'intelligence artificielle créée en France en 2013. Elle s'est imposée depuis parmi les références mondiales des studios de data science et de machine learning.

SOMMAIRE

Dataiku, c’est quoi ?

Dataiku DSS, qu'est-ce que c'est ?

Quelles sont les fonctionnalités de Dataiku ?

Quel est le prix de Dataiku ?

Qu’est-ce que Dataiku Online ?

Dataiku Academy : formation / certification

Dataiku vs DataRobot

Dataiku vs Alteryx

Dataiku vs Databricks

Dataiku Community

Dataiku, c’est quoi ?

Dataiku est une plateforme de data science d'origine française. Elle se démarque historiquement par son caractère très packagé et intégré. Ce qui la met à la portée aussi bien des data scientists confirmés que débutants. Grâce à son ergonomie, elle permet de créer un modèle en quelques clics, tout en industrialisant en toile de fonds l'ensemble de la chaine de traitement : collecte, préparation des données…

Co-fondée en 2013 à Paris par Florian Douetteau, son CEO actuel, et Clément Stenac (tous deux anciens d'Exalead) aux côtés de Thomas Cabrol et Marc Batty, Dataiku affiche une croissance fulgurante. Dès 2015, la société s'implante aux Etats-Unis. Après une levée de 101 millions de dollars en 2018, Dataiku boucle un tour de table de 400 millions de dollars en 2021 pour une valorisation de 4,6 milliards de dollars. L'entreprise compte plus de 1000 salariés et plus de 300 clients parmi les plus grands groupes mondiaux. Parmi eux figurent les sociétés françaises Accor, BNP Paribas, Engie ou encore SNCF.

Dataiku DSS, qu'est-ce que c'est ?

Dataiku DSS (pour Dataiku Data Science Studio) est le nom de la plateforme d'IA de Dataiku.

Quelles sont les fonctionnalités de Dataiku ?

La plateforme de Dataiku compte environ 90 fonctionnalités que l'on peut regrouper en plusieurs grands domaines :

L'intégration. La plateforme s'intègre à Hadoop, Spark, mais aussi aux services des clouds AWS, Azure, Google Cloud. Au total, la plateforme est équipée de plus de 25 connecteurs. 

Les plugins. Une galerie de plus de 100 plugins permet de bénéficier d'applications tierces dans de nombreux domaines : traduction, NLG, météo, moteur de recommandation, import/export de données...

La data préparation / data ops. Une console graphique gère la préparation des données. Les time series et données géospatiales sont supportées. Plus de 90 data transformers prépackagés sont disponibles. 

Le développement. Dataiku prend en charge les notebooks Jupyter, les langages Python, R, Scala, SQL, Hive, Pig, Impala. Il supporte PySpark, SparkR et SparkSQL.

Le machine Learning. La plateforme inclut un moteur d'automatisation du machine learning (auto ML), une console de visualisation pour l'entrainement des réseaux de neurones profonds, le support de Scikit-learn et XGBoost, etc.

La collaboration. Dataiku intègre des fonctionnalités de gestion de projet, de chat, de wiki, de versioning (via Git)...

La gouvernance. La plateforme propose une console de monitoring des modèles, d'audit, ainsi qu'un feature store.

Le MLOps. Dataiku gère le déploiement de modèles. Il prend en charge les architecture Kubernetes mais aussi les offres de Kubernetes as a Service d'AWS, Azure et Google Cloud.

La data visualisation. Une interface de visualisation statistique est complétée par 25 graphiques de data visualisation pour identifier les relations et aperçus au sein des jeux de données.

Dataiku est conçu pour gérer graphiquement des pipelines de machine learning. © JDN / Capture

Quel est le prix de Dataiku ?

Dataiku propose une édition gratuite de sa plateforme à installer soi-même. Baptisée Dataiku Free, elle se limite à trois utilisateurs, mais donne accès à la majorité des fonctionnalités. Elle est disponible pour Windows, Linux, MacOS, Amazon EC2, Google Cloud et Microsoft Azure. 

Pour aller plus loin, Dataiku commercialise trois éditions dont les prix sont disponibles sur demande : Dataiku Discover pour les petites équipes, Dataiku Business pour les équipes de taille moyenne, et Dataiku Enterprise pour déployer la plateforme à l'échelle d'une grande entreprise.

Qu’est-ce que Dataiku Online ?

Principalement conçu pour de petites structures, Dataiku Online permet de gérer les projets de data science à une échelle modérée. Il s’agit d’un dispositif de type SaaS (Software as a Service). Les fonctionnalités sont similaires à Dataiku, mais le paramétrage et le lancement de l’application sont plus rapides.

Dataiku Academy : formation et certification Dataiku

La Dataiku Academy regroupe une série de formations en ligne à la plateforme de Dataiku. Elle propose un programme Quicks Start qui permet de commencer à utiliser la solution en quelques heures, mais aussi des sessions Learning Paths pour acquérir des compétences plus avancées. Chaque programme permet de décrocher une certification Dataiku : Core Designer Certificate, ML Practitioner Certificate, Advanced Designer Certificate, Developer Certificate et MLOps Practitioner Certificate.

Dataiku prend en charge les time series et données géospatiales. © JDN / Capture

Dataiku vs DataRobot

Créé en 2012, l'américain DataRobot peut être considéré comme le pure player historique du machine learning automatisé (auto ML). Un terrain sur lequel Dataiku s'est positionne plus tard. Au fur et à mesure de leur développement, les deux plateformes tendent désormais à être de plus en plus comparables.

Face à DataRobot, Dataiku se distingue cependant sur le front de la collaboration. L'éditeur multiplie les fonctionnalités dans ce domaine : wiki, partage de tableaux de bord de résultats, système de gestion des rôles et de traçabilité des actions, etc.

Dataiku vs Alteryx

Alors que Dataiku est avant tout une plateforme de data science orientée machine learning, Alteryx, lui, se positionne comme un solution d'intelligence décisionnelle ciblant potentiellement tout décideur d'entreprise, bien au-delà des équipes de data science.

La principale valeur ajoutée d'Alteryx est d'automatiser la création de tableaux de bord analytics. Des tableaux de bord qui pourront inclure des indicateurs prédictifs basés sur des modèles de machine learning. Dans cet optique, Alteryx intègre des fonctionnalités de machine learning automatisé (auto ML) pour permettre aux utilisateurs de générer ce type d'indicateur. C'est son principal point commun avec Dataiku.

Dataiku vs Databricks

Dataiku et Databricks sont des plateformes très différentes. La première s'oriente vers la data science, la conception et le déploiement de modèles de machine learning. La seconde se présente sous la forme d'une data platform universelle répondant à la fois aux cas d'usage orientés entrepôt de données et BI, data lake, mais aussi streaming de données et calcul distribué.

Reste que Databricks s'enrichit de plus en plus de fonctionnalités orientées machine learning. La société de San Francisco a acquis l'environnement de data science low-code / no code 8080 Labs en octobre 2021, puis la plateforme de MLOps Cortex Labs en avril 2022. Deux technologies qu'elle est en train d'intégrer. 

Dataiku Community : tutoriels et documentation

Dataiku Community est un espace d'échange et de documentation pour parfaire ses connaissances sur Dataiku et ses champs d'application. Après inscription, il est possible d'intégrer le forum de discussions.

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[hadoopcourse]

Spark vs Hadoop, which one is better?

Hadoop

Hadoop is a project of Apache.org and it is a software library and an action framework that allows the distributed processing of large data sets, known as big data, through thousands of conventional systems that offer power processing and storage space. Hadoop is, in essence, the most powerful design in the big data analytics space.

Several modules participate in the creation of its framework and among the main ones we find the following:

Hadoop Common (Utilities and libraries that support other Hadoop modules)

Hadoop Distributed File Systems (HDFS)

Hadoop YARN (Yet Another Resource Negociator), cluster management technology.

Hadoop Mapreduce (programming model that supports massive parallel computing)

Although the four modules mentioned above make up the central core of Hadoop, there are others. Among them, as quoted by Hess, are Ambari, Avro, Cassandra, Hive, Pig, Oozie, Flume, and Sqoop. All of them serve to extend and extend the power of Hadoop and be included in big data applications and processing of large data sets.

Many companies use Hadoop for their large data and analytics sets. It has become the de facto standard in big data applications. Hess notes that Hadoop was originally designed to handle crawling functions and search millions of web pages while collecting information from a database. The result of that desire to browse and search the Web ended up being Hadoop HDFS and its distributed processing engine, MapReduce.

According to Hess, Hadoop is useful for companies when the data sets are so large and so complex that the solutions they already have cannot process the information effectively and in what the business needs define as reasonable times.

MapReduce is an excellent word-processing engine, and that's because crawling and web search, its first challenges, are text-based tasks.

We hope you understand Hadoop Introduction tutorial for beginners. Get success in your career as a Tableau developer by being a part of the Prwatech, India’s leading hadoop training institute in btm layout.

Apache Spark Spark is also an open source project from the Apache foundation that was born in 2012 as an enhancement to Hadoop's Map Reduce paradigm . It has high-level programming abstractions and allows working with SQL language . Among its APIs it has two real-time data processing (Spark Streaming and Spark Structured Streaming), one to apply distributed Machine Learning (Spark MLlib) and another to work with graphs (Spark GraphX).

Although Spark also has its own resource manager (Standalone), it does not have as much maturity as Hadoop Yarn, so the main module that stands out from Spark is its distributed processing paradigm.

For this reason it does not make much sense to compare Spark vs Hadoop and it is more accurate to compare Spark with Hadoop Map Reduce since they both perform the same functions. Let's see the advantages and disadvantages of some of its features:

performance Apache Spark is up to 100 times faster than Map Reduce since it works in RAM memory (unlike Map Reduce that stores intermediate results on disk) thus greatly speeding up processing times.

In addition, the great advantage of Spark is that it has a scheduler called DAG that sets the tasks to be performed and optimizes the calculations .

Development complexity Map Reduce is mainly programmed in Java although it has compatibility with other languages . The programming in Map Reduce follows a specific methodology which means that it is necessary to model the problems according to this way of working.

Spark, on the other hand, is easier to program today thanks to the enormous effort of the community to improve this framework.

Spark is compatible with Java, Scala, Python and R which makes it a great tool not only for Data Engineers but also for Data Scientists to perform analysis on data .

Cost In terms of computational costs, Map Reduce requires a cluster that has more disks and is faster for processing. Spark, on the other hand, needs a cluster that has a lot of RAM.

We hope you understand Apache Introduction tutorial for beginners. Get success in your career as a Tableau developer by being a part of the Prwatech, India’s leading apache spark training institute in Bangalore.

[datalake-solutions-blog]

What are Data Lake Solutions? How does it work for Business Strategies

A data lake is regarded as the centralized repository by which it is possible to store unstructured and structured data at different scales. It is possible to store the data without the requirement to structuring the data. It also helps in running various kinds of analytics, which range from visualization to dashboard, to the processing of big data, machine learning, and real time analytics, for the guidance of better and effective decisions.

Benefits of data lake solution

Business firms which are known to fetch the business value from the data are going to beat the competitive edge. Studies reveal the fact that implementation of data lake can lead to a better growth in the organic revenue. 

The leaders are capable of doing latest kinds of analytics such as machine learning over different sources such as data from social media, click streams, long files, as well as internet connected devices which are known to be stored in the data lake. 

It plays a vital role in the identification and implementation of opportunities which facilitate better and faster growth of the business by providing a boost to the productivity of the business, retaining the users, maintaining of the devices. It also plays a vital role in making informed decisions for the business.

Round the clock availability of data

With the aid of data lake solution, it is possible to ensure that the employees, regardless of their designation is capable of getting access to the data. It is referred to as data democratization. 

Take an example where at present, only the upper management in the organization is authorized to collect different kinds of data for seeking an understanding of the things, prior to taking any kind of vital decision. With the aid of Data Lake, it is possible to make the necessary data available to different levels of the employees, regardless of their designation or level. 

In case you are a member of the admin department, you will have every access to the admin data, related to the unused stationery, and used stationery, to name a few. In addition to this, you can get access to the data, which you intend to ignore. Data Lake boasts of an immense processing power which is useful to the business organization in fetching top quality of data.

Decision analysis in real time

Data lake solutions have earned a high reputation in reaping the benefits of larger quantity of consistent data along with deep learning algorithm for the arrival at the real time decision analytics.

Support to SQL as well as other different languages

Conventional data warehouse technologies bestow support to the SQL which is ideal for analytics. You require more alternatives for the analysis of data in the advanced use cases. Big Data Lake provides different options along with language support for the objective of analysis. It also comes with Hawk/Impala/Hive which bestows support to SQL.

In addition to this, it is equipped with specific features which are useful in taking different kinds of advance requirements. Say, for example, you possess PIG for the purpose of machine learning for the analysis of data flow and you can make use of Spark MLlib.

Versatile

A data lake is capable of storing both unstructured and structured data from a wide assortment of resources. Also, it is capable of storing multimedia, logs, XML, chat, sensor data, binary, social data as well as people data.

Scalable

Data lakes play a vital role in providing scalability which involve a reduced cut off from the pocket.

Also Read: Data Warehousing – Traditional vs Cloud!

Schema flexibility

For the traditional schema, it is a prerequisite to have data in the structured format. The traditional products, present in the data warehouse is known to be schema based. For analytics, it can be a glitch as the data should be analyzed in the raw form. 

With the aid of Hadoop Data Lake, it is possible to be schema free. You can also come up with a plethora of schemas for similar data. Thus, it short, it is useful in the separation of schema from the data which is certainly a good option for the analytics.

Resolving traditional challenges of warehouse

At present, the users of business depend on content repositories as well as diverse applications for bestowing support to their daily work as well as strategic goals. This results in higher demand, more effective and faster access to data, as well as analytics at the fingertips of the end users.

However, complicated database skills are required for Business Intelligence applications and traditional data warehouses for getting access to the data. Several days or hours may be consumed for the retrieval of data which are required for the analysis from the administrators of the data warehouse. Beside this, the proprietary data warehouses are useful in bringing a reduction in the flexibility and scalability.

High Performing data lake solution

Data Lake is considered to be the repository of the enterprise wide raw data. It is also combined with big data as well as other search engines. Data Lake is known to bring together data from different resources owing to which it becomes searchable. It helps in maximizing the analytics, discovery and reporting of different capabilities for the end users.

Data lake solutions have earned a high reputation for the data richness. Thus, it is capable of storing as well as processing unstructured and structured data from different types, multiple sources, which are inclusive of JSON, text, XML, video, image, and audio. 

Search is regarded as a universal tool to find information. Thus, it is likely that end users can receive the specific data, which they were looking for in no time with the aid of search engine, without any kind of SQL language.

In addition to this, open source is known to have zero licensing costs which help the system in scaling with the growth of the data. Data Lake works in conjugation with the data warehouse for ensuring an integrated data strategy. Data lake solutions work for implementation of the most effective business strategies which help in enhancing the bottom line of the business. If you’re making any drastic changes or improvements at your product or software, doesn’t it make sense to go with a company like Indium Software - Leading Data Warehouse Solution Provider.

Thanks and Regards,

Gracesophia

[patrickpstapleton]

Sticky

Hello, random tumblr user and/or fellow aspiring data scientists. I’m a former prep cook / pastry cook / technical writer learning big data programming with scala. Since I’m not really sure how to blog about that yet, this blog will soon be filled with links to resources for what I’m learning. I may also occasionally reblog something Animal Crossing related, and I may eventually figure out how to blog about what I’m learning.

I’m going to keep an index here so it’s easy to find my resource posts, since tumblr can be difficult to navigate. I’ll add links to resource posts as I make them.

Hortonworks Data Platform (HDP) Sandbox: post link

Spark SQL resource links here: post link

Agile / Scrum resource links here: post link

Planned future posts:

Big data

Scala

Python

SQL vs NoSQL

MongoDB

Hadoop

Hive

Spark

Kafka

[avantaglobal]

Data Analytics using Tableau Course

What is Tableau?

Tableau may be a powerful and quickest growing knowledge image tool utilized in the Business Intelligence business. It helps in simplifying information during a very simply perceivable format. Tableau helps produce the info which will be understood by professionals at any level in a company. It additionally permits non-technical users to make custom-built dashboards.

Data analysis is incredibly quick with Tableau tool and also the visualizations created are within the style of dashboards and worksheets.

The best features of Tableau software are

Data Blending

Real time analysis

Collaboration of data

The great factor concerning Tableau computer code is that it doesn’t need any technical or any reasonably programming skills to control. The tool has garnered interest among the individuals from all sectors like business, researchers, completely different industries, etc.

In this article, you'll learn-

·         What is Tableau?        

·         Tableau product suite

·         Tableau Desktop

·         Tableau Public

·         Tableau Server

·         Tableau Online

·         Tableau Reader

·         How does Tableau work?

·         Tableau Uses

·         Excel Vs. Tableau

 Tableau Product Suite

The Tableau Product Suite consists of:

·         Tableau Desktop

·         Tableau Public

·         Tableau on-line

·         Tableau Server

·         Tableau Reader

For a transparent understanding, knowledge analytics in Tableau tool may be classified into 2 sections.

Developer Tools: The Tableau tools that are used for development like the creation of dashboards, charts, report generation, image make up this class. The Tableau merchandise, below this class, are the Tableau Desktop and also the Tableau Public.

Sharing Tools: because the name suggests, the aim of those Tableau merchandise is sharing the visualizations, reports, dashboards that were created mistreatment the developer tools. merchandise that makes up this class are Tableau on-line, Server, and Reader.

Let’s study all the Tableau merchandise one by one.

·         Tableau Desktop

Tableau Desktop encompasses a wealthy feature set and permits you to code and customise reports. Right from making the charts, reports, to mixing all along to create a dashboard, all the required work is formed in Tableau Desktop.

For live knowledge analysis, Tableau Desktop provides property to knowledge Warehouse, also as alternative varied forms of files. The workbooks and also the dashboards created here may be either shared regionally or in public.

Based on the property to the info sources and publication possibility, Tableau Desktop is assessed into

Tableau Desktop Personal: the event options are almost like Tableau Desktop. Personal version keeps the book non-public, and also the access is proscribed. The workbooks can't be printed on-line. Therefore, it ought to be distributed either Offline or in Tableau Public.

Tableau Desktop Professional: it's just about almost like Tableau Desktop. The distinction is that the work created within the Tableau Desktop may be printed on-line or in Tableau Server. Also, in skilled version, there's full access to any or all kinds of the datatype. it's best appropriate for people who would like to publish their add Tableau Server.

·         Tableau Public

It is Tableau version specially build for the cost-efficient users. By the word “Public,” it means the workbooks created can't be saved locally; successively, it ought to be saved to the Tableau’s public cloud which may be viewed and accessed by anyone.

There is no privacy to the files saved to the cloud since anyone will transfer and access an equivalent. This version is that the best for the people World Health Organization need to be told Tableau and for those World Health Organization need to share their knowledge with the final public.

·         Tableau Server

The computer code is specifically accustomed share the workbooks, visualizations that ar created within the Tableau Desktop application across the organization. To share dashboards within the Tableau Server, you need to initial publish you add the Tableau Desktop. Once the work has been uploaded to the server, it'll be accessible solely to the accredited users.

However, it’s not necessary that the accredited users have to be compelled to have the Tableau Server put in on their machine. they solely need the login credentials with that they will check reports via an internet browser. the safety is high in Tableau server, and it's abundant suited to fast and effective sharing of information in a company.

The admin of the organization can invariably have full management over the server. The hardware and also the computer code are maintained by the organization.

  ·         Tableau on-line

As the name suggests, it's an internet sharing tool of Tableau. Its functionalities are almost like Tableau Server; however, the info is kept on servers hosted within the cloud that are maintained by the Tableau cluster.

There is no storage limit on the info which will be printed within the Tableau on-line. Tableau on-line creates a right away link to over forty knowledge sources that are hosted within the cloud like the MySQL, Hive, Amazon Aurora, Spark SQL and plenty of a lot of.

To publish, each Tableau on-line and Server need the workbooks created by Tableau Desktop. knowledge that's streamed from the net applications say Google Analytics, Salesforce.com are supported by Tableau Server and Tableau on-line.

·         Tableau Reader

Tableau Reader may be a free tool that permits you to look at the workbooks and visualizations created mistreatment Tableau Desktop or Tableau Public. the info may be filtered however redaction and modifications are restricted. the safety level is zero in Tableau Reader as anyone World Health Organization gets the book will read it mistreatment Tableau Reader.

If you wish to share the dashboards that you simply have created, the receiver ought to have Tableau Reader to look at the document.

How will Tableau work?

Tableau connects and extracts the info keep in varied places. It will pull knowledge from any platform thinkable. a straightforward info like AN surpass, pdf, to a posh info like Oracle, a info within the cloud like Amazon webs services, Microsoft Azure SQL info, Google Cloud SQL and varied alternative knowledge sources may be extracted by Tableau.

When Tableau is launched, prepared knowledge connectors are on the market that permits you to attach to any info. looking on the version of Tableau that you simply have purchased the amount of information connectors supported by Tableau can vary.

The force knowledge may be either connected live or extracted to the Tableau’s knowledge engine, Tableau Desktop. this can be wherever the info analyst, knowledge engineer work with the info that was force up and develop visualizations. The created dashboards are shared with the users as a static file. The users World Health Organization receive the dashboards views the file mistreatment Tableau Reader.

The data from the Tableau Desktop may be printed to the Tableau server. this can be AN enterprise platform wherever collaboration, distribution, governance, security model, automation options are supported. With the Tableau server, the top users have an improved expertise in accessing the files from all locations be it a desktop, mobile or email.

Tableau Uses

Following are the most uses and applications of Tableau:

Business Intelligence

Data Visualization

Data Collaboration

Data Blending

Real-time data analysis

Query translation into visualization

To import large size of data

To create no-code data queries

To manage large size metadata

Excel Vs. Tableau

Both surpass and Tableau are knowledge analysis tools, however every tool has its distinctive approach to knowledge exploration. However, the analysis in Tableau is more impregnable than surpass.

Excel works with rows and columns in spreadsheets whereas Tableau permits in exploring surpass knowledge mistreatment its drag and drop feature. Tableau formats the info in Graphs, photos that are simply perceivable.

Tableau beats surpass in major areas just like the interactive dashboards, visualizations, capabilities to figure with large-scale knowledge and plenty of a lot of.

[wonbindatascience]

Data Analysts vs Data Scientists

Data analysts

What they do?

sift through data and seek to identify trends. What stories do the numbers tell? What business decisions can be made based on these insights? They may also create visual representations, such as charts and graphs to better showcase what the data reveals.

Requirements

Degree in mathematics, statistics, or business, with an analytics focus

Experience working with languages such as SQL/CQL, R, Python

A strong combination of analytical skills, intellectual curiosity, and reporting acumen

A solid understanding of data mining techniques, emerging technologies (MapReduce, Spark, large-scale data frameworks, machine learning, neural networks) and a proactive approach, with an ability to manage multiple priorities simultaneously

Familiarity with agile development methodology

Exceptional facility with Excel and Office

Strong written and verbal communication skills

Data scientists

What they do

are pros at interpreting data, but also tend to have coding and mathematical modeling expertise. Most data scientists hold an advanced degree, and many actually went from data analyst to data scientist. They can do the work of a data analyst, but are also hands-on in machine learning, skilled with advanced programming, and can create new processes for data modeling. They can work with algorithms, predictive models, and more.

Requirements

Master’s or Ph.D. in statistics, mathematics, or computer science

Experience using statistical computer languages such as R, Python, SQL, etc.

Experience in statistical and data mining techniques, including generalized linear model/regression, random forest, boosting, trees, text mining, social network analysis

Experience working with and creating data architectures

Knowledge of machine learning techniques such as clustering, decision tree learning, and artificial neural networks

Knowledge of advanced statistical techniques and concepts, including regression, properties of distributions, and statistical tests

5-7 years of experience manipulating data sets and building statistical models

Experience using web services: Redshift, S3, Spark, DigitalOcean, etc.

Experience analyzing data from third-party providers, including Google Analytics, Site Catalyst, Coremetrics, AdWords, Crimson Hexagon, Facebook Insights, etc.

Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.

Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc.

https://www.springboard.com/blog/data-analyst-vs-data-scientist/

[isearchgoood]

December 20, 2019 at 10:00PM - Big Data Mastery with Hadoop Bundle (89% discount) Ashraf

Big Data Mastery with Hadoop Bundle (89% discount) Hurry Offer Only Last For HoursSometime. Don't ever forget to share this post on Your Social media to be the first to tell your firends. This is not a fake stuff its real.

Big data is hot, and data management and analytics skills are your ticket to a fast-growing, lucrative career. This course will quickly teach you two technologies fundamental to big data: MapReduce and Hadoop. Learn and master the art of framing data analysis problems as MapReduce problems with over 10 hands-on examples. Write, analyze, and run real code along with the instructor– both on your own system, and in the cloud using Amazon’s Elastic MapReduce service. By course’s end, you’ll have a solid grasp of data management concepts.

Learn the concepts of MapReduce to analyze big sets of data w/ 56 lectures & 5.5 hours of content

Run MapReduce jobs quickly using Python & MRJob

Translate complex analysis problems into multi-stage MapReduce jobs

Scale up to larger data sets using Amazon’s Elastic MapReduce service

Understand how Hadoop distributes MapReduce across computing clusters

Complete projects to get hands-on experience: analyze social media data, movie ratings & more

Learn about other Hadoop technologies, like Hive, Pig & Spark

Hadoop is perhaps the most important big data framework in existence, used by major data-driven companies around the globe. Hadoop and its associated technologies allow companies to manage huge amounts of data and make business decisions based on analytics surrounding that data. This course will take you from big data zero to hero, teaching you how to build Hadoop solutions that will solve real world problems – and qualify you for many high-paying jobs.

Access 43 lectures & 10 hours of content 24/7

Learn how technologies like Mapreduce apply to clustering problems

Parse a Twitter stream Python, extract keywords w/ Apache Pig, visualize data w/ NodeJS, & more

Set up a Kafka stream w/ Java code for producers & consumers

Explore real-world applications by building a relational schema for a health care data dictionary used by the US Department of Veterans Affairs

Log collections & analytics w/ the Hadoop distributed file system using Apache Flume & Apache HCatalog

Have you ever wondered how major companies, universities, and organizations manage and process all the data they’ve collected over time? Well, the answer is Big Data, and people who can work with it are in huge demand. In this course you’ll cover the MapReduce algorithm and its most popular implementation, Apache Hadoop. Throughout this comprehensive course, you’ll learn essential Big Data terminology, MapReduce concepts, advanced Hadoop development, and gain a complete understanding of the Hadoop ecosystem so you can become a big time IT professional.

Access 76 lectures & 15.5 hours of content 24/7

Learn how to setup Node Hadoop pseudo clusters

Understand & work w/ the architecture of clusters

Run multi-node clusters on Amazon’s Elastic Map Reduce (EMR)

Master distributed file systems & operations including running Hadoop on HortonWorks Sandbok & Cloudera

Use MapReduce w/ Hive & Pig

Discover data mining & filtering

Learn the differences between Hadoop Distributed File System vs. Google File System

Hadoop is one of the most commonly used Big Data frameworks, supporting the processing of large data sets in a distributed computing environment. This tool is becoming more and more essential to big business as the world becomes more data-driven. In this introduction, you’ll cover the individual components of Hadoop in detail and get a higher level picture of how they interact with one another. It’s an excellent first step towards mastering Big Data processes.

Access 30 lectures & 5 hours of content 24/7

Install Hadoop in Standalone, Pseudo-Distributed, & Fully Distributed mode

Set up a Hadoop cluster using Linux VMs

Build a cloud Hadoop cluster on AWS w/ Cloudera Manager

Understand HDFS, MapReduce, & YARN & their interactions

Take your Hadoop skills to a whole new level by exploring its features for controlling and customizing MapReduce to a very granular level. Covering advanced topics like building inverted indexes for search engines, generating bigrams, combining multiple jobs, and much more, this course will push your skills towards a professional level.

Access 24 lectures & 4.5 hours of content 24/7

Cover advanced MapReduce topics like mapper, reducer, sort/merge, partitioning, & more

Use MapReduce to build an inverted index for search engines & generate bigrams from text

Chain multiple MapReduce jobs together

Write your own customized partitioner

Sort a large amount of data by sampling input files

Analyzing data is an essential to making informed business decisions, and most data analysts use SQL queries to get the answers they’re looking for. In this course, you’ll learn how to map constructs in SQL to corresponding design patterns for MapReduce jobs, allowing you to understand how these two programs can be leveraged together to simplify data problems.

Access 49 lectures & 1.5 hours of content 24/7

Master the art of “thinking parallel” to break tasks into MapReduce transformations

Use Hadoop & MapReduce to implement a SQL query like operations

Work through SQL constructs such as select, where, group by, & more w/ their corresponding MapReduce jobs in Hadoop

You see recommendation algorithms all the time, whether you realize it or not. Whether it’s Amazon recommending a product, Facebook recommending a friend, Netflix, a new TV show, recommendation systems are a big part of internet life. This is done by collaborative filtering, something you can perform through MapReduce with data collected in Hadoop. In this course, you’ll learn how to do it.

Access 4 lectures & 1 hour of content 24/7

Master the art of “thinking parallel” to break tasks into MapReduce transformations

Use Hadoop & MapReduce to implement a recommendations algorithm

Recommend friends on a social networking site using a MapReduce collaborative filtering algorithm

Data, especially in enterprise, will often expand at a rapid scale. Hadoop excels at compiling and organizing this data, however, to do anything meaningful with it, you may need to run machine learning algorithms to decipher patterns. In this course, you’ll learn one such algorithm, the K-Means clustering algorithm, and how to use MapReduce to implement it in Hadoop.

Access 7 lectures & 1.5 hours of content 24/7

Master the art of “thinking parallel” to break tasks into MapReduce transformations

Use Hadoop & MapReduce to implement the K-Means clustering algorithm

Convert algorithms into MapReduce patterns

from Active Sales – SharewareOnSale https://ift.tt/2iKO0kW https://ift.tt/eA8V8J via Blogger https://ift.tt/36WGZFt #blogger #bloggingtips #bloggerlife #bloggersgetsocial #ontheblog #writersofinstagram #writingprompt #instapoetry #writerscommunity #writersofig #writersblock #writerlife #writtenword #instawriters #spilledink #wordgasm #creativewriting #poetsofinstagram #blackoutpoetry #poetsofig

[manipriyan-blog]

Data science vs Big Data vs Data Analytics

Data Science

It is a field of analyzing the large amount of raw data. It categorizes the raw data into useful patterns. The patterns are used to form more concrete set of questions. The answers for these questions give the correlation between the data sets. This correlation and the patterns will be useful in solving more problems.

The Data science uses the predictive analysis. It also uses the statistics and the AI for data analysis. The queries framed in data science is used to make the form. It is also useful in groups. Groups are also known as the clusters. This clustering is further used to identify the business analysis. Data Science will take unordered data. It will convert it in to the ordered format. The ordered format is very useful for further analysis.

The Data Science is a potential field. It is used to analyze the huge amount of the data. Finally, it will find useful answers from the large amount of the data. It is used to predict the search results based on the input. It recommends the users about the most visited pages. Data science will also recommend the products.

Big Data

Big data is used for gathering and storing large amount of information. It contains both structured as well as unstructured data. There are three Vs in big data. They are Volume, Velocity, Variety. Volume stands for data. Velocity stands for the speed of data. Variety stands for the number of types of the data.

In big data, we can handle any data from any sources. It will analyze the given data to find the answer. The Answer makes possible for the cost saving as well as the time reduction. New product development is feasible with the big data. Understand the market conditions is easy with big data.  It will control the online reputation.

Big data Spark is an open source framework. It is from the Apache Software Foundation. Big data spark is a computing engine. Using this, we can process and analyze the large amount of real time data. Mainly inter-connected platforms system uses this.

Big data spark processes data very quickly.  Transferring details from computer’s hard disk is possible using this spark. Faster electronic memory will store the transferred data. It also works with the cluster as well as process the data in parallel.

Data Analytics

It is the science of raw data used to give meaningful info and results. The results we derived are from the existing data. It is simply said to be the data analytics. It is implementing algorithmic process of extracting the raw data.

Many companies use this Data analytics process. This process will enable them to take effective decision. They also verify and disprove the old models or theory. Data analytics is one of the most powerful tools. The result will be from the fact known to the researchers.

It is the process of understand and devising the effective pattern. The patterns may be of recorded data using math as well as statistics. The Machine learning Technique as well as predictive modeling uses this.

Data Science vs Big Data vs Data Analytics in Tools and Technologies Perspective

The data analytics tools are used to achieve our goals. The most popular analytics tools are SAS, Python, R as well as Hadoop. ClickView, Tableau Microsoft are also some of the popular tool used. Following are tools and technologies related to these three terms.

Big Data Tools

Hadoop

Hadoop is an open source frame work. It is a java based frame work. It is responsible for running application as well as storing data. Using Cluster of commodities hardware, we can able to store these data. It will allow expansive storage of the varied range of data. Hadoop will concentrate on financial management as well as operation.

Hadoop is one of the best big data tools. They are highly scalable. It is flexible to store the big data. It makes computer to perform fast. They have high tolerance against the hardware malfunctions to protect data.

NoSQL

NoSQL is one of the most important big data tools. It will handle both structured as well as unstructured data. The scope as well as applications will differentiate NoSQL from SQL. It doesn’t not used any technology to store unstructured data. They have common values in each set of rows. NoSQL will work effectively to store large amount of data. There are number of open source NoSQL data base use to analysis data.

HIVE

Apache Hive is the best distributed data management tool for Hadoop. Hive has its own query language. It is like the SQL. HiveSQL is the query language of the Hive. It is also known as HSQL. Hive query language will run on the top of the Hadoop architecture. Data mining as well as data management uses this.

Data Analytics Tools and Languages

R

R is an open source programming language as well as software environment. It will facilitate the graphics as well as the statistical computing. The Data miners will use the R programming language. The Statisticians will use this R to develop software for statistical analysis. The Data analysis will also use this.

The Social media sites will use R. Manufacturing, predictive model for the automotive uses the R. The R is famous in data visualization in the journalism. We can use R in the finance & banking as well as Drugs & food manufacturing. To generate a report, we will use the R programming.

To read more Click here: Data science vs Big Data vs Data Analytics

[mindzcloudconsulting-blog]

Hadoop Tutorial for Beginners | Hadoop Online Training

Course Description

This Online Course will give you a cognizance into the majority of the musings identified with Hadoop. It will help you in learning the considerations of Hadoop close-by one of a kind Hadoop natural system devices, for example, MapReduce, Yarn, Pig, Hive, Oozie, Sqoop, Flume. Additionally, This course will in like way give you a brief about NoSQL. It will cover the underneath subjects in detail:

big Data

Hadoop

map Reduce

Hive

No SQL

Course Overview:

This course is proposed for individuals wanting to change Big Data. This course revolves around these occupations for Hadoop Technology Hadoop and Spark Developer, Hadoop Analyst, Hadoop Tester,hadoop online course

BigData Introduction and Hadoop as a Solution:

In this session, you will find a few solutions concerning What is Big Data, Evolution of Big Data, Big Data Use Cases, Introduction to Hadoop, Hadoop Cluster Using Commodity Hardware, big data online course

Planning of Hadoop:

In this session, you will find a few solutions concerning Hadoop Architecture, Hadoop Daemons/Services in Hadoop Release-1, Hadoop Cluster and Racks, Hadoop Architecture Breakdown and different segments Overview.

HDFS - Hadoop Distributed File System:

In this session, you will find a few solutions concerning HDFS Architecture, Basic Unix heading for Hadoop, NameNode Operation, Data Block Split and Benefits.

Guide Reduce Framework:

In this session, you will find a few solutions concerning How Map Reduce fills in as Processing Framework, End to End execution stream of Map Reduce work, Different undertakings in Map Reduce occupation, Combiner and Partitioner, Characteristics of MapReduce, Real-time Uses of MapReduce, Map Reduce complex conditions.

YARN Framework And Advanced MapReduce Framework:

In this session, you will find a few solutions concerning Introduction to YARN, Why YARN If MapReduce Already there?, In-Depth YARN Architecture, Role of every single segment of YARN Architecture, Distributed Cache, Input Formats in MapReduce, Output Formats in MapReduce, Data Types in Hadoop, Joins in MapReduce, Reduce Side Join, Map Side Join, Skewed Join, Replicated Join, Composite Join, Cartesian Product

Pig (1):

In this session, you will find a few solutions concerning Pig Introduction, Brief History and Reason for Naming this area as Pig, Pig Real Life Use Cases Introduction, How Pig Works, Pig Execution Modes, Pig Features

Pig (2):

In this session, you will find concerning Why Pig if MR Already there?, Data Model in Pig, Pig Data Types, Pig Latin Language Introduction, Pig Latin Manual with direction, Functions, Pig UDFs

Pig (3):

In this session, you will find a few solutions concerning Processing Structured Data utilizing Pig, Processing Semi-Structured Data utilizing Pig, Pig Libraries, Pig Complex Data Types, When to utilize Pig and when not to?

read more....ui ux design course

Hive (1):

In this session, you will find a few solutions concerning What is Hive and Why we have Hive when Pig and MR Already there?, Brief History of Hive, Hive Architecture and its parts, Hive Metastore and its strategy types, Hive Thrift Server, Hive Query Language (HQL), Hive versus SQL

Hive (2):

In this session, you will find a few solutions concerning Types of Tables in Hive, HQL Syntax, Data Types In Hive, Run and Execute Hive demand, Hive question making and execution modes

Hive (3):

In this session, you will find a few solutions concerning Programming utilizing Hive, Hive Functions - Built-in and UDFs, UDAFs utilizing Hive, Hive Versions and the highlights combined into different structures, Partitioning and Bucketing in Hive, When to utilize Hive and when not to?

SQOOP:

In this session, you will find a few solutions concerning What is Sqoop, Introducing Data Import/Export Tools, Sqoop and Its Uses, Benefits of Sqoop, Sqoop Processing, Sqoop Execution Process, Importing Data to Hive Directly, Exporting Data from Hadoop Using Sqoop

Oozie (1):

In this session, you will find a few solutions concerning Introduction to Oozie, How to plan occupations utilizing Oozie, What sort of jobs can be saved utilizing Oozie

Oozie (2):

In this session, you will find a few solutions concerning How to timetable occupations which are time fragile, Apache Oozie Workflow and Coordinator, Oozie Bundles

NoSQL DB Overview:

In this session, you will find a few solutions concerning Introduction to NoSQL DB, NoSQL DB Vs RDBMS, Schemaless Approach clarified, CAP Theorem with Real-time model, ACID Vs. Top, Which NoSQL to use in various conditions?, Types of NoSQL DBs

[hadoopcourse]

Difference between Hadoop and Spark in 2020

Hadoop is an open source project of the Apache foundation and created in 2011 that allows processing large volumes of data benefiting from distributed computing. Learn Hadoop Training in Bangalore from prwatech with our professional skilled trainers.

 It is made up of different modules that make up the complete framework, among them we can highlight:

Hadoop Distributed File System (HDFS): Distributed file system

Hadoop Yarn: Cluster Resource Manager

Hadoop Map Reduce: Programming paradigm oriented to distribute     processing.

Spark is also an open source project from the Apache foundation that was born in 2012 as an enhancement to Hadoop's Map Reduce paradigm. It has high-level programming abstractions and allows working with SQL language.

Hadoop Uses                                                        

It is an Apache.org project. Hadoop can scale from individual computer systems to thousands of basic systems that offer local storage and computability.

Companies that use large data sets and analytics use Hadoop. It has become an important big data application. Hadoop was originally designed to handle the crawling and searching of billions of web pages and collect their information in a database. The result of the desire to crawl and search the web was Hadoop's HDFS and its distributed processing engine, MapReduce.

Uses of Spark

Spark is very fast, it is up to 100 times faster than Hadoop MapReduce. Spark can do batch processing, too, but it really excels at streaming workloads, interactive queries, and machine-based learning.

Comparison: Spark vs Hadoop

The reason Spark is so fast is because it processes everything in memory. Spark's in-memory processing provides near-real-time analytics for marketing campaign data, machine learning, Internet of Things sensors, record monitoring, security analytics, and social media sites.

Whereas, MapReduce alternately uses batch processing and was never actually built for amazing speed. Initially, it was configured to collect information from websites and there was no requirement for this data in real time or near real time.

Spark vs Hadoop: Ease of Use

Apache Spark is well known for its ease of use as it comes with easy to use APIs for Scala, Java, Python, and Spark SQL.

Spark also has an interactive mode so that developers and users can have immediate feedback on queries and other actions.

In contrast, Hadoop MapReduce has no interactive mode; however plugins like Hive and Pig make working with MapReduce a bit easier for adopters.

We’re Providing Advanced Apache Spark Course and Apache Spark Training in Bangalore with Our certified IT industry professionals will help you to learn the concepts of Scala, RDD, OOPS to enroll yourself at prwatech institute. 

Spark and Hadoop compatibility

MapReduce and Spark are compatible with each other.

Spark vs Hadoop: Data Processing

MapReduce is an engine where it is batch processed. MapReduce operates in sequential steps when reading data from the cluster, performing its operation on the data, writing the results back to the cluster, reading updated data from the cluster, performing the following data operation, writing those results back to the cluster, etc..

Apache Spark performs similar operations; however, it does it in one step and in memory. It reads data from the cluster, performs its operation on the data, and then writes it back to the cluster.

Spark has its own graph calculation library, GraphX. GraphX, and allows users to view the same data as graphs and collections.

Hadoop vs Spark: fault tolerance

MapReduce and Spark solve the problem from two different directions. MapReduce uses TaskTrackers that beat the JobTracker. If a heartbeat is missed, JobTracker reprograms all pending and ongoing operations to another TaskTracker. This method is effective in providing fault tolerance; however, it can significantly increase completion times for operations that have even a single error.

Spark uses Resilient Distributed Datasets (RDDs), which are sets of fault-tolerant elements that can be operated in parallel. RDDs can refer to a data set on an external storage system, such as a shared file system, HDFS, HBase, or any data source that offers a Hadoop InputFormat.

Spark vs Hadoop: Scalability

Both MapReduce and Apache Spark are scalable using HDFS.

Reports indicate that Yahoo has a Hadoop cluster of 42,000 nodes, so perhaps the limit is endless.

The largest known Spark cluster is 8,000 nodes, but as the big data grows, cluster sizes are expected to increase to maintain performance expectations.

Hadoop offers us features that Spark does not have, such as a distributed file system and Spark provides real-time processing in memory for those data sets that require it.

If you are interested in learning more about Hadoop and spark, enroll with Hadoop Admin Training in Bangalore to get advanced apache spark training in Bangalore from excellent skilled trainers.

[jobisite11]

Hadoop Developer at Raleigh, NC

only H1B GC Citizens. No EAD?S Education/Experience ? Bachelor?s degree in computer science or mathematics OR Associate?s degree specifically in computer science field. ? 5+ years? development experience, 7+ years? experience required if no degree. ? 2+ years? experience with Ab Initio ETL Development ? Unix scripting experience ? Experience with Hadoop, HDFS, Hive, and Spark ? Experience with Python 3+ ? Development experience with at least one of the major database systems (i.e. DB2, Oracle, SQL Server, Teradata). ? Experience working with vendors for effective solution delivery. ? Experience with SDLC and Agile vs Waterfall development Additional Education/Experience Preferences ? Java Development experience ? Experience with NoSQL databases (e.g., MongoDB, Cassandra, etc.) ? Experience with integrated solutions. ? Experience with testing methodologies with the stated major development languages. ? Experience with software development tools (i.e. Rational) ? Familiarity with using REST API ? Experience with Reference-, Meta-, and Master- Data Management ? Experience with Data Quality frameworks HadoopDeveloperatRaleigh,NC from Job Portal https://www.jobisite.com/extrJobView.htm?id=135297

[usajobsite]

Hadoop Developer at Raleigh, NC

only H1B GC Citizens. No EAD?S Education/Experience ? Bachelor?s degree in computer science or mathematics OR Associate?s degree specifically in computer science field. ? 5+ years? development experience, 7+ years? experience required if no degree. ? 2+ years? experience with Ab Initio ETL Development ? Unix scripting experience ? Experience with Hadoop, HDFS, Hive, and Spark ? Experience with Python 3+ ? Development experience with at least one of the major database systems (i.e. DB2, Oracle, SQL Server, Teradata). ? Experience working with vendors for effective solution delivery. ? Experience with SDLC and Agile vs Waterfall development Additional Education/Experience Preferences ? Java Development experience ? Experience with NoSQL databases (e.g., MongoDB, Cassandra, etc.) ? Experience with integrated solutions. ? Experience with testing methodologies with the stated major development languages. ? Experience with software development tools (i.e. Rational) ? Familiarity with using REST API ? Experience with Reference-, Meta-, and Master- Data Management ? Experience with Data Quality frameworks HadoopDeveloperatRaleigh,NC from Job Portal https://www.jobisite.com/extrJobView.htm?id=135297

[jobisitejobs]

Hadoop Developer at Raleigh, NC

only H1B GC Citizens. No EAD?S Education/Experience ? Bachelor?s degree in computer science or mathematics OR Associate?s degree specifically in computer science field. ? 5+ years? development experience, 7+ years? experience required if no degree. ? 2+ years? experience with Ab Initio ETL Development ? Unix scripting experience ? Experience with Hadoop, HDFS, Hive, and Spark ? Experience with Python 3+ ? Development experience with at least one of the major database systems (i.e. DB2, Oracle, SQL Server, Teradata). ? Experience working with vendors for effective solution delivery. ? Experience with SDLC and Agile vs Waterfall development Additional Education/Experience Preferences ? Java Development experience ? Experience with NoSQL databases (e.g., MongoDB, Cassandra, etc.) ? Experience with integrated solutions. ? Experience with testing methodologies with the stated major development languages. ? Experience with software development tools (i.e. Rational) ? Familiarity with using REST API ? Experience with Reference-, Meta-, and Master- Data Management ? Experience with Data Quality frameworks HadoopDeveloperatRaleigh,NC from Job Portal https://www.jobisite.com/extrJobView.htm?id=135297

[isearchgoood]

November 21, 2019 at 10:00PM - Big Data Mastery with Hadoop Bundle (89% discount) Ashraf

Big Data Mastery with Hadoop Bundle (89% discount) Hurry Offer Only Last For HoursSometime. Don't ever forget to share this post on Your Social media to be the first to tell your firends. This is not a fake stuff its real.

Big data is hot, and data management and analytics skills are your ticket to a fast-growing, lucrative career. This course will quickly teach you two technologies fundamental to big data: MapReduce and Hadoop. Learn and master the art of framing data analysis problems as MapReduce problems with over 10 hands-on examples. Write, analyze, and run real code along with the instructor– both on your own system, and in the cloud using Amazon’s Elastic MapReduce service. By course’s end, you’ll have a solid grasp of data management concepts.

Learn the concepts of MapReduce to analyze big sets of data w/ 56 lectures & 5.5 hours of content

Run MapReduce jobs quickly using Python & MRJob

Translate complex analysis problems into multi-stage MapReduce jobs

Scale up to larger data sets using Amazon’s Elastic MapReduce service

Understand how Hadoop distributes MapReduce across computing clusters

Complete projects to get hands-on experience: analyze social media data, movie ratings & more

Learn about other Hadoop technologies, like Hive, Pig & Spark

Hadoop is perhaps the most important big data framework in existence, used by major data-driven companies around the globe. Hadoop and its associated technologies allow companies to manage huge amounts of data and make business decisions based on analytics surrounding that data. This course will take you from big data zero to hero, teaching you how to build Hadoop solutions that will solve real world problems – and qualify you for many high-paying jobs.

Access 43 lectures & 10 hours of content 24/7

Learn how technologies like Mapreduce apply to clustering problems

Parse a Twitter stream Python, extract keywords w/ Apache Pig, visualize data w/ NodeJS, & more

Set up a Kafka stream w/ Java code for producers & consumers

Explore real-world applications by building a relational schema for a health care data dictionary used by the US Department of Veterans Affairs

Log collections & analytics w/ the Hadoop distributed file system using Apache Flume & Apache HCatalog

Have you ever wondered how major companies, universities, and organizations manage and process all the data they’ve collected over time? Well, the answer is Big Data, and people who can work with it are in huge demand. In this course you’ll cover the MapReduce algorithm and its most popular implementation, Apache Hadoop. Throughout this comprehensive course, you’ll learn essential Big Data terminology, MapReduce concepts, advanced Hadoop development, and gain a complete understanding of the Hadoop ecosystem so you can become a big time IT professional.

Access 76 lectures & 15.5 hours of content 24/7

Learn how to setup Node Hadoop pseudo clusters

Understand & work w/ the architecture of clusters

Run multi-node clusters on Amazon’s Elastic Map Reduce (EMR)

Master distributed file systems & operations including running Hadoop on HortonWorks Sandbok & Cloudera

Use MapReduce w/ Hive & Pig

Discover data mining & filtering

Learn the differences between Hadoop Distributed File System vs. Google File System

Hadoop is one of the most commonly used Big Data frameworks, supporting the processing of large data sets in a distributed computing environment. This tool is becoming more and more essential to big business as the world becomes more data-driven. In this introduction, you’ll cover the individual components of Hadoop in detail and get a higher level picture of how they interact with one another. It’s an excellent first step towards mastering Big Data processes.

Access 30 lectures & 5 hours of content 24/7

Install Hadoop in Standalone, Pseudo-Distributed, & Fully Distributed mode

Set up a Hadoop cluster using Linux VMs

Build a cloud Hadoop cluster on AWS w/ Cloudera Manager

Understand HDFS, MapReduce, & YARN & their interactions

Take your Hadoop skills to a whole new level by exploring its features for controlling and customizing MapReduce to a very granular level. Covering advanced topics like building inverted indexes for search engines, generating bigrams, combining multiple jobs, and much more, this course will push your skills towards a professional level.

Access 24 lectures & 4.5 hours of content 24/7

Cover advanced MapReduce topics like mapper, reducer, sort/merge, partitioning, & more

Use MapReduce to build an inverted index for search engines & generate bigrams from text

Chain multiple MapReduce jobs together

Write your own customized partitioner

Sort a large amount of data by sampling input files

Analyzing data is an essential to making informed business decisions, and most data analysts use SQL queries to get the answers they’re looking for. In this course, you’ll learn how to map constructs in SQL to corresponding design patterns for MapReduce jobs, allowing you to understand how these two programs can be leveraged together to simplify data problems.

Access 49 lectures & 1.5 hours of content 24/7

Master the art of “thinking parallel” to break tasks into MapReduce transformations

Use Hadoop & MapReduce to implement a SQL query like operations

Work through SQL constructs such as select, where, group by, & more w/ their corresponding MapReduce jobs in Hadoop

You see recommendation algorithms all the time, whether you realize it or not. Whether it’s Amazon recommending a product, Facebook recommending a friend, Netflix, a new TV show, recommendation systems are a big part of internet life. This is done by collaborative filtering, something you can perform through MapReduce with data collected in Hadoop. In this course, you’ll learn how to do it.

Access 4 lectures & 1 hour of content 24/7

Master the art of “thinking parallel” to break tasks into MapReduce transformations

Use Hadoop & MapReduce to implement a recommendations algorithm

Recommend friends on a social networking site using a MapReduce collaborative filtering algorithm

Data, especially in enterprise, will often expand at a rapid scale. Hadoop excels at compiling and organizing this data, however, to do anything meaningful with it, you may need to run machine learning algorithms to decipher patterns. In this course, you’ll learn one such algorithm, the K-Means clustering algorithm, and how to use MapReduce to implement it in Hadoop.

Access 7 lectures & 1.5 hours of content 24/7

Master the art of “thinking parallel” to break tasks into MapReduce transformations

Use Hadoop & MapReduce to implement the K-Means clustering algorithm

Convert algorithms into MapReduce patterns

from Active Sales – SharewareOnSale https://ift.tt/2iKO0kW https://ift.tt/eA8V8J via Blogger https://ift.tt/35s4kxY #blogger #bloggingtips #bloggerlife #bloggersgetsocial #ontheblog #writersofinstagram #writingprompt #instapoetry #writerscommunity #writersofig #writersblock #writerlife #writtenword #instawriters #spilledink #wordgasm #creativewriting #poetsofinstagram #blackoutpoetry #poetsofig

[netxtract]

IDG Contributor Network: The siren song of Hadoop

Hadoop seems incredibly well-suited to shouldering machine-learning workloads. With HDFS you can store both structured and unstructured data across a cluster of machines, and SQL-on-Hadoop technologies like Hive make those structured data look like database tables. Execution frameworks like Spark let you distribute compute across the cluster as well. On paper, Hadoop is the perfect environment for running compute-intensive distributed machine learning algorithms across a vast amount of data.

Unfortunately, though, Hadoop seems incredibly well-suited for a lot of other things too. Streaming data? Storm and Flink! Security? Kerberos, Sentry, Ranger, and Knox! Data movement and message queues? Flume, Sqoop, and Kafka! SQL? Hive, Impala and Hawq! The Hadoop ecosystem has become a bag of often overlapping and competing technologies. Cloudera vs. Hortonworks vs. MapR is responsible for some of this, as is the dynamism of the open source community.

To read this article in full or to leave a comment, please click here

from Computerworld http://www.computerworld.com/article/3196509/data-analytics/the-siren-song-of-hadoop.html#tk.rss_all