#datastore technology

[chrl21]

Objects in the Clouds - the flexible datastore technology

Many methods have been created to store our data. In our digital world, there are also plenty of sophisticated technologies to store our digital data. Among those, one technology that inspires and interests me -  as a computer engineering student - is the Cloud Object Storage technology.

----------Cloud Object Storage Technology----------

Object storage is a datastore architecture designed to handle large amounts of unstructured data. Unlike file storage architecture, which stores data into hierarchies with directories and folders, object storage architecture divides data into distinct units (objects) and gives each object a unique identifier. This makes it easier to find any desired data from databases which can get enormous. These objects are usually stored in the clouds, and thus called cloud object storage. Object storage is good for its virtually unlimited scalability and its lower cost.

----------The Way it is Special to Me----------

Cloud storage holds a special place in my digital life, primarily due to the conveniences it offers. Its ability to seamlessly bridge the gap between my many devices has transformed the way I interact with my documents, photos, and videos. The days of laboriously transferring files from one device to another are gone; the cloud enables me to access my data effortlessly, regardless of the gadget I'm using. Its flexibility mirrors the real world, where I can retrieve any file as if it were right at my fingertips. This adaptable nature not only streamlines my daily routine but also gives me a sense of control akin to managing tangible objects. Furthermore, the cloud has liberated me from the burden of carrying my computer around or relying on memory sticks. It's as if my data follows me wherever I go, allowing me to focus on the task at hand rather than the logistics of data storage. Cloud storage has truly changed the way I manage and interact with my digital assets, and its impact on my life is nothing short of remarkable.

[govindhtech]

CloudTrail Lake Features For Cloud Visibility And Inquiries

Enhancing your cloud visibility and investigations with new features added to AWS CloudTrail Lake

Updates to AWS CloudTrail Lake, a managed data lake that may be used for auditing, security investigations, and operational issues. It allows you to aggregate, store, and query events that are recorded by AWS CloudTrail in an immutable manner.

The most recent CloudTrail Lake upgrades are:

Improved CloudTrail event filtering options

Sharing event data stores across accounts

The creation of natural language queries driven by generative AI is generally available.

AI-powered preview feature for summarizing query results

Comprehensive dashboard features include a suite of 14 pre-built dashboards for different use cases, the option to construct custom dashboards with scheduled refreshes, and a high-level overview dashboard with AI-powered insights (AI-powered insights is under preview).

Let’s examine each of the new features individually.

Improved possibilities for filtering CloudTrail events that are ingested into event data stores

With improved event filtering options, you have more control over which CloudTrail events are ingested into your event data stores. By giving you more control over your AWS activity data, these improved filtering options increase the effectiveness and accuracy of security, compliance, and operational investigations. Additionally, by ingesting just the most pertinent event data into your CloudTrail Lake event data stores, the new filtering options assist you in lowering the costs associated with your analytical workflow.

Both management and data events can be filtered using properties like sessionCredentialFromConsole, userIdentity.arn, eventSource, eventType, and eventName.

Sharing event data stores across accounts

Event data repositories have a cross-account sharing option that can be used to improve teamwork in analysis. Resource-Based Policies (RBP) allow it to securely share event data stores with specific AWS principals. Within the same AWS Region in which they were formed, this feature enables authorized organizations to query shared event data stores.

CloudTrail Lake’s generative AI-powered natural language query generation is now widely accessible

AWS revealed this feature in preview form for CloudTrail Lake in June. With this launch, you may browse and analyze AWS activity logs (only management, data, and network activity events) without requiring technical SQL knowledge by creating SQL queries using natural language inquiries. The tool turns natural language searches into ready-to-use SQL queries that you can execute in the CloudTrail Lake UI using generative AI. This makes exploring event data warehouses easier and retrieving information on error counts, the most popular services, and the reasons behind problems. This capability is now available via the AWS Command Line Interface (AWS CLI) for users who prefer command-line operations, offering them even more flexibility.

Preview of the CloudTrail Lake generative AI-powered query result summarizing feature

To further streamline the process of examining AWS account activities, AWS is launching a new AI-powered query results summary function in preview, which builds on the ability to generate queries in natural language. This feature minimizes the time and effort needed to comprehend the information by automatically summarizing the main points of your query results in natural language, allowing you to quickly extract insightful information from your AWS activity logs (only management, data, and network activity events).

Extensive dashboard functionalities

CloudTrail Lake’s new dashboard features, which will improve visibility and analysis throughout your AWS deployments.

The first is a Highlights dashboard that gives you a concise overview of the data events saved in event data stores and the data collected in your CloudTrail Lake management. Important facts, such the most frequent failed API calls, patterns in unsuccessful login attempts, and spikes in resource creation, are easier to swiftly find and comprehend using this dashboard. It highlights any odd patterns or anomalies in the data.

Currently accessible

AWS CloudTrail Lake’s new features mark a significant step forward in offering a complete audit logging and analysis solution. These improvements help with more proactive monitoring and quicker incident resolution across your entire AWS environments by enabling deeper understanding and quicker investigation.

CloudTrail Lake in the US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), and Europe (London) AWS Regions is now offering generative AI-powered natural language query creation.

Previews of the CloudTrail Lake generative AI-powered query results summary feature are available in the Asia Pacific (Tokyo), US East (N. Virginia), and US West (Oregon) regions.

With the exception of the generative AI-powered summarization feature on the Highlights dashboard, which is only available in the US East (N. Virginia), US West (Oregon), and Asia Pacific (Tokyo) Regions, all regions where CloudTrail Lake is available have improved filtering options and cross-account sharing of event data stores and dashboards.

CloudTrail Lake pricing

CloudTrail Lake query fees will apply when you run queries. See AWS CloudTrail price for further information.

Read more on govindhtech.com

[fromdevcom]

The past 15 years have witnessed a massive change in the nature and complexity of web applications. At the same time, the data management tools for these web applications have undergone a similar change. In the current web world, it is all about cloud computing, big data and extensive users who need a scalable data management system. One of the common problems experienced by every large data web application is to manage big data efficiently. The traditional RDBM databases are insufficient in handling Big Data. On the contrary, NoSQL database is best known for handling web applications that involve Big Data. All the major websites including Google, Facebook and Yahoo use NoSQL for data management. Big Data companies like Netflix are using Cassandra (NoSQL database) for storing critical member data and other relevant information (95%). NoSQL databases are becoming popular among IT companies and one can expect questions related to NoSQL in a job interview. Here are some excellent books to learn more about NoSQL. Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement (By: Eric Redmond and Jim R. Wilson ) This book does what it is meant for and it gives basic information about seven different databases. These databases include Redis, CouchDB, HBase, Postgres, Neo4J, MongoDB and Riak. You will learn about the supporting technologies relevant to all of these databases. It explains the best use of every single database and you can choose an appropriate database according to the project. If you are looking for a database specific book, this might not be the right option for you. NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence (By: Pramod J. Sadalage and Martin Fowler ) It offers a hands-on guide for NoSQL databases and can help you start creating applications with NoSQL database. The authors have explained four different types of databases including document based, graph based, key-value based and column value database. You will get an idea of the major differences among these databases and their individual benefits. The next part of the book explains different scalability problems encountered within an application. It is certainly the best book to understand the basics of NoSQL and makes a foundation for choosing other NoSQL oriented technologies. Professional NoSQL (By: Shashank Tiwari ) This book starts well with an explanation of the benefits of NoSQL in large data applications. You will start with the basics of NoSQL databases and understand the major difference among different types of databases. The author explains important characteristics of different databases and the best-use scenario for them. You can learn about different NoSQL queries and understand them well with examples of MongoDB, CouchDB, Redis, HBase, Google App Engine Datastore and Cassandra. This book is best to get started in NoSQL with extensive practical knowledge. Getting Started with NoSQL (By: Gaurav Vaish ) If you planning to step into NoSQL databases or preparing it for an interview, this is the perfect book for you. You learn the basic concepts of NoSQL and different products using these data management systems. This book gives a clear idea about the major differentiating features of NoSQL and SQL databases. In the next few chapters, you can understand different types of NoSQL storage types including document stores, graph databases, column databases, and key-value NoSQL databases. You will even come to know about the basic differences among NoSQL products such as Neo4J, Redis, Cassandra and MongoDB. Data Access for Highly-Scalable Solutions: Using SQL, NoSQL, and Polyglot Persistence (By: John Sharp, Douglas McMurtry, Andrew Oakley, Mani Subramanian, Hanzhong Zhang ) It is an advanced level book for programmers involved in web architecture development and deals with the practical problems in complex web applications. The best part of this book is that it describes different real-life

web development problems and helps you identify the best data management system for a particular problem. You will learn best practices to combine different data management systems and get maximum output from it. Moreover, you will understand the polyglot architecture and its necessity in web applications. The present web environment requires an individual to understand complex web applications and practices to handle Big Data. If you are planning to start high-end development and get into the world of NoSQL databases, it is best to choose one of these books and learn some practical concepts about web development. All of these books are full of practical information and can help you prepare for different job interviews concerning NoSQL databases. Make sure to do the practice section and implement these concepts for a better understanding.

[zoofsoftware]

WHAT IS A CLOUD-BASED POS SYSTEM?

A Cloud-based POS system is a point of sale system hosted in the cloud. It allows businesses to access their sales data from any device with an internet connection, reducing the need for expensive hardware. This also enables businesses to enjoy increased flexibility, scalability, and reliability.

[naidilenatesh-blog]

AI Builder for Power Platform: At Microsoft Business Application Summit in 2019, Microsoft announced Artificial Intelligence investments in the Power Platform. The Power Platform is a low code platform that enables organizations to analyze data, act on it through applications, and automate business processes. It allows everyone, from the professional developer to the frontline worker, to participate in driving better business outcomes by building apps.

It will be available for consumption on data that already exists in the Common Data Service (CDS), the enterprise-grade datastore included in the Power Platform. AI Builder is the platform for providing a low-code user experience for every developer to create and customize their PowerApps and Flows.

You can access AI Builder from the navigation pane within the PowerApps Studio or the Microsoft Flow website. The simple wizard like experience is tailored to empower every developer in keeping with the essence of the Power Platform. You can access all your AI models in a single pane under the AI Builder ‘Models’ tab, providing key information about each model upfront in a tabular format. Clicking a particular model takes you into its details page, where other key information and actions are made available to the user. You can perform actions like publish and test the model, view the model efficacy and weights of data contributing to it or view other recommended actions from the details page.

Below are the key capabilities of AI Builder that we are announcing today:

Binary Classification

Binary Classification uses historical data to predict whether new data falls into one of two categories. AI Builder binary classification is an AI model that predicts yes/no business outcomes by learning to associate historical data patterns with historical outcomes. Based on those results, the binary classification model detects learned patterns in new data to predict future outcomes. Use the binary classification AI model to explore any business question that is answered as one of two available options, such as yes/no, true/false, pass/fail, and go/no go.

Text Classification

Text Classification tags any snippet of text based on the historical data you provide. Streamline your business by automatically tagging new text. Text classification is one of the fundamental Natural Language Processing (NLP) problems. It allows tagging of text entries with tags or labels which can be used for sentiment analysis, spam detection and routing customer requests, just to name a few examples. Use AI Builder text classification with Microsoft Flow and PowerApps to automate and scale your business processes, and free your employees to act on these insights. It can also be used as an input for other AI capabilities such as subscription user churn and predictive analysis. AI Builder can learn from your previously labeled text items, and enable you to classify unstructured text data stored in Common Data Service into your own business-specific categories.

Object Detection

Object Detection lets you count, locate, and identify selected objects within any image. You can use this model in PowerApps to extract information from pictures you take with the camera. Object detection can be used to expedite or automate business processes in multiple industries. In the retail industry, it can be used to simplify the inventory management, allowing retail leaders to focus on on-site customer relationship building. In the manufacturing industry, technicians can use it to speed up the repair process by looking up the manual for a piece of machinery by taking a picture, even if the UPC/serial number is not visible.

Business Card Reader

Business Card Reader is a component available in the PowerApps studio that lets you scan business cards. You can use this control to extract contact information from pictures of business cards or your mobile phones camera.

Form Processing

Form Processing identifies the structure of your documents based on examples you provide to extract text from any matching form. Examples might include tax forms or invoices. Form processing allows you to create and consume models that use machine learning technology to identify and extract key-value pairs and table data from form documents. Train your model and define what information needs to be retrieved from your form documents. You only need five form documents to get started. You can get results quickly, accurately and tailored to your specific content without the need for a lot of manual intervention or extensive data science expertise.

With the above announcements, AI Builder makes available Cognitive and AI offerings from Microsoft to everyone by lowering the bar to entry and learning by providing a low code experience to create and consume AI in Business apps and Processes. There by providing a true no-cliffs opportunity with a grow up story to Azure ML and Cognitive services.

We’re delighted to continue to add value to the Power Platform and realize the vision of enabling the next wave of Citizen and Pro-developers to make intelligent business applications.

Customer Testimonial

G&J Pepsi, headquartered in Cincinnati, Ohio, has more than 1,600 employees focused on manufacturing, distributing, and marketing the full line of Pepsi-Cola products. The G&J Pepsi IT team used AI Builder and PowerApps to create the next generation of their Store Audit App, enabling better mobility and efficiency for field personnel whose job it is to assess product needs on the shelves in stores.

“With AI Builder, we were able to easily build an AI model to help automatically identify and track our products using the object detection model,” said Eric McKinney, Enterprise Business Systems Manager at G&J Pepsi. “For our field worker, it’s now as simple as taking a photo and let AI builder do the rest.”

[wehyus-furniture]

Why is laminated furniture so popular

What is laminate office furniture

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This may possibly trigger essential information reduction when an individual least want it. You then basically decide on which partition (or even partitions, if indeed they did not take up the very same space) you want, and this plan will rebuild it. Bigger computer screens with greater resolutions call for elevated space in workstations so that personnel are not forced to sit too close to the screen, putting a strain on their vision. It really is time to get rid of the clutter that's been overtaking that makeshift home office space in the corner of your area.

[pimcore]

What’s Master Data Management and Why it’s Essential for Supply Chain?

With the proliferation of devices and channels, effective data management has become inextricably linked to customer experience and business growth. Quality, storage, security, and dissemination of key data assets like product data, asset data, customer data, and location data play a critical role in achieving business goals. Ergo, strategizing data management and aligning it to your business aim is more important than ever before.

When it comes to common data types that organizations deal with, it’s mostly to do with data sets like reference data, transactional data, hierarchical data, and metadata. However, if we combine all this data that describes objects around which business is conducted, it’s called the ‘master data’. Gartner interprets it as the ‘consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise’.

Master data is the business-critical data about parties, places, and things. In the supply chain management (SCM), parties typically pertain to suppliers, manufacturers, warehouse managers, retailers, distributors, customers, etc.; places are all the locations where assets are stored including warehouses and stores; and things range from products, raw materials, domains, vehicles & vessels, assets, etc.

Master data is used throughout the organization under commonly agreed structures and is managed through enterprise-wide governance. It is not transactional in nature, does not change frequently, and is not specific to any geographic location, supply chain process, unit, or system.

Mastering the Master Data

Understanding the significance of master data solves only half the problem. How do you collate it? How do you classify and manage it? And most importantly, how do you administer its flow throughout your legacy system? That is where Master Data Management (MDM) comes into the picture. MDM is a systematic approach of data handling which has become a competitive advantage for companies that leverage from data-driven insights and analytics.

The significance of master data management (MDM) has amplified for organizations — making them recalibrate their data strategy and goals to future-proof their growth.

Gartner’s definition: “MDM is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets.”

Forrester’s definition: “MDM solutions provide the capabilities to create the unique and qualified reference of shared enterprise data, such as customer, product, supplier, employee, site, asset, and organizational data.”

MDM is as business-centric as it is IT-centric. It is a technologically driven discipline encompassing tools and processes. MDM maintains authority over master data, by creating a unified repository or a ‘single source of truth’. It aims to attain accuracy, consistency, and completeness of data throughout the enterprise and its ecosystem of business partners.

Data Consolidation + Data Governance + Data Standards + Data Quality = MDM

How MDM helps simplify Supply Chain Management

The sheer range and volume of data involved in SCM are huge. It can originate from online forms, ERPs, CRM, routing data from fleets, employee profiles, vendors, and so on. Adopting an MDM strategy and implementing MDM solutions in the supply chain results in the integration of all this data so that it stays uniform across domains and departments. It removes data silos, collects data records into a master file, maintains its quality and integrity, eliminates redundancies and duplicities, as well as standardizes, preserves and governs data.

For example, inconsistencies in product SKUs, order numbers, or customer data records can cause unthinkable complications that can escalate as the data flows through different departments of the supply chain. MDM helps mitigate such issues.

While the benefits of MDM solutions vary depending on the domain/function in which they are implemented – there is a unique value proposition for every department. The solutions create a data architecture, which is so thoroughly inter-referenced that any stakeholder in any department can utilize it. They can provide insights on customer types and behaviors for sales and marketing decisions as well as provide insights on logistics based on routing data. Here are some of the key benefits associated with MDM:

Centralized Data Architecture – everyone can access data from different customers and vendors in multiple locations. This particularly helps in tracking and routing assets from procurement to manufacturing to distributors.

Optimization and Efficiency – data consolidation reduces the chances of human error and inaccuracy. With better visibility and optimization of end-to-end data, supply chain operations become more efficient.

Customer Engagement – data integrated from CRM and other departments help gauge customer behaviors, as well as internal service capacity across the globe.

Cracking the Last Mile – data-driven insights help in realizing customer patterns and thereby cut costs of the traveling salesman.

Master Edits – information modified in a master repository gets reflected throughout sub-databases. For example – data modified by the manufacturer on the product ingredient list seamlessly gets renewed for the distributor/retailer.

Data Reliability – minimal chances of data mix-up or obsolete inputs in a spreadsheet with a cross-referenced, authentic datastore visible to everybody in the supply chain.

Backup – data damage or loss at any stage of the supply chain can easily be recovered with a centralized database or ‘golden record’.

Are You Being a Good Data Steward?

Undefined or loose data governance can allow inaccurate data percolating throughout the supply chain and can severely damage your business and rapport. There can be huge repercussions if such flaws persist for a long time. In such a case, MDM becomes more necessary than beneficial. You can gauge your data management loopholes by looking for-

Data complications due to duplicate/poor quality/redundant data between different entities in the supply chain

Botched up shipment/procurement/retail orders due to data inaccuracies

Delayed product launches

Customer service flooded with complaints of inconsistent or inaccurate product data

MDM initiatives must quickly be undertaken by individuals responsible for data governance, stewardship, and administration if any of the aforementioned criteria are present in your supply chain management.

Effective MDM drives efficient SCM

Exponential data growth is a fundamental challenge that overwhelms most businesses today. The issue escalates proportionally with the number of entities or nodes in the internal/external business environment. And that is why optimizing supply chain management (SCM), which in itself is a complex network, hugely depends on data management.

One must act quickly to take control of data growth, complexity, and chaos. To seize the full potential of digital, decision-makers of SCM must develop data strategies and incorporate data management discipline. It will also help leverage upcoming supply chain technologies like advanced analytics, automation, machine learning, IoT, and blockchain. SCM managers must act now to focus, simplify, and standardize data through an enterprise master data management (MDM) strategy.

[govindhtech]

Boost the development of AI apps with Cloud Modernization

Cloud Modernization

A new era of intelligent applications that can comprehend natural language, produce material that is human-like, and enhance human abilities has begun with the development of generative AI. But as businesses from all sectors start to see how AI may completely transform their operations, they frequently forget to update their on-premises application architecture, which is an essential first step.

Cloud migration

Cloud migration is significantly more advantageous than on-premises options if your company wants to use AI to improve customer experiences and spur growth. Numerous early adopters, including TomTom and H&R Block, have emphasized that their decision to start updating their app architecture on Azure was what prepared them for success in the AI era.

Further information to connect the dots was provided by a commissioned study by IDC titled “Exploring the Benefits of Cloud Migration and Cloud Modernization for the Development of Intelligent Applications,” which was based on interviews with 900 IT leaders globally regarding their experiences moving apps to the cloud. They’ll go over a few of the key points in this article.

Modernise or lag behind: The necessity of cloud migration driven by AI

Let’s say what is obvious: Artificial Intelligence is a potent technology that can write code, produce content, and even develop whole apps. The swift progress in generative AI technologies, such OpenAI’s GPT-4 has revolutionized the way businesses function and engage with their clientele.

However, generative AI models such as those that drive ChatGPT or image-generating software are voracious consumers of data. To achieve their disruptive potential, they need access to enormous datasets, flexible scaling, and immense computing resources. The computation and data needs of contemporary AI workloads are simply too much for on-premises legacy systems and compartmentalized data stores to handle.

Cloud Modernization systems, which are entirely managed by the provider, offer the reliable infrastructure and storage options required to handle AI workloads. Because of its nearly infinite scalability, apps can adapt to changing demand and continue to operate at a high level.

The main finding of the IDC survey was that businesses were mostly driven to move their applications to the cloud by a variety of benefits, such as enhanced security and privacy of data, easier integration of cloud-based services, and lower costs. Furthermore, companies can swiftly test, refine, and implement AI models because to the cloud’s intrinsic agility, which spurs innovation.

With its most recent version, the.NET framework is ready to use AI in cloud settings. Developers can use libraries like OpenAI, Qdrant, and Milvus as well as tools like the Semantic Kernel to include AI capabilities into their apps. Applications may be delivered to the cloud with excellent performance and scalability thanks to the integration with.

NET Aspire. H&R Block’s AI Tax Assistant, for instance, shows how companies may build scalable, AI-driven solutions to improve user experiences and operational efficiency. It was developed using.NET and Azure OpenAI. You may expedite development and boost the adoption of AI in all areas of your company operations by integrating. NET into your cloud migration plan.

Utilising cloud-optimized old on-premises programmes through migration and restructuring allows for the seamless scaling of computation, enormous data repositories, and AI services. This can help your business fully incorporate generative AI into all aspects of its data pipelines and intelligent systems, in addition to allowing it to develop generative AI apps.

Reach your AI goals faster in the cloud

The ambition of an organisation to use generative AI and the realisation of its full value through cloud migration are strongly correlated, according to a recent IDC study. Let’s dissect a few important factors:

Data accessibility: Consolidating and accessing data from several sources is made easier by cloud environments, giving AI models the knowledge they require for training and improvement.

Computational power: Elastic computing resources in the cloud may be flexibly distributed to fulfil complicated AI algorithm needs, resulting in optimal performance and cost effectiveness.

Collaboration: Data scientists, developers, and business stakeholders may work together more easily thanks to cloud-based tools, which speeds up the creation and application of AI.

Cloud migration speeds up innovation overall in addition to enabling generative AI. Cloud platforms offer an abundance of ready-to-use services, such as serverless computing, machine learning, and the Internet of Things, that enable businesses to quickly develop and integrate new intelligent features into their apps.

Adopt cloud-based AI to beat the competition

Gaining a competitive edge is the driving force behind the urgent need to migrate and modernise applications it’s not simply about keeping up with the times. Companies who use AI and the cloud are better positioned to:

Draw in elite talent Companies with state-of-the-art tech stacks attract the best data scientists and developers.

Adjust to shifting circumstances: Because of the cloud’s elasticity, organisations may quickly adapt to changing client wants or market conditions.

Accelerate the increase of revenue: Applications driven by AI have the potential to provide new revenue streams and improve consumer satisfaction.

Embrace AI-powered creativity by updating your cloud

Cloud migration needs to be more than just moving and lifting apps if it is to stay competitive. The key to unlocking new levels of agility, scalability, and innovation in applications is Cloud Modernization through rearchitecting and optimizing them for the cloud. Your company can: by updating to cloud-native architectures, your apps can:

Boost performance: Incorporate intelligent automation, chatbots, and personalised recommendations all enabled by AI into your current applications.

Boost output: To maximise the scalability, responsiveness, and speed of your applications, take advantage of cloud-native technology.

Cut expenses: By only paying for the resources you use, you can do away with the expensive on-premises infrastructure.

According to the IDC poll, most respondents decided to move their apps to the Cloud Modernization because it allowed them to develop innovative applications and quickly realize a variety of business benefits.

Boost the development of your intelligent apps with a cloud-powered AI

In the age of generative AI, moving and updating apps to the cloud is not a choice, but a requirement. Businesses that jump on this change quickly will be in a good position to take advantage of intelligent apps’ full potential, which will spur innovation, operational effectiveness, and consumer engagement.

The combination of generative AI and cloud computing is giving organizations previously unheard-of options to rethink their approaches and achieve steady growth in a cutthroat market.

Businesses may make well-informed decisions on their cloud migration and Cloud Modernization journeys by appreciating the benefits and measuring the urgency, which will help them stay at the forefront of technical advancement and commercial relevance.

Read more on Govindhtech.com

[graysquirrel7]

While the humans were siloed away in their own warships, things were relatively straight forward. It was when the Federation decided to start an integration program that things got strange.

For starters, who knew that there was a xenopsychologist available for every berth at every major port? That was something new. As were the briefings on "making the most of your human liaison" and the handouts.

Soft, ape-like creatures with no real claws, pincers, mandibles, or vicious teeth to speak of, yet there were whole sections dedicated to warnings about boredom, hunger, and emotional cues. Strangely absent were specifications about weapons to not expose humans to. In terms of technology, that is. The physio data indicated that humans could be neutralized without much effort.

Which brings up what was strangely present in the briefing materials. Footnotes on literally every observation. Some footnotes indicated contradictions, others provided references to more details.

It turns out "without much effort" had a whole string of footnote markings. Yes, a human could be neutralized by another human without any weapons of any kind. Yet the contradictions and expansions went well beyond the pale.

At some point in the labyrinthian datastore, a quote from a story associated with what humans referred to as science fiction by an author named Saberhagen came up, damaging any sense of curiosity I had in learning more about them. The translator did its best: "The best weapon to deploy against an earth-descendant human is another earth-descendant human."

That was their reputation now, absolutely. Yet this story dated back to when these soft creatures were still contained on a planet they called Earth. I wound back the spooling information to get back to present time. I needed something useful to know about who the Federation would be assigning to my crew.

Preferably not cautionary tales about contradictory creatures. The stories were fearsome enough. I had suspected it was clever information operations being run by human sympathizers in the Federation, but those only really work when there's a kernel of truth to them.

The feed returned to the first strange thing I had noted: a lack of guidance on technologies to not expose humans to. This struck me as strange, because their ships were often ugly, brutal slabs, wedges, and spikes of titanium, lead, and steel, with bombardment primary weapons and a host of secondary and tertiary armament, but all of it was crude ballistic technology from what I gathered. Surely such a violent species is best kept away from...

Oh.

The best word I can find to summarize the feeling that hit my nervous system when I followed the single footnote reference on technology concerns was the human word: fuck.

"Don't bother concealing your technology from the human liaison. If any single one of them witnesses or hears about it, they will talk about it with other humans. The humans will figure out how it works from description of its effects alone, integrate that information into their own design languages, and then return to appearing to be simple creatures with a penchant for brutal violence. See attached known weapons profiles for human warfare upon their own kind."

Well.

Now I was beginning to understand why I've never seen actual footage of these creatures fighting. They didn't need natural claws, fangs, etc., to fill an apex predator role. They came at you with fists and stones, and if you used a stick, they brought a stone lashed to a stick, or figured out a way to make a sharp stick move fast enough to hit you from across a clearing.

They parked these dreadnaught-style warships in front of anyone as a dare to throw something at it. The only thing you could count on is that they would find some inventive modification and throw it back. A linked reference on human cooperation blinked at me, daring me to face the terror gnawing at my mind. This relatively small section also included cross references to emotional cues, boredom, and hunger.

Does this mean that they invent better weapons for killing even themselves out of boredom? Why the fuck is the Federation putting them on every Federation ship?!

The data reader must have noted my agitated state, as it refreshed to a screen that read: "please stand by, the xenopsychologist is on their way to provide assistance."

Humans are the proverbial “Sleeping Giant,” and thus make remarkably good deterrents. A common tactic of the Galactic Federation is to simply call in a human warship, such as the USS “Fuck Around and, FindOut,” and simply let it sit nearby. Peace Talks happen within the week.

[padmah2k121]

Kubernetes Training  from h2kinfosys

About kubernetes training course

Kubernetes is a portable, extensible open-source platform for managing containerized workloads and services that facilitates both declarative configuration and automation. It has a large, rapidly growing ecosystem. Google open-sourced the Kubernetes project in 2014. Kubernetes builds upon a decade and a half of experience that Google has with running production workloads at scale, combined with best-of-breed ideas.

  In our kubernetes Training you will learn:

Various     components of k8s cluster on AWS cloud using ubuntu 18.04 linux images.

Setting     up AWS cloud environment manually.

Installation     and setting up kubernetes cluster on AWS manually from scratch.

Installation     and Setting up etcd cluster ( key-value ) datastore

Provisioning     the CA and Generating TLS Certificates for k8s cluster and etcd server.

Installation     of Docker.

Configuring     and CNI plugins to wire docker containers for networking.

Creating     IAM roles for the kubernetes cloud setup.

Kubernetes     deployments, statefulsets, Network policy etc.

Why consider a kubernetes career path in IT industry?

 Kubernetes demand has exploded and its adoption is increasing many folds every quarter.

As more and more companies moving towards the automation and embracing open source technologies. Kubernetes slack-user has more 65,000 users and counting.

 Who is eligible for the kubernetes course?

 Beginner to intermediate level with elementary knowledge of Linux and docker.

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https://www.youtube.com/watch?v=Fa9JfWmqR2k

[alphawebrticles]

Big Data in Business: Examples & Applications

The technology known as Big Data is one of the most impactful innovations of the digital age. Patterns and correlations hidden in massive collections of data, revealed by powerful analytics, are informing planning and decision making across nearly every industry. In fact, within just the last decade, Big Data services and its usage has grown to the point where it touches nearly every aspect of our lifestyles, shopping habits, and routine consumer choices. In this blog, we will explore examples of how big data companies are being leveraged in business across different domains. Advertising and Marketing

Ads have always been targeted towards specific consumer segments. In the past, marketers have employed TV and radio preferences, survey responses, and focus groups to try to ascertain people’s likely responses to campaigns. At best, these methods amounted to educated guesswork.

Today, advertisers buy or gather huge quantities of data to identify what consumers actually click on, search for, and “like.” Marketing campaigns are also monitored for effectiveness using click-through rates, views, and other precise metrics.

For example, Amazon accumulates massive data stories on the purchases, delivery methods, and payment preferences of its millions of customers. The company then sells ad placements that can be highly targeted to very specific segments and subgroups.

Fraud Detection

Fraud is a significant concern for businesses across industries. Big data service provider offers powerful tools to detect and prevent fraudulent activities. By analyzing large volumes of data in real-time, organizations can identify suspicious patterns, detect anomalies, and flag potential fraud cases. While Big Data can expose businesses to a greater risk of cyberattacks, the same datastores can be used to prevent and counteract online crime through the power of machine learning and analytics. Historical data analysis can yield intelligence to create more effective threat controls. And machine learning can warn businesses when deviations from normal patterns and sequences occur, so that effective countermeasures can be taken against threats such as ransomware attacks, malicious insider programs, and attempts at unauthorized access.

After a company has suffered an intrusion or data theft, post-attack analysis can uncover the methods used, and machine learning can then be deployed to devise safeguards that will foil similar attempts in the future.

Risk Management

Big data analytics has transformed the way businesses approach risk management. By analyzing vast amounts of data from internal and external sources, organizations can identify potential risks, assess their impact, and develop effective risk mitigation strategies. This data-driven approach enables businesses to make informed decisions, enhance operational resilience, and minimize the impact of unforeseen events.

Media and Entertainment

The entertainment industry harnesses Big Data to glean insights from customer reviews, predict audience interests and preferences, optimize programming schedules, and target marketing campaigns.

Two conspicuous examples are Amazon Prime, which uses Big Data analytics to recommend programming for individual users, and Spotify, which does the same to offer personalized music suggestions.

Healthcare Analytics

Big data services has enormous potential in the healthcare industry. By analyzing patient data, medical records, and clinical trials, healthcare providers can improve diagnosis accuracy, personalize treatment plans, and enhance patient outcomes. Furthermore, big data analytics can help identify disease patterns, predict epidemics, and support public health initiatives.

Energy Management

Big data analytics is playing a crucial role in optimizing energy consumption and improving sustainability. By analyzing energy usage data, businesses can identify inefficiencies, optimize resource allocation, and reduce energy waste. This not only helps in lowering operational costs but also contributes to environmental conservation.

The examples mentioned above provide a glimpse into the vast potential of big data in business. By leveraging the power of big data companies, businesses can gain valuable insights, make informed decisions, and drive innovation across various domains. However, it is essential to recognize the big data experts like Alpha Data, big data service provider in UAE, if you are looking for top  big data service provider in Dubai who have robust infrastructure, skilled professionals, and data privacy considerations. As technology continues to evolve, the role of big data in shaping the future of business will undoubtedly grow, offering endless possibilities for those who can effectively harness its potential.

[functionup]

The Most Popular Big Data Frameworks in 2023

Big data is the vast amount of information produced by digital devices, social media platforms, and various other internet-based sources that are part of our daily lives. Utilizing the latest techniques and technology, huge data can be used to find subtle patterns, trends, and connections to help improve processing, make better decisions, and predict the future, ultimately improving the quality of life of people, companies, and society all around.

As more and more data is generated and analyzed, it is becoming increasingly hard for researchers and companies to get insights into their data quickly. Therefore, Big Data frameworks are becoming ever more crucial. In this piece, we’ll examine the most well-known big data frameworks- Apache Storm, Apache Spark, Presto, and others – which are increasingly sought-after for Big Data analytics.

What are Big Data Frameworks?

Big data frameworks are a set of tools that make it simpler to handle large amounts of information. Big data framework is made to handle extensive data efficiently and quickly, and be safe. The frameworks that deal with big data are generally open source are big data frameworks. This means they’re available for free, with the possibility of obtaining the support you require.

Big Data is about collecting, processing, and analyzing Exabytes of data and petabyte-sized sets. Big Data concerns the amount of data, the speed, and the variety of data. Big Data is about the capability to analyze and process data at speeds and in a way that was impossible before that.

 Hadoop

Apache Hadoop is an open-source big data framework that can store and process huge quantities of data. Written in Java and is suitable to process streams, batch processing, and real-time analytics.

Apache Hadoop is home to several programs that allow you to deal with huge amounts of data within just one computer or multiple machines via networks in an approach that the programs don’t know they’re distributed over multiple computers.

One of the major strengths of Hadoop is its ability to manage huge volumes of information. Based upon a distributed computing model, Hadoop breaks down large data sets into smaller pieces processed by a parallel process across a set of nodes. This method helps achieve the highest level of fault tolerance and faster processing speed, making it the ideal choice for managing Big Data workloads.

 Spark

Apache Spark can be described as a powerful and universal engine to process large amounts of data. It has high-level APIs in Java, Scala, and Python, as well as R (a statically-oriented programming language), and, therefore, developers of any level can utilize the APIs. Spark is commonly utilized in production environments for processing data from several sources, such as HDFS (Hadoop Distributed File System) as well as another system for file storage, Cassandra database, Amazon S3 storage service (which also provides web services for the storage of data over the Internet) in addition to as web services that are external to the Internet including Google’s Datastore.

The main benefit of Spark is the capacity to process information at a phenomenal speed which is made possible through its features for processing in memory. It significantly cuts down on I/O processing, making it ideal for extensive data analyses. Furthermore, Spark offers considerable flexibility in allowing for a wide range of operations in data processing, like streaming, batch processing, and graph processing, using its integrated libraries.

 Hive

Apache Hive is an open-source big data framework software allowing users to access and modify large data sets. It’s a big data framework built upon Hadoop, which allows users to create SQL queries and use different languages such as HiveQL and Pig Latin (a scripting language ). Apache Hive is part of the Hadoop ecosystem. You require an installation of Apache Hadoop before installing Hive.

Apache Hive’s advantage is managing petabytes of data effectively by using Hadoop Distributed File System (HDFS) to store data and Apache Tez or MapReduce for processing.

 Elasticsearch

Elasticsearch is a fully-managed open-source, distributed column-oriented analytics and big data framework. Elasticsearch is used for search (elastic search), real-time analytics (Kibana), log storage/analytics/visualization (Logstash), centralized server logging aggregation (Logstash Winlogbeat), and data indexing.

Elasticsearch consulting may be utilized to analyze large amounts of data as it’s highly scalable and resilient and has an open architecture that allows using more than one node on various servers or possibly cloud servers. It has an HTTP interface that includes JSON support, allowing easy integration with other apps using common APIs, such as RESTful calls and Java Spring Data JPA annotations for domain classes.

MongoDB

MongoDB is a NoSQL database. It holds data in JSON-like formats, so there’s no requirement to establish schemas before creating your app. MongoDB is a free-of-cost open source available for on-premises use and as a cloud-based solution (MongoDB Atlas ).

MongoDB as a big data framework can serve numerous purposes: from logs to analysis and from ETL to machine learning (ML). The database can hold millions of documents and not worry about performance issues due to its horizontal scaling mechanism and efficient management of memory. Additionally, it is easy for developers of software who wish to concentrate on developing their apps instead of having to think about designing data models and tuning the systems behind them; MongoDB offers high availability using replica sets, a cluster model that lets multiple nodes duplicate their data automatically, or manually establishing clusters that have auto failover when one fails.

MapReduce

MapReduce is a big data framework that can process large data sets within a group. It was built to be fault-tolerant and spread the workload across the machines.

MapReduce is an application that is batch-oriented. This means it can process massive quantities of data and produce results within a relatively short duration.

MapReduce’s main strength is its capacity to divide massive data processing tasks over several nodes, which allows it to run parallel tasks and dramatically improves efficiency.

Samza

Samza is the name of a big data framework for stream processing. It utilizes Apache Kafka as the underlying messages bus and data store and is run on YARN. The Samza development is run by Apache, which means that it’s freely available and open to download, make use of, modify, and distribute in accordance with the Apache License version 2.0.

An example of how this is implemented in real life is how a user looking to handle a stream of messages could write the application in any programming software they want to use (Java or Python is currently supported). The application runs in a container located on at least one worker node, which is part of the Samza-Samza cluster. They form an internal pipeline that processes all messages coming from Kafka areas in conjunction with similar pipelines. Every message is received by the workers responsible for processing it before it is sent out to Kafka, another location in the system, or out of it, if needed, to accommodate the growing demands.

 Flink

Flink is an another big data framework for processing data streams. It’s also a hybrid big-data processor. Flink can perform real-time analysis ETL, batch, or real-time processing.

Flink’s architecture is designed for stream processing and interactive queries for large data sets. Flink allows events and processing metadata for data streams, allowing it to manage real-time analytics and historical analysis on the same cluster using the identical API. Flink is especially well-suited to applications that require real-time data processing, like financial transactions, anomaly detection, and applications based on events that are part of IoT ecosystems. Additionally, its machine-learning and graph processing capabilities make Flink a flexible option for decision-making based on data within various sectors.

Heron

Heron is an another big data framework for distributed stream processing that is utilized to process real-time data. It can be utilized to build low-latency applications such as microservices and IoT devices. Heron can be written using C++. It offers a high-level programming big data framework to write streams processing software distributed across Apache YARN, Apache Mesos, and Kubernetes in a tightly integrated way to Kafka or Flume for the communication layer.

Heron’s greatest strength lies in its ability to offer the highest level of fault tolerance and excellent performance for large-scale data processing. The software is developed to surpass the weaknesses of Apache Storm, its predecessor Apache Storm, by introducing an entirely new scheduling model and a backpressure system. This allows Heron to ensure high performance and low latency. This makes Heron ideal for companies working with huge data collections.

 Kudu

Kudu is a columnar data storage engine designed for the analysis of work. Kudu is the newest youngster on the block, yet it’s already taking the hearts of data scientists and developers. Data scientists, thanks to their capacity to combine the best features of relational databases and NoSQL databases in one.

Kudu is a also a big data framework combining relational databases (strict ACID compliance) advantages with NoSQL databases (scalability and speed). Additionally, it comes with several benefits. It comes with native support for streaming analytics. This means you can use your SQL abilities to analyze stream data in real time. It also supports JSON data storage and columnar storage for improved performance of queries by keeping related data values.

Conclusion

The emerging field of Big Data is a sector of research that takes the concept of large information sets and combines the data using hardware-based architectures of super-fast parallel processors, storage software and hardware APIs, and open-source software stacks. It’s a thrilling moment to become an expert in data science. It’s not just that greater tools are available than before within the Big Data ecosystem. Still, they’re also becoming stronger, more user-friendly to work with, and more affordable to manage. That means companies will gain more value from their data and not have to shell out as much for infrastructure.

FunctionUp’s data science online course is exhaustive and a door to take a big leap in mastering data science. The skills and learning by working on multiple real-time projects will simulate and examine your knowledge and will set your way ahead.

Learn more-

Do you know how data science can be used in business to increase efficiency? Read now.

[aditioffpage1]

A Comprehensive Overview of the Tools Used in Modern Data Science

Have you ever wondered what a comprehensive syllabus of a data science course would look like? Data science is an ever-evolving field with various tools and technologies available for analysis. 

We will provide an overview of the tools used in modern data science and their related usage in the industry.

To start off, having a strong knowledge base about programming languages such as Python and R is essential for most data scientists. These programming languages are used for statistical analysis and other data manipulation tasks. You’ll need to be familiar with object-oriented concept, primitive data types, functions, operators, control flow statements and classes to effectively work with Python and R. Additionally, both languages have extensive libraries that can be utilized to perform different tasks while working with large datasets. 

Data scientists also rely heavily on machine learning algorithms. Knowledge of supervised learning algorithms such as linear regression, logistic regression and naïve bayes will enable you to apply predictive analytics in practical scenarios. Having a thorough understanding of unsupervised learning algorithms such as clustering and dimensionality reduction will help you create effective models from un-labelled datasets. 

Apart from the above-mentioned tools and technologies, having hands on experience with databases such as SQL and NoSQL are also important for data scientists. 

This allows them to understand how different kinds of datastores store information which can be used for further analysis using SQL queries or NoSQL commands. 

Additionally, being proficient in big data processing frameworks such as Hadoop will help you deploy sophisticated big data solutions using real-time streaming or batch processing techniques.  

In conclusion, these tools form the foundation of modern data science syllabus giving budding professionals an edge in this highly competitive field.

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Price: [price_with_discount] (as of [price_update_date] - Details) [ad_1] All Indian Reprints of O'Reilly are printed in Grayscale. Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures Publisher ‏ : ‎ Shroff/O'Reilly; First edition (1 January 2017); Shroff Publishers & Distributors Pvt. Ltd. B-103, First Floor, Railway Commercial Complex Sector 3, Sanpada (E), Navi Mumbai 400705 Tel: +91 22 4158 4158 Fax: +91 22 4158 4141 WhatsApp +91 73044 87700 email: info@shroffpublishers.com Language ‏ : ‎ English Paperback ‏ : ‎ 616 pages ISBN-10 ‏ : ‎ 9352135245 ISBN-13 ‏ : ‎ 978-9352135240 Reading age ‏ : ‎ Customer suggested age: 13 years and up Item Weight ‏ : ‎ 1 kg Dimensions ‏ : ‎ 24 x 18 x 1 cm Country of Origin ‏ : ‎ India Packer ‏ : ‎ Computer Bookshop (I) Pvt. Ltd. , Kitab Mahal Building, 190 Dr. D N Road, Fort , Mumbai - 400001 , Whatsapp - +91 9987380571, Email - info@cb-india.com, Website - www.cb-india.com Generic Name ‏ : ‎ Printed Book [ad_2]

[yahoodevelopers]

Open-Sourcing Panoptes, Oath’s distributed network telemetry collector

By Ian Flint, Network Automation Architect and Varun Varma, Senior Principal Engineer

The Oath network automation team is proud to announce that we are open-sourcing Panoptes, a distributed system for collecting, enriching and distributing network telemetry.  

We developed Panoptes to address several issues inherent in legacy polling systems, including overpolling due to multiple point solutions for metrics, a lack of data normalization, consistent data enrichment and integration with infrastructure discovery systems.  

Panoptes is a pluggable, distributed, high-performance data collection system which supports multiple polling formats, including SNMP and vendor-specific APIs. It is also extensible to support emerging streaming telemetry standards including gNMI.

Architecture

The following block diagram shows the major components of Panoptes:

Panoptes is written primarily in Python, and leverages multiple open-source technologies to provide the most value for the least development effort. At the center of Panoptes is a metrics bus implemented on Kafka. All data plane transactions flow across this bus; discovery publishes devices to the bus, polling publishes metrics to the bus, and numerous clients read the data off of the bus for additional processing and forwarding. This architecture enables easy data distribution and integration with other systems. For example, in preparing for open-source, we identified a need for a generally available time series datastore. We developed, tested and released a plugin to push metrics into InfluxDB in under a week. This flexibility allows Panoptes to evolve with industry standards.

Check scheduling is accomplished using Celery, a horizontally scalable, open-source scheduler utilizing a Redis data store. Celery’s scalable nature combined with Panoptes’ distributed nature yields excellent scalability. Across Oath, Panoptes currently runs hundreds of thousands of checks per second, and the infrastructure has been tested to more than one million checks per second.

Panoptes ships with a simple, CSV-based discovery system. Integrating Panoptes with a CMDB is as simple as writing an adapter to emit a CSV, and importing that CSV into Panoptes. From there, Panoptes will manage the task of scheduling polling for the desired devices. Users can also develop custom discovery plugins to integrate with their CMDB and other device inventory data sources.

Finally, any metrics gathering system needs a place to send the metrics. Panoptes’ initial release includes an integration with InfluxDB, an industry-standard time series store. Combined with Grafana and the InfluxData ecosystem, this gives teams the ability to quickly set up a fully-featured monitoring environment.

Deployment at Oath

At Oath, we anticipate significant benefits from building Panoptes. We will consolidate four siloed polling solutions into one, reducing overpolling and the associated risk of service interruption. As vendors move toward streaming telemetry, Panoptes’ flexible architecture will minimize the effort required to adopt these new protocols.

There is another, less obvious benefit to a system like Panoptes. As is the case with most large enterprises, a massive ecosystem of downstream applications has evolved around our existing polling solutions. Panoptes allows us to continue to populate legacy datastores without continuing to run the polling layers of those systems. This is because Panoptes’ data bus enables multiple metrics consumers, so we can send metrics to both current and legacy datastores.

At Oath, we have deployed Panoptes in a tiered, federated model. We install the software in each of our major data centers and proxy checks out to smaller installations such as edge sites.  All metrics are polled from an instance close to the devices, and metrics are forwarded to a centralized time series datastore. We have also developed numerous custom applications on the platform, including a load balancer monitor, a BGP session monitor, and a topology discovery application. The availability of a flexible, extensible platform has greatly reduced the cost of producing robust network data systems.

Easy Setup

Panoptes’ open-source release is packaged for easy deployment into any Linux-based environment. Deployment is straightforward, so you can have a working system up in hours, not days.

We are excited to share our internal polling solution and welcome engineers to contribute to the codebase, including contributing device adapters, metrics forwarders, discovery plugins, and any other relevant data consumers.  

Panoptes is available at https://github.com/yahoo/panoptes, and you can connect with our team at network-automation@oath.com.