Summary

With the proliferation of data sources to give a more comprehensive view of the information critical to your business it is even more important to have a canonical view of the entities that you care about. Is customer number 342 in your ERP the same as Bob Smith on Twitter? Using master data management to build a data catalog helps you answer these questions reliably and simplify the process of building your business intelligence reports. In this episode the head of product at Tamr, Mark Marinelli, discusses the challenges of building a master data set, why you should have one, and some of the techniques that modern platforms and systems provide for maintaining it.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Mark Marinelli about data mastering for modern platforms

Interview

Introduction How did you get involved in the area of data management? Can you start by establishing a definition of data mastering that we can work from?

How does the master data set get used within the overall analytical and processing systems of an organization?

What is the traditional workflow for creating a master data set?

What has changed in the current landscape of businesses and technology platforms that makes that approach impractical? What are the steps that an organization can take to evolve toward an agile approach to data mastering?

At what scale of company or project does it makes sense to start building a master data set? What are the limitations of using ML/AI to merge data sets? What are the limitations of a golden master data set in practice?

Are there particular formats of data or types of entities that pose a greater challenge when creating a canonical format for them? Are there specific problem domains that are more likely to benefit from a master data set?

Once a golden master has been established, how are changes to that information handled in practice? (e.g. versioning of the data) What storage mechanisms are typically used for managing a master data set?

Are there particular security, auditing, or access concerns that engineers should be considering when managing their golden master that goes beyond the rest of their data infrastructure? How do you manage latency issues when trying to reference the same entities from multiple disparate systems?

What have you found to be the most common stumbling blocks for a group that is implementing a master data platform?

What suggestions do you have to help prevent such a project from being derailed?

What resources do you recommend for someone looking to learn more about the theoretical and practical aspects of

Many organizations have begun to rethink the strategy of allowing regional teams to maintain independent databases that are periodically consolidated with the head office. As businesses extend their reach globally, these hierarchical approaches no longer work. Instead, an enterprise’s entire data infrastructure—including multiple types of data persistence—needs to be shared and updated everywhere at the same time with fine-grained control over who has access. This practical report examines the requirements and challenges of constructing a geo-distributed data platform, including examples of specific technologies designed to meet them. Authors Ted Dunning and Ellen Friedman also provide real-world use cases that show how low-latency geo-distribution of very large-scale data and computation provide a competitive edge. With this report, you’ll explore: How replication and mirroring methods for data movement provide the large scale, low latency, and low cost that systems demand The importance of multimaster replication of data streams and databases Advantages (and disadvantages) of cloud neutrality, cloud bursting, and hybrid cloud architecture for transferring data Why effective data governance is a complex process that requires the right tools for controlling and monitoring geo-distributed data How to make containers work for geo-distributed data at scale, even where stateful applications are involved Use cases that demonstrate how telecoms and online advertisers distribute large quantities of data

Develop applications for the big data landscape with Spark and Hadoop. This book also explains the role of Spark in developing scalable machine learning and analytics applications with Cloud technologies. Beginning Apache Spark 2 gives you an introduction to Apache Spark and shows you how to work with it. Along the way, you’ll discover resilient distributed datasets (RDDs); use Spark SQL for structured data; and learn stream processing and build real-time applications with Spark Structured Streaming. Furthermore, you’ll learn the fundamentals of Spark ML for machine learning and much more. After you read this book, you will have the fundamentals to become proficient in using Apache Spark and know when and how to apply it to your big data applications. What You Will Learn Understand Spark unified data processing platform Howto run Spark in Spark Shell or Databricks Use and manipulate RDDs Deal with structured data using Spark SQL through its operations and advanced functions Build real-time applications using Spark Structured Streaming Develop intelligent applications with the Spark Machine Learning library Who This Book Is For Programmers and developers active in big data, Hadoop, and Java but who are new to the Apache Spark platform.

"Expert GeoServer" guides readers through the process of building, optimizing, and securing GeoServer-powered web mapping applications. By exploring concepts like spatial analysis platforms, tile caching, and secure authentication, this book equips you to create highly performant and secure geospatial applications. What this Book will help me do Learn to develop spatial analysis platforms using web processing services. Master tile caching to significantly enhance the speed of your mapping applications. Implement secure authentication to protect sensitive geospatial data. Optimize GeoServer for improved performance and resource utilization. Deploy your GeoServer-backed applications on modern cloud-hosting infrastructures. Author(s) None Mearns is an experienced software developer and geospatial technology expert. With a strong background in GeoServer implementation, None has helped organizations optimize and secure their geospatial platforms. Their writing aims to provide clear and actionable instructions for professionals and learners alike. Who is it for? This book is perfect for geospatial developers and professionals aiming to take their GeoServer skills to the next level. A basic understanding of GeoServer is assumed, as this guide tackles advanced topics like performance optimization and security. If you are looking to enhance the speed, usability, and security of your mapping applications, this is for you. Those aiming to confidently deploy production-ready applications will find it invaluable.

Learn everything you need to manage Azure SQL Database with 'Professional Azure SQL Database Administration'. This book covers critical tasks such as migration, performance optimization, security, and disaster recovery. Perfect for those transitioning to the cloud, it equips you with skills to ensure your database runs smoothly and efficiently. What this Book will help me do Effectively migrate on-premise SQL Server databases to Azure. Master backup, restore, and security operations with Azure SQL Database. Optimize performance and scalability using monitoring and tuning techniques. Implement high availability and disaster recovery strategies. Simplify database management through automation and advanced techniques. Author(s) Ahmad Osama is a seasoned database admin and Azure expert with extensive experience in SQL Server and cloud database management. As a consultant and trainer, he has guided numerous organizations through cloud transitions. Ahmad's teaching philosophy blends practical insights with clear instruction. Who is it for? This book is intended for database administrators and developers looking to transition their skills to Azure SQL Database. If you have some experience with on-premise SQL Server and are familiar with PowerShell, you'll find this guide invaluable. Ideal for those wanting to develop, migrate, or manage Azure SQL solutions.

Uncover the power of Microsoft Power BI with this accessible and practical guide. This book introduces you to the concepts of data modeling, transformation, and visualization, ensuring that you can build effective dashboards and gain valuable insights. You'll be empowered to productively utilize Power BI in your organization to achieve your analytics goals. What this Book will help me do Connect to various data sources and harness the capabilities of the Query Editor. Transform and clean data for analysis, learning to use languages like M and R. Build robust data models with relationships and powerful DAX expressions. Create impactful reports with efficient and custom visualizations in Power BI. Deploy and administer Power BI solutions both in the cloud and on-premise. Author(s) The authors, Devin Knight, Mitchell Pearson, and Manuel Quintana, are seasoned experts in Business Intelligence and Power BI. They bring years of experience simplifying complex data challenges. Their writing is approachable and hands-on, equipping readers with the skills to solve real-world problems. Who is it for? This book is perfectly suited for professionals in Business Intelligence roles, data analysts, or those aiming to adopt Power BI solutions. Whether you're new to Power BI or have basic BI knowledge, this guide will take you from fundamentals to advanced implementations. Ideal for anyone aiming to unlock actionable insights from their data.

Today, new business models in the marketplace coexist with traditional ones and their well-established IT architectures. They generate new business needs and new IT requirements that can only be satisfied by new service models and new technological approaches. These changes are reshaping traditional IT concepts. Cloud in its three main variants (Public, Hybrid, and Private) represents the major and most viable answer to those IT requirements, and software-defined infrastructure (SDI) is its major technological enabler. IBM® technology, with its rich and complete set of storage hardware and software products, supports SDI both in an open standard framework and in other vendors' environments. IBM services are able to deliver solutions to the customers with their extensive knowledge of the topic and the experiences gained in partnership with clients. This IBM Redpaper™ publication focuses on software-defined storage (SDS) and IBM Storage Systems product offerings for software-defined environments (SDEs). It also provides use case examples across various industries that cover different client needs, proposed solutions, and results. This paper can help you to understand current organizational capabilities and challenges, and to identify specific business objectives to be achieved by implementing an SDS solution in your enterprise.

We revisit the 2018 Microsoft Build in this episode, focusing on the latest ideas in DevOps. Kyle interviews Cloud Developer Advocates Damien Brady, Paige Bailey, and Donovan Brown to talk about DevOps and data science and databases. For a data scientist, what does it even mean to "build"? Packaging and deployment are things that a data scientist doesn't normally have to consider in their day-to-day work. The process of making an AI app is usually divided into two streams of work: data scientists building machine learning models and app developers building the application for end users to consume. DevOps includes all the parties involved in getting the application deployed and maintained and thinking about all the phases that follow and precede their part of the end solution. So what does DevOps mean for data science? Why should you adopt DevOps best practices? In the first half, Paige and Damian share their views on what DevOps for data science would look like and how it can be introduced to provide continuous integration, delivery, and deployment of data science models. In the second half, Donovan and Damian talk about the DevOps life cycle of putting a database under version control and carrying out deployments through a release pipeline.

Dive into the world of big data processing and analytics with the "PySpark Cookbook". This book provides over 60 hands-on recipes for implementing efficient data-intensive solutions using Apache Spark and Python. By mastering these recipes, you'll be equipped to tackle challenges in large-scale data processing, machine learning, and stream analytics. What this Book will help me do Set up and configure PySpark environments effectively, including working with Jupyter for enhanced interactivity. Understand and utilize DataFrames for data manipulation, analysis, and transformation tasks. Develop end-to-end machine learning solutions using the ML and MLlib modules in PySpark. Implement structured streaming and graph-processing solutions to analyze and visualize data streams and relationships. Deploy PySpark applications to the cloud infrastructure efficiently using best practices. Author(s) This book is co-authored by None Lee and None Drabas, who are experienced professionals in data processing and analytics leveraging Python and Apache Spark. With their deep technical expertise and a passion for teaching through practical examples, they aim to make the complex concepts of PySpark accessible to developers of varied experience levels. Who is it for? This book is ideal for Python developers who are keen to delve into the Apache Spark ecosystem. Whether you're just starting with big data or have some experience with Spark, this book provides practical recipes to enhance your skills. Readers looking to solve real-world data-intensive challenges using PySpark will find this resource invaluable.

There are many benefits to becoming a data-driven organization, including the ability to accelerate and improve business decision accuracy through the real-time processing of transactions, social media streams, and IoT data. But those benefits require significant changes to your infrastructure. You need flexible architectures that can copy data to analytics platforms at near-zero latency while maintaining 100% production uptime. Fortunately, a solution already exists. This ebook demonstrates how change data capture (CDC) can meet the scalability, efficiency, real-time, and zero-impact requirements of modern data architectures. Kevin Petrie, Itamar Ankorion, and Dan Potter—technology marketing leaders at Attunity—explain how CDC enables faster and more accurate decisions based on current data and reduces or eliminates full reloads that disrupt production and efficiency. The book examines: How CDC evolved from a niche feature of database replication software to a critical data architecture building block Architectures where data workflow and analysis take place, and their integration points with CDC How CDC identifies and captures source data updates to assist high-speed replication to one or more targets Case studies on cloud-based streaming and streaming to a data lake and related architectures Guiding principles for effectively implementing CDC in cloud, data lake, and streaming environments The Attunity Replicate platform for efficiently loading data across all major database, data warehouse, cloud, streaming, and Hadoop platforms

In this podcast, Steve Goldbach & Geoff Tuff from Deloitte sat with Vishal to discuss their recently released book "Detonate". They shared their insights on a cleaner way to create strategies for a future proof and transformation friendly organization. Their tactical suggestions go a long way in helping install a robust strategy to increase responsiveness. Timeline: 0:29 The author's journeys. 4:30 Motivation behind writing "Detonate". 8:31 Getting rid of best practices. 12:08 Relevance of "Detonate" for data ops employees. 14:28 Detonate mindset. 22:17 Fixing orthodoxies. 26:19 How can "detonate" serve different kinds of companies? 32:50 Reason behind failing companies. 36:02 Existing company culture vs. detonate mindset. 39:15 "Failure is not ok". 45:10 Aha moments while writing "Detonate". 47:35 The animations in "Detonate". 49:33 Selling the detonate mindset. 52:13 What's next in detonating mindset? 53:54 Steven's and Geoff's favorite reads. 58:15 Key takeaways.

Steve / Geoff's Recommended Read: Geoff's suggestion: Cloud Atlas: A Novel by David Mitchell The Opposable mind https://amzn.to/2rA5BAV The Last Days of Night: A Novel by Graham Moore https://amzn.to/2rAvErB

Steve's suggestion: Thinking, Fast and Slow by Daniel Kahneman https://amzn.to/2ryIx5C The Opposable Mind: How Successful Leaders Win Through Integrative Thinking by Roger L. Martin https://amzn.to/2Kds7Y2 The Big Short: Inside the Doomsday Machine by Michael Lewis https://amzn.to/2KaFgRI

Podcast Link: https://futureofdata.org/solving-futureoforgs-with-detonate-mindset-by-steven_goldbach-geofftuff-futureofdata/

Geoff's Bio: GEOFF TUFF is a principal at Deloitte and a senior leader of the firm’s Innovation and Applied Design practices. In the past, he led the design firm Doblin and was a senior partner at Monitor Group, serving as a member of its global Board of Directors before the company was acquired by Deloitte. He has been with some form of Monitor for more than 25 years. He holds degrees from Dartmouth College and Harvard Business School.

Steve's Bio: STEVEN GOLDBACH is a principal at Deloitte and serves as the organization’s chief strategy officer. He is also a member of the Deloitte U.S. executive leadership team. Before joining Deloitte, Goldbach was a partner at Monitor Group and head of its New York office. Goldbach helps executives and their teams transform their organizations by making challenging and pragmatic strategy choices in the face of uncertainty. He is an architect, expert practitioner, and teacher of the variety of strategy methodologies developed and used by Monitor Deloitte over the years. Serving clients in many industries, including consumer products, telecommunications, media, and health care, Goldbach helps companies combine rigor and creativity to create their own future. He holds degrees from Queen’s University at Kingston and Columbia Business School.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Wanna Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #Leadership #Podcast #Future of #Work #Worker & #Workplace

Big Data Architect's Handbook is your comprehensive guide to mastering the art of building sophisticated big data solutions. As you delve into this book, you'll learn to design end-to-end big data pipelines and integrate data from various sources for insightful analysis. What this Book will help me do Understand the Hadoop ecosystem and familiarize yourself with major Apache projects. Make informed decisions when designing cloud infrastructures for big data needs. Gain expertise in analyzing structured and unstructured data using machine learning. Develop skills to implement scalable and efficient big data pipelines. Enhance your ability to visualize and monitor data insights effectively. Author(s) None Akhtar has amassed a wealth of experience in big data architecture and related technologies. With years of hands-on involvement in development, analysis, and implementation of big data systems, None brings a pragmatic and insightful perspective. This passion for educating others about data-driven technologies shines through in a user-first approach to making complex topics accessible. Who is it for? This book caters to aspiring data professionals, software developers, and tech enthusiasts aiming to enhance their expertise in big data. Readers with basic programming and data analysis skills will find the content approachable yet challenging enough to deepen their understanding. If your career goal involves managing, analyzing, and making decisions based on large datasets, this book will help bridge the gap between skill and application.

Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies. Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard. What You’ll Learn Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical advice Integrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and Spark Use StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processing Utilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processing Turbocharge Spark with Alluxio, a distributed in-memory storage platform Deploy big data in the cloud using Cloudera Director Perform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and Spark Understand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasks Implement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive modeling Study real-world big data case studies from innovative companies, including Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard Who This Book Is For BI and big data warehouse professionals interested in gaining practical and real-world insight into next-generation big data processing and analytics using Apache Kudu, Impala, and Spark; and those who want to learn more about other advanced enterprise topics

Summary

With the increased ease of gaining access to servers in data centers across the world has come the need for supporting globally distributed data storage. With the first wave of cloud era databases the ability to replicate information geographically came at the expense of transactions and familiar query languages. To address these shortcomings the engineers at Cockroach Labs have built a globally distributed SQL database with full ACID semantics in Cockroach DB. In this episode Peter Mattis, the co-founder and VP of Engineering at Cockroach Labs, describes the architecture that underlies the database, the challenges they have faced along the way, and the ways that you can use it in your own environments today.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Peter Mattis about CockroachDB, the SQL database for global cloud services

Interview

Introduction How did you get involved in the area of data management? What was the motivation for creating CockroachDB and building a business around it? Can you describe the architecture of CockroachDB and how it supports distributed ACID transactions?

What are some of the tradeoffs that are necessary to allow for georeplicated data with distributed transactions? What are some of the problems that you have had to work around in the RAFT protocol to provide reliable operation of the clustering mechanism?

Go is an unconventional language for building a database. What are the pros and cons of that choice? What are some of the common points of confusion that users of CockroachDB have when operating or interacting with it?

What are the edge cases and failure modes that users should be aware of?

I know that your SQL syntax is PostGreSQL compatible, so is it possible to use existing ORMs unmodified with CockroachDB?

What are some examples of extensions that are specific to CockroachDB?

What are some of the most interesting uses of CockroachDB that you have seen? When is CockroachDB the wrong choice? What do you have planned for the future of CockroachDB?

Contact Info

Peter

LinkedIn petermattis on GitHub @petermattis on Twitter

Cockroach Labs

@CockroackDB on Twitter Website cockroachdb on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

CockroachDB Cockroach Labs SQL Google Bigtable Spanner NoSQL RDBMS (Relational Database Management System) “Big Iron” (colloquial term for mainframe computers) RAFT Consensus Algorithm Consensus MVCC (Multiversion Concurrency Control) Isolation Etcd GDPR Golang C++ Garbage Collection Metaprogramming Rust Static Linking Docker Kubernetes CAP Theorem PostGreSQL ORM (Object Relational Mapping) Information Schema PG Catalog Interleaved Tables Vertica Spark Change Data Capture

The intro and outro music is from The Hug by The Freak Fandan

Abstract Today’s global organizations depend on being able to unlock business insights from massive volumes of data. Now, with IBM® FlashSystem 900 Model AE3, powered by IBM FlashCore® technology, they can make faster decisions based on real-time insights and unleash the power of the most demanding applications, including online transaction processing (OLTP) and analytics databases, virtual desktop infrastructures (VDIs), technical computing applications, and cloud environments. This IBM Redbooks® publication introduces clients to the IBM FlashSystem® 900 Model AE3. It provides in-depth knowledge of the product architecture, software and hardware, implementation, and hints and tips. Also illustrated are use cases that show real-world solutions for tiering, flash-only, and preferred-read, and also examples of the benefits gained by integrating the FlashSystem storage into business environments. This book is intended for pre-sales and post-sales technical support professionals and storage administrators, and for anyone who wants to understand how to implement this new and exciting technology.

Abstract This IBM® Redbooks® publication describes the new member of the IBM Z® family, IBM z14™ Model ZR1 (Machine Type 3907). It includes information about the Z environment and how it helps integrate data and transactions more securely, and can infuse insight for faster and more accurate business decisions. The z14 ZR1 is a state-of-the-art data and transaction system that delivers advanced capabilities, which are vital to any digital transformation. The z14 ZR1 is designed for enhanced modularity, in an industry standard footprint. A data-centric infrastructure must always be available with a 99.999% or better availability, have flawless data integrity, and be secured from misuse. It also must be an integrated infrastructure that can support new applications. Finally, it must have integrated capabilities that can provide new mobile capabilities with real-time analytics that are delivered by a secure cloud infrastructure. IBM z14 ZR1 servers are designed with improved scalability, performance, security, resiliency, availability, and virtualization. The superscalar design allows z14 ZR1 servers to deliver a record level of capacity over the previous IBM Z platforms. In its maximum configuration, z14 ZR1 is powered by up to 30 client characterizable microprocessors (cores) running at 4.5 GHz. This configuration can run more than 29,000 million instructions per second and up to 8 TB of client memory. The IBM z14 Model ZR1 is estimated to provide up to 54% more total system capacity than the IBM z13s® Model N20. This Redbooks publication provides information about IBM z14 ZR1 and its functions, features, and associated software support. More information is offered in areas that are relevant to technical planning. It is intended for systems engineers, consultants, planners, and anyone who wants to understand the IBM Z servers functions and plan for their usage. It is intended as an introduction to mainframes. Readers are expected to be generally familiar with IBM Z technology and terminology.

Spark for Data Professionals introduces and solidifies the concepts behind Spark 2.x, teaching working developers, architects, and data professionals exactly how to build practical Spark solutions. Jeffrey Aven covers all aspects of Spark development, including basic programming to SparkSQL, SparkR, Spark Streaming, Messaging, NoSQL and Hadoop integration. Each chapter presents practical exercises deploying Spark to your local or cloud environment, plus programming exercises for building real applications. Unlike other Spark guides, Spark for Data Professionals explains crucial concepts step-by-step, assuming no extensive background as an open source developer. It provides a complete foundation for quickly progressing to more advanced data science and machine learning topics. This guide will help you: Understand Spark basics that will make you a better programmer and cluster “citizen” Master Spark programming techniques that maximize your productivity Choose the right approach for each problem Make the most of built-in platform constructs, including broadcast variables, accumulators, effective partitioning, caching, and checkpointing Leverage powerful tools for managing streaming, structured, semi-structured, and unstructured data

Big Data Analytics with Hadoop 3 is your comprehensive guide to understanding and leveraging the power of Apache Hadoop for large-scale data processing and analytics. Through practical examples, it introduces the tools and techniques necessary to integrate Hadoop with other popular frameworks, enabling efficient data handling, processing, and visualization. What this Book will help me do Understand the foundational components and features of Apache Hadoop 3 such as HDFS, YARN, and MapReduce. Gain the ability to integrate Hadoop with programming languages like Python and R for data analysis. Learn the skills to utilize tools such as Apache Spark and Apache Flink for real-time data analytics within the Hadoop ecosystem. Develop expertise in setting up a Hadoop cluster and performing analytics in cloud environments such as AWS. Master the process of building practical big data analytics pipelines for end-to-end data processing. Author(s) Sridhar Alla is a seasoned big data professional with extensive industry experience in building and deploying scalable big data analytics solutions. Known for his expertise in Hadoop and related ecosystems, Sridhar combines technical depth with clear communication in his writing, providing practical insights and hands-on knowledge. Who is it for? This book is tailored for data professionals, software engineers, and data scientists looking to expand their expertise in big data analytics using Hadoop 3. Whether you're an experienced developer or new to the big data ecosystem, this book provides the step-by-step guidance and practical examples needed to advance your skills and achieve your analytical goals.

This IBM® Redbooks® publication provides information to help you with the sizing, configuration, and monitoring of hybrid cloud solutions using the transparent cloud tiering (TCT) functionality of IBM Spectrum™ Scale. IBM Spectrum Scale™ is a scalable data, file, and object management solution that provides a global namespace for large data sets and several enterprise features. The IBM Spectrum Scale feature called transparent cloud tiering allows cloud object storage providers, such as IBM Cloud™ Object Storage, IBM Cloud, and Amazon S3, to be used as a storage tier for IBM Spectrum Scale. Transparent cloud tiering can help cut storage capital and operating costs by moving data that does not require local performance to an on-premise or off-premise cloud object storage provider. Transparent cloud tiering reduces the complexity of cloud object storage by making data transfers transparent to the user or application. This capability can help you adapt to a hybrid cloud deployment model where active data remains directly accessible to your applications and inactive data is placed in the correct cloud (private or public) automatically through IBM Spectrum Scale policies. This publication is intended for IT architects, IT administrators, storage administrators, and those wanting to learn more about sizing, configuration, and monitoring of hybrid cloud solutions using IBM Spectrum Scale and transparent cloud tiering.

Hands-On Data Science with Anaconda is your guide to harnessing the full potential of Anaconda, a powerful platform for data science and machine learning. With this book, you will learn how to set up Anaconda, manage packages, explore advanced data processing techniques, and create robust machine learning models using Python, R, and Julia. What this Book will help me do Master data preprocessing techniques including cleaning, sorting, and classification using Anaconda. Understand and utilize the conda package manager for efficient package management. Learn to explore and visualize data using packages and frameworks supported by Anaconda. Perform advanced operations like clustering, regression, and building predictive models. Implement distributed computing and manage environments effectively with Anaconda Cloud. Author(s) Yuxing Yan and co-author None Yan are seasoned data science professionals with extensive experience in utilizing cutting-edge tools like Anaconda to simplify and enhance data science workflows. With a focus on making complex concepts accessible, they offer a practical and systematic approach to mastering tools that power real-world data science projects. Who is it for? This book is for data science practitioners, analysts, or developers with a basic understanding of Python, R, and linear algebra who want to scale their skills and learn to utilize the Anaconda platform for their projects. If you're seeking to work more effectively within the Anaconda ecosystem or equip yourself with efficient tools for data analysis and machine learning, this book is for you.