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Join our next Big Data Europe conference on 22-25 November in 2022 where you will be able to learn from global experts giving technical talks and hand-on workshops in the fields of Big Data, High Load, Data Science, Machine Learning and AI. This time, the conference will be held in a hybrid setting allowing you to attend workshops and listen to expert talks on-site or online.

Dive into the world of data engineering with 'Azure Data Engineering Cookbook' to master building efficient ETL workflows using Microsoft Azure Data services. Whether you're working on batch processing solutions or real-time analytics, this book is your guide to implementing effective, scalable data operations. What this Book will help me do Design and implement efficient ETL pipelines for batch and real-time processing on MS Azure. Understand the use of Azure Blob storage for managing large data sets. Ingest, process, and analyze data using tools like Azure Synapse and Databricks. Develop and secure automation pipelines using Azure Data Factory. Leverage Azure Stream Analytics for real-time data processing workflows. Author(s) Ahmad Osama and Nagaraj Venkatesan bring years of expertise in cloud solutions and data engineering. Renowned for their practical teaching approach, they have helped countless professionals master the intricacies of Azure. Their focus is on equipping readers with actionable skills for real-world data challenges. Who is it for? This book is ideal for data engineers and database professionals aiming to hone their expertise in advanced Azure data engineering tasks. Readers should have a working knowledge of Azure fundamentals and basic data engineering concepts. If you're a technical architect or ETL developer seeking to transition or enhance your skills in Azure's ecosystem, you'll find immense value here.

Get a 360-degree view of how the journey of data analytics solutions has evolved from monolithic data stores and enterprise data warehouses to data lakes and modern data warehouses. You will This book includes comprehensive coverage of how: To architect data lake analytics solutions by choosing suitable technologies available on Microsoft Azure The advent of microservices applications covering ecommerce or modern solutions built on IoT and how real-time streaming data has completely disrupted this ecosystem These data analytics solutions have been transformed from solely understanding the trends from historical data to building predictions by infusing machine learning technologies into the solutions Data platform professionals who have been working on relational data stores, non-relational data stores, and big data technologies will find the content in this book useful. The book also can help you start your journey into the data engineer world as it provides an overview of advanced data analytics and touches on data science concepts and various artificial intelligence and machine learning technologies available on Microsoft Azure. What Will You Learn You will understand the: Concepts of data lake analytics, the modern data warehouse, and advanced data analytics Architecture patterns of the modern data warehouse and advanced data analytics solutions Phases—such as Data Ingestion, Store, Prep and Train, and Model and Serve—of data analytics solutions and technology choices available on Azure under each phase In-depth coverage of real-time and batch mode data analytics solutions architecture Various managed services available on Azure such as Synapse analytics, event hubs, Stream analytics, CosmosDB, and managed Hadoop services such as Databricks and HDInsight Who This Book Is For Data platform professionals, database architects, engineers, and solution architects

Summary Data lakes offer a great deal of flexibility and the potential for reduced cost for your analytics, but they also introduce a great deal of complexity. What used to be entirely managed by the database engine is now a composition of multiple systems that need to be properly configured to work in concert. In order to bring the DBA into the new era of data management the team at Upsolver added a SQL interface to their data lake platform. In this episode Upsolver CEO Ori Rafael and CTO Yoni Iny describe how they have grown their platform deliberately to allow for layering SQL on top of a robust foundation for creating and operating a data lake, how to bring more people on board to work with the data being collected, and the unique benefits that a data lake provides. This was an interesting look at the impact that the interface to your data can have on who is empowered to work with it.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! You listen to this show because you love working with data and want to keep your skills up to date. Machine learning is finding its way into every aspect of the data landscape. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. The Data Engineering Podcast is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to dataengineeringpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll. Your host is Tobias Macey and today I’m interviewing Ori Rafael and Yoni Iny about building a data lake for the DBA at Upsolver

Interview

Introduction How did you get involved in the area of data management? Can you start by sharing your definition of what a data lake is and what it is comprised of? We talked last in November of 2018. How has the landscape of data lake technologies and adoption changed in that time?

How has Upsolver changed or evolved since we last spoke?

How has the evolution of the underlying technologies impacted your implementation and overall product strategy?

What are some of the common challenges that accompany a data lake implementation? How do those challenges influence the adoption or viability of a data lake? How does the introduction of a universal SQL layer change the staffing requirements for building and maintaining a data lake?

What are the advantages of a data lake over a data warehouse if everything is being managed via SQL anyway?

What are some of the underlying realities of the data systems that power the lake which will eventually need to be understood by the operators of the platform? How is the SQL layer in Upsolver implemented?

What are the most challenging or complex aspects of managing the underlying technologies to provide automated partitioning, indexing, etc.?

What are the main concepts that you need to educate your customers on? What are some of the pitfalls that users should be aware of? What features of your platform are often overlooked or underutilized which you think should be more widely adopted? What have you found to be the most interesting, unexpected, or challenging lessons learned while building the technical and business elements of Upsolver? What do you have planned for the future?

Contact Info

Ori

LinkedIn

Yoni

yoniiny on GitHub LinkedIn

Parting Question

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

Links

Upsolver

Podcast Episode

DBA == Database Administrator IDF == Israel Defense Forces Data Lake Eventual Consistency Apache Spark Redshift Spectrum Azure Synapse Analytics SnowflakeDB

Podcast Episode

BigQuery Presto

Podcast Episode

Apache Kafka Cartesian Product kSQLDB

Podcast Episode

Eventador

Podcast Episode

Materialize

Podcast Episode

Common Table Expressions Lambda Architecture Kappa Architecture Apache Flink

Podcast Episode

Reinforcement Learning Cloudformation GDPR

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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Choice is usually a good thing for consumers but it can be difficult to navigate the landscape of Open Source ESBs. I believe this book is extremely valuable for readers looking to choose an ESB and looking to get a stronger grasp on how to implement an ESB using open source projects. I found the case studies in section III particularly useful since they pull together many of the concepts learned throughout the book. This book guides the reader through a logical journey of discovery and demonstration to deliver a solid understanding of the core ESB concepts and how they can be used in the real world. Armed with this book and the wealth of open source projects available I think the reader will be ready to take on any ESB project. Open Source made ESBs a lot more fun, go and enjoy yourself! —From the Foreword by Ross Mason, Creator of the Mule Project Open-Source ESBs in Action describes how to use ESBs in real-world situations. You will learn how the various features of an ESB such as transformation, routing, security, connectivity, and more can be implemented on the example of two open-source ESB implementations: Mule and ServiceMix. The authors first introduce ServiceMix and Mule, and then present general principles and patterns of ESB use, as well as a structured approach to solving common integration problems, through examples using them. About the Technology The need for enterprise integration is widespread for the simple reason that businesses require independent applications to exchange information with each other. A CRM application must know about the order history of a customer, but that history is stored in the sales application. A technology that companies increasingly use to integrate enterprise applications is the Enterprise Service Bus (ESB). About the Book Working in integration projects is exciting, with new technologies and paradigms arriving every day. In this area, open source is playing a more and more dominant role with projects such as Mule and ServiceMix. Open-Source ESBs in Action will help you to learn open-source integration technologies quickly and will provide you with knowledge that you can use to effectively work with Mule and ServiceMix. What's Inside Numerous code examples Detailed explanation on how to use Mule and ServiceMix Practical, real-world examples and case studies Integration with a full open source tool stack About the Reader About the Authors Tijs Rademakers is a software architect with more than six years of experience in designing and developing Java and EE applications. He works for Atos Origin, a large European system integrator, where he is responsible for SOA and BPM services and knowledge development. Tijs has designed and implemented large process- and application-integration solutions, primarily focused on open standards. He has extensive product knowledge of open source as well as closed source SOA and enterprise integration tools, including Mule, ServiceMix, jBPM, and WebSphere Process Server. Tijs is a regular speaker at Java conferences, where he talks about open source integration topics like Mule and ServiceMix. Tijs lives in the Netherlands near Eindhoven with his girlfriend and his new daughter, Liv. Jos Dirksen has been working with Java and J2EE applications for more than six years as a software architect. The last couple of years, his focus topics have been open source, security, and quality. He has worked with various open source and commercial integration solutions, mostly in the government and the healthcare areas. Jos has a lot of project experience working with Mule, Apache Synapse, and Apache Axis2 and has also completed projects based on the integration tooling from IBM. Jos regularly gives presentation on open source, Mule, and other related topics. He lives in Eindhoven, the Netherlands, with his wife. Quotes A great book for any ESB practitioner. - Rick Wagner, Acxiom Corporation A must-have ESB resource! - Craig Borysowich, Imagination Edge, Inc. The most comprehensive content that I've seen on open source ESBs. - Rodney Biresch, Chariot Solutions The Bible for integration architects. - Davide Piazza, Omnys s.r.l. ...ample code samples and excellent descriptions. - Jeff Davis, HireRight, Inc. This book will take you to a new level. - Christian Siegers, Stater International Mortgage Services