Apache Iceberg: The Definitive Guide: Data Lakehouse Functionality, Performance, and Scalability on the Data Lake
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| Publisher | O'Reilly Media |
| ISBN 13 | 9781098148621 |
| ISBN 10 | 1098148622 |
| Book Description | Traditional data architecture patterns are severely limited. To use these patterns, you have to ETL data into each tool--a cost-prohibitive process for making warehouse features available to all of your data. The lack of flexibility with these patterns requires you to lock into a set of priority tools and formats, which creates data silos and data drift. This practical book shows you a better way. Apache Iceberg provides the capabilities, performance, scalability, and savings that fulfill the promise of an open data lakehouse. By following the lessons in this book, you'll be able to achieve interactive, batch, machine learning, and streaming analytics with this high-performance open source format. Authors Tomer Shiran, Jason Hughes, and Alex Merced from Dremio show you how to get started with Iceberg. With this book, you'll learn: The architecture of Apache Iceberg tables What happens under the hood when you perform operations on Iceberg tables How to further optimize Iceberg tables for maximum performance How to use Iceberg with popular data engines such as Apache Spark, Apache Flink, and Dremio Discover why Apache Iceberg is a foundational technology for implementing an open data lakehouse. |
| About the Author | Tomer Shiran is the Founder and Chief Product Officer of Dremio, an open data lakehouse platform that enables companies to run analytics in the cloud without the cost, complexity and lock-in of data warehouses. As the company's founding CEO, Tomer built a world-class organization that has raised over $400M and now serves hundreds of the world's largest enterprises, including 3 of the Fortune 5. Prior to Dremio, Tomer was the 4th employee and VP Product of MapR, a Big Data analytics pioneer. He also held numerous product management and engineering roles at Microsoft and IBM Research, founded several websites that have served millions of users and hundreds of thousands of paying customers, and is a successful author and presenter on a wide range of industry topics. He holds an MS in Computer Engineering from Carnegie Mellon University and a BS in Computer Science from Technion - Israel Institute of Technology.Jason Hughes is the Director of Technical Advocacy at Dremio. Previously at Dremio, he's been a Product Director, Technical Director and a Senior Solutions Architect. He's been working in technology and data for over a decade, including roles as tech lead for the field at Dremio, the pre-sales and post-sales lead for Presto and QueryGrid for the Americas at Teradata, and leading the development, deployment, and management of a custom CRM system for multiple auto dealerships. He is passionate about making customers and individuals successful and self-sufficient. When he's not working, he's usually taking his dog to the dog park, playing hockey, or cooking (when he feels like it). He lives in San Diego, California.Alex Merced is a developer advocate for Dremio and has worked as a developer and instructor for companies like GenEd Systems, Crossfield Digital, CampusGuard and General Assembly. Alex is passionate about technology and has put out tech content on outlets such as blogs, videos and his podcasts Datanation and Web Dev 101. Alex Merced has contributed a variety of libraries in the Javascript & Python worlds including SencilloDB, CoquitoJS, dremio-simple-query and more.Dipankar Mazumdar is currently a Data Eng/Science Advocate at Dremio where his primary focus is advocating data practitioners on Dremio's open lakehouse platform and various open-sourced projects, such as Apache Iceberg. Dipankar is also interested in Visual Analytics research, and his latest work was on "Explainability of ensemble models" using multidimensional projection techniques. |
| Language | English |
| Author | Tomer Shiran |
| Publication Date | 2024-06-11 |
| Number of Pages | 341 pages |
Apache Iceberg: The Definitive Guide: Data Lakehouse Functionality, Performance, and Scalability on the Data Lake
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