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Designing Cloud Data Platforms

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(as of May 20,2023 21:05:49 UTC – Details)


Designing Cloud Data Platforms is an essential guide to designing and envisioning modern scalable data platforms, which leverage the flexibility and cost-effectiveness of cloud computing. The book, written by Danil Zburivsky and Lynda Partner, presents a six-layer approach that offers greater flexibility and cost savings, while also providing insights into patterns for ingesting data from various sources and utilizing cloud vendor services.

The traditional centralized data warehouses are quickly giving way to multi-faceted cloud data platforms, which provide an integrated view of an organization’s business using all of its data. This allows for advanced analytic practices and prediction models that boost efficiency and overall performance. Designing Cloud Data Platforms takes you through the core components of a cloud data platform, with a focus on key technologies, such as Spark and Kafka Streams.

The book covers various aspects of data management in the cloud, including security measures, advanced analytic and BI tools, metadata and real-time analytics, and defensive architecture. With its clear and concise approach, this book helps data professionals with a solid foundation in cloud computing, Hadoop, or Spark achieve maximum performance, regardless of the cloud vendor they use.

This book, published by Manning Publications, includes a free eBook in PDF, Kindle, and ePub formats. The language used is English, and the paperback version contains 336 pages. Its ISBN-10 is 1617296449, and ISBN-13 is 978-1617296444. The book weighs 1.32 pounds, and its dimensions are 7.38 x 0.6 x 9.25 inches.

Overall, Designing Cloud Data Platforms is an excellent resource for anyone looking to create a scalable, secure, and cost-efficient cloud data platform.

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