December 2021
Beginner to intermediate
482 pages
11h 27m
English
Content preview from Data Engineering with AWS
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Section 2: Architecting and Implementing Data Lakes and Data Lake Houses
In this section of the book, we examine an approach for architecting a high-level data pipeline and then dive into the specifics of data ingestion and transformation. We also examine different types of data consumers, learn about the important role of data marts and data warehouses, and finally put it all together by orchestrating data pipelines. We get hands-on with various AWS services for data ingestion (Amazon Kinesis and DMS), transformation (AWS Glue Studio), consumption (AWS Glue DataBrew), and pipeline orchestration (Step Functions).
This section comprises the following chapters:
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.