October 2021
Beginner to intermediate
322 pages
7h 27m
English
Content preview from Essential PySpark for Scalable Data Analytics
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Section 1: Data Engineering
This section introduces the uninitiated to the Distributed Computing paradigm and shows how Spark became the de facto standard for big data processing.
Upon completion of this section, you will be able to ingest data from various data sources, cleanse it, integrate it, and write it out to persistent storage such as a data lake in a scalable and distributed manner. You will also be able to build real-time analytics pipelines and perform change data capture in a data lake. You will understand the key differences between the ETL and ELT ways of data processing, and how ELT evolved for the cloud-based data lake world. This section also introduces you to Delta Lake to make cloud-based data lakes more reliable and performant. ...
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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.