August 2022
Intermediate to advanced
434 pages
10h 34m
English
book
Serverless ETL and Analytics with AWS Glue
by Vishal Pathak, Subramanya Vajiraya, Noritaka Sekiyama, Tomohiro Tanaka, Albert Quiroga, Ishan Gaur
Content preview from Serverless ETL and Analytics with AWS Glue
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Chapter 3: Data Ingestion
In the previous chapter, we discussed the fundamental concepts and inner workings of the various features/microservices that are available in AWS Glue, such as Glue Data Catalog, connections, crawlers, and classifiers, the schema registry, Glue ETL jobs, development endpoints, interactive sessions, and triggers. We also explored how AWS Glue crawlers aid in data discovery by crawling different types of data stores – Amazon S3, JDBC (Amazon RDS or on-premises databases), and DynamoDB/MongoDB/DocumentDB infer the schema and populate AWS Glue Data Catalog. While discussing Glue ETL in the previous chapter, we introduced a few of the important extensions/features of Spark ETL, including GlueContext, DynamicFrame, JobBookmark ...
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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.