Serverless ETL and Analytics with AWS Glue
by Vishal Pathak, Subramanya Vajiraya, Noritaka Sekiyama, Tomohiro Tanaka, Albert Quiroga, Ishan Gaur
Chapter 4: Data Preparation
In the previous chapter, we explored fundamental concepts surrounding data ingestion and how we can leverage AWS Glue to ingest data from various sources, such as file/object stores, JDBC data stores, streaming data sources, and SaaS data stores. We also discussed different features of AWS Glue ETL, such as schema flexibility, schema conflict resolution, advanced ETL transformations and extensions, incremental data ingestion using job bookmarks, grouping, and workload partitioning using bounded execution in detail with practical examples. Doing so allowed us to understand how each of these features can be used to ingest data from data stores in specific use cases.
In this chapter, we will be introducing the fundamental ...
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