Quiz and Summary on AWS Data Stores
The chapter outlines essential AWS data engineering concepts, focusing on data characteristics, storage optimization, and various database types. It emphasizes the 3 Vs—Volume, Velocity, and Variety—as key factors in selecting AWS services, such as RDS for moderate workloads and S3 for large-scale data lakes. It discusses storage formats like Parquet for analytical workloads, data lineage tracking, and schema evolution. Additionally, it covers relational and non-relational databases, data warehousing with Amazon Redshift, and the role of data lakes in distributed computing, highlighting strategies for efficient data management and migration.
Summary
This chapter established foundational AWS data engineering concepts spanning data characteristics, storage optimization, relational and non-relational databases, data warehousing, data lakes, and lifecycle management, including migration strategies.
Data characteristics and the 3 Vs
Volume, velocity, and variety form the decision framework for selecting AWS services. Volume determines whether you use RDS for moderate workloads or S3-based data lakes for petabyte-scale. Velocity dictates whether to use Glue ETL for batch processing or Kinesis Data Streams for real-time streaming. Variety guides format choices: structured data fits relational stores, ...