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Grokking the AWS Certified Data Engineer – Associate Exam

Serverless ETL with AWS Glue

Serverless ETL with AWS Glue focuses on creating efficient ETL pipelines that extract, transform, and load data using serverless Spark applications. Glue ETL Jobs utilize the DynamicFrame API to handle schema inconsistencies and optimize data for analytics. Key practices include transforming data formats, managing small files, and implementing partitioning strategies to enhance performance and reduce costs. The production optimization checklist emphasizes using Parquet format with Snappy compression, right-sizing DPUs, and enabling job bookmarks for incremental processing. Understanding these concepts is crucial for the AWS Certified Data Engineer exam and effective data management.

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Building serverless ETL (Extract, Transform, Load)A data integration pattern where data is pulled from source systems, reshaped or cleaned, and written to a target store optimized for analytics. pipelines is one of the most heavily tested competencies on the AWS Certified Data Engineer – Associate (DEA-C01) exam. A data integration pattern where data is pulled from source systems, reshaped or cleaned, and written to a target store optimized for analytics.A Glue ETL Job is a serverless Spark application that reads data referenced by the Data Catalog, applies transformations, and writes output to a target location. This covers both the Transform and Load phases of the pipeline.

The DynamicFrame API and transformation logic

An AWS Glue extension of Spark DataFrames that handles schema inconsistencies through choice types, where a single column may contain mixed data types across records. The ResolveChoice ...