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Grokking the AWS Certified Data Engineer – Associate Exam

SQL Transformations and Lambda Processing

Data engineering often involves transforming data using SQL within Amazon Redshift and orchestrating AWS Lambda functions for efficient processing. Key practices include utilizing stored procedures in Redshift to minimize latency, employing Common Table Expressions (CTEs) for modularity, and optimizing query performance through early filtering and selective column retrieval. Lambda functions serve as connectors in data pipelines, with concurrency management and memory scaling being crucial for performance. Additionally, integrating Amazon EFS allows Lambda to handle larger datasets beyond its local storage limits, facilitating seamless data processing and transformation workflows.

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Data pipelines rarely consist of a single technology. In the previous lesson, you explored how containers handle long-running, compute-heavy workloads. However, a significant portion of data engineering work involves transforming data with SQL inside a warehouse and orchestrating lightweight event-driven functions that glue pipeline stages together. For the AWS Certified Data Engineer – Associate exam, you must understand two critical pillars:

  • How to write and orchestrate SQL transformations within Amazon Redshift

  • How to configure AWS Lambda functions for concurrency, performance, and storage-mounted workloads.

Candidates are expected to distinguish when Redshift stored procedures outperform Glue ETL, how distribution and sort keys affect query performance, and why mounting Amazon EFS on Lambda solves storage limitations that /tmp cannot. This lesson moves through SQL query structuring and optimization first, then shifts to Lambda configuration and EFS mounting for large-dataset processing.

Writing SQL for data transformations

Amazon Redshift is a columnar, massively parallel processing (MPP) data warehouse where SQL serves as the primary interface for transforming data within pipelines. Rather than extracting data out of Redshift to transform it externally, data engineers encapsulate transformation logic directly inside the warehouse using stored procedures.

Redshift stored procedures and pipeline integration

Stored procedures run entirely within the Redshift cluster, they eliminate the round-trip latency that occurs when an external application issues individual SQL statements sequentially. Stored procedures integrate naturally into broader data pipelines. AWS Step Functions can invoke them as workflow steps, Glue Python shell jobs can call them via JDBC, and the Redshift Data API enables asynchronous execution ...