Quiz and Summary on Data Analysis and Quality Control
The chapter outlines the comprehensive process of data analysis and quality control using AWS services, detailing the choice between provisioned and serverless analytics. Key practices include optimizing SQL queries in Athena, leveraging partitioning and columnar formats for cost efficiency, and utilizing Spark for interactive data exploration. It emphasizes data validation dimensions—completeness, consistency, accuracy, and integrity—alongside profiling techniques to detect data skew. Tools like AWS Glue DataBrew and Amazon QuickSight facilitate data preparation and visualization, while DQDL rules ensure quality checks throughout the data pipeline.
We'll cover the following...
- Summary
- Provisioned vs. serverless analytics
- Structuring SQL queries in Athena
- Cost optimization with partitioning and columnar formats
- Spark exploration in Athena notebooks
- Aggregation techniques
- Visualization with DataBrew and QuickSight
- Data validation dimensions
- Data profiling and skew detection
- Sampling techniques
- AWS Glue DataBrew workflows
- DQDL quality rules and inline checks
- Test your knowledge
Summary
This chapter covered the complete journey from querying data in S3 data lakes to validating, profiling, and visualizing analytical outputs using AWS serverless and managed services. The content spanned architectural decisions between provisioned and serverless analytics, SQL query optimization in Athena, interactive exploration with Spark notebooks, data quality enforcement, and dashboard creation.
Provisioned vs. serverless analytics
The chapter established a decision framework for choosing between provisioned services like Amazon Redshift and Amazon EMR vs. serverless options like Amazon Athena. Provisioned services excel when workloads require predictable sub-second latency, high concurrency, or heavy Spark-based transformations. Athena is ideal for ad hoc exploration, intermittent queries, and cost-sensitive scenarios where pay-per-scan pricing aligns with budget constraints. Both approaches integrate with the AWS Glue Data Catalog for centralized metadata management.
Structuring SQL queries in Athena
Athena uses the Trino engine, which supports ANSI SQL with full capabilities for SELECT statements, JOIN operations, and aggregations. Critical practices include always including ...