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Grokking the AWS Certified Data Engineer – Associate Exam

Data Validation and Profiling

Data validation and profiling are essential for ensuring data quality before it is utilized, preventing errors that could lead to flawed analyses or compliance issues. Validation checks data against established rules, focusing on completeness, consistency, accuracy, and integrity, while profiling examines datasets to reveal their structure and anomalies. Key AWS services like Glue Data Quality and Redshift system views support these processes. Additionally, addressing data skew and employing sampling techniques can enhance performance and efficiency when working with large datasets, ensuring that data remains trustworthy and actionable.

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Data that has been explored, aggregated, and visualized is only as valuable as its underlying trustworthiness. For the AWS Certified Data Engineer – Associate exam, understanding how to confirm data quality before it reaches consumers is a critical competency. Pipelines that ingest and transform data without validation propagate errors silently, leading to incorrect dashboards, flawed ML models, and compliance failures.

This lesson covers two complementary disciplines that guard against these risks. Data validation checks data against known rules and expectations, while data profiling discovers the actual structure, distributions, and anomalies within a dataset. The primary AWS services relevant here include AWS Glue Data Quality for rule-based validation, the AWS Glue Data Catalog ColumnStatistics for profiling metadata, and Amazon Redshift system views such as SVV_TABLE_INFO for skew and sort diagnostics. This lesson focuses on the concepts and mechanisms themselves.

The four dimensions of data validation

Every data validation strategy rests on four foundational dimensions. Understanding each dimension and its AWS implementation is essential for both real-world pipelines and exam scenarios.

  • Completeness ensures that no required fields are null or missing. Consider an S3-based data lake where every order record must contain a customer_id before loading into Redshift. A Glue Data Quality Completeness rule can enforce that the null count in this column equals zero, preventing incomplete records from reaching the consumption layer.

  • Consistency ensures values conform to the same format and business rules across sources. When DMS-replicated RDS data and Kinesis-ingested event data both land in S3, date formats must match (for example, ISO 8601 across all partitions). Glue Data Quality CustomSql expressions and ColumnStatistics help detect format drift between sources.

  • Accuracy ensures values reflect real-world truth. Validating that latitude values fall between -90 and 90 and longitude values fall between -180 and 180 is a classic accuracy check. Glue Data Quality ColumnValues rules with bound expressions handle this natively.

  • Integrity ensures referential relationships hold. Every product_id in a transactions table must have a corresponding entry in the products dimension table. In Redshift, informational foreign key constraints document these ...