Data Warehouse ( ETL Test ) for Beginners.

Perform eyeballing validation by focusing on the data model design, ensuring table and column types and lengths match the spec, and verifying metadata naming standards and mappings in mapping documents.

Validate transformation logic by comparing source and target date format results using a mapping document, SQL checks, and quick visual cross-checks for compliance.

Validate records by count to detect skew between source and target tables using key acuity. Compare primary source and target records, note discrepancies, and download the attached queries for auditing.

Analyze six-table data warehouse ETL tests by composing and amending a SQL query to count records, compare source and transformed results, and document actual results for reference.

Master null value validation by ensuring mandatory fields, such as first name, last name, middle name, and date of birth, are present for ETL testing in the data warehouse.

Learn SQL queries for null value validation in a data warehouse ETL testing context, creating reusable checks across test cases.

Explore SQL null value validation in an ETL test context by building test cases, copying column schemas, applying formulas, and interpreting results when fields are optional.

Enforce a rule that ensures uniqueness of a column combination, such as first name, last name, middle name, and birth date, guaranteeing a single record per person with a query.

Perform SQL duplicate validation in an ETL test, ensuring the empty table yields no data and test results align with expected outcomes for a passing status.

Validate data integrity in large ETL workflows by comparing source and target data through targeted column checks, and analyze data profiles with unique values, counts, mean, max, min, and frequency.

Learn how to create test cases, understand queries, build faster formulas, handle null values and sku queries, and assess data completeness in large data sets.