data-integrate

To prevent fan-out during data integration, you must enforce explicit record grain definition before joining multiple CSV files. This process guides key null checks, cardinality inspection, and pre/post-join row-count comparisons to detect unintended duplicates.

Data lineage records the provenance of your merged datasets into an analysis_context.md file during ETL preparation. It documents transformations and integrity checks to ensure downstream analysis remains reproducible and auditable.

Safely merging time-series data requires temporal alignment validation to prevent future information leakage. This process checks time ranges across event and order tables before joining to ensure chronological correctness for analysis.

Yes, you can integrate tabular datasets from expanded ZIP archives, Excel files, and CSV files. The process merges customer, order, and event tables into a single canonical analysis_table.csv while verifying key uniqueness.

Mismatched rows often occur when joining customer and order tables without clear grain definition. Running missing key checks, duplicate inspections, and primary-key validations before merging resolves these semantic inconsistencies.

The best way to document data quality checks during ETL preparation is by recording null checks, cardinality inspections, and provenance into project documentation. This creates an auditable lineage trail alongside your canonical analysis table.