Move SQL Server data into open formats such as CSV, JSON, and Parquet while preserving types, NULLs, encoding, and readable files.
Slowly Changing Dimension Quiz: Type 1 or Type 2?
This Slowly Changing Dimension Quiz asks which type to use when a customer moves from Denver to Austin and sales before the move must stay under Denver. The answer is Type 2. A tested script runs Type 1 and Type 2 side by side, checks the move date and shows both reports.
Data Quality Rules in T-SQL
Data quality rules in T-SQL turn nulls, duplicates, invalid dates, and broken references into visible failures before rows reach reports.
Watermarks for Incremental Extracts
Watermarks for incremental extracts need clear boundaries, durable checkpoints, and a plan for late updates, deletes, and missed rows.
SSIS Project and Package Deployment Explained
Understand SSIS deployment models, catalog environments, parameter bindings, and the checks that keep package execution predictable after deployment.
Handling the Row That Arrives Late
Handle late arriving data with explicit event dates, inferred dimension members, replay windows, and a clear policy for correcting published history.
Where a Business Rule Should Actually Live
Place the business rules database applications depend on in constraints, procedures, triggers, or application code according to their guarantees.