Catalyst blog

• What bad data actually costs your business — and why catching it early is cheaper

  A non-technical guide to the business cost of poor data quality — where the money really goes, why an error gets roughly ten times more expensive at every stage it survives, and how to measure your own number instead of borrowing an analyst's.

• What is ODCS? The Open Data Contract Standard, explained

  What the Open Data Contract Standard is, what a v3 contract actually contains, why an open standard beats a vendor's rule format, and how importing and exporting ODCS keeps your data quality rules portable.

• How to validate data in Amazon Redshift

  A practical guide to validating data quality in Amazon Redshift — the six checks every table needs, how to keep them off the WLM queue, the unenforced-constraint trap, and how to run them as a versioned data contract.

• How to validate data in Microsoft Fabric

  A practical guide to validating data quality in Microsoft Fabric — Warehouse and Lakehouse SQL analytics endpoint, the T-SQL to write the six essential checks, the metadata sync trap, and how to run it all as a versioned data contract.

• How to validate data in BigQuery

  A practical guide to validating data quality in Google BigQuery — the six checks every table needs, how to write them without scanning terabytes, and how to turn them into a versioned data contract that runs on a schedule.

• How to validate data in MySQL

  A practical guide to validating data quality in MySQL — the six checks every table needs, the SQL to write them, and the coercion, collation and zero-date traps that make MySQL uniquely good at hiding bad data.

• How to validate data in PostgreSQL

  A practical guide to validating data quality in PostgreSQL — the six checks every table needs, the SQL to write them, the null and locking traps to avoid, and how to turn them into a versioned data contract.

• How to validate data in SQL Server

  A practical guide to validating data quality in Microsoft SQL Server — the six checks every table needs, the T-SQL to write them, and how to turn them into a versioned data contract that runs on a schedule.