Catalyst blog

• Use Catalyst from Claude, ChatGPT and your IDE (MCP)

  How to connect Catalyst to Claude, ChatGPT, Claude Code, Cursor or VS Code through its MCP server, what the assistant can do, and what it can never touch.

• The best data validation tools in 2026

  Great Expectations, Soda, dbt tests, Monte Carlo, Elementary, Catalyst and plain SQL compared: what each is good at, where it falls short, and how to choose.

• Catalyst vs dbt tests: monitoring beyond your models

  dbt tests are build-time assertions on the models dbt owns. Catalyst monitors any table on its own schedule, including the sources dbt never touches.

• Catalyst vs Great Expectations: which should you use?

  A hosted, contract-based monitor compared with an open-source Python framework: what each does well, when each is the wrong choice, and how teams run both.

• Catalyst vs Soda: data quality tools compared

  SodaCL and Soda Core against contracts in the Open Data Contract Standard — setup, connectors, anomaly detection, and when each is the wrong choice.

• How to validate data: the complete guide

  The six checks that catch most real incidents, where to run them, and how to turn ad-hoc SQL into a versioned data contract that runs on a schedule.

• The 6 dimensions of data quality, explained

  What completeness, uniqueness, validity, consistency, accuracy and timeliness each mean, with an example violation and the check that catches it.

• What is a data contract? A practical definition

  What a data contract is, what goes in one, how it differs from a schema or an SLA, and how it gets enforced, with a worked ODCS example.

• What is data validation? Definition, methods and examples

  How data validation differs from testing, observability and cleaning, the four methods that cover almost every real check, and where each of them runs.

• How to validate CSV, JSON and Excel files

  How to validate flat files: why spreadsheets break in ways databases don't, the six checks that catch the damage, and how to run your warehouse data contract against a CSV.

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

  A non-technical guide to what poor data quality costs a business: where the money goes, why an error gets roughly ten times more expensive at each stage it survives, and how to work out 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, keeping them off your ETL's WLM queue, the unenforced-constraint trap, and running them as a versioned data contract.

• How to validate data in Microsoft Fabric

  How to validate data quality in Microsoft Fabric's Warehouse and Lakehouse SQL analytics endpoint: T-SQL for the six essential checks, the metadata sync trap, and running it all as a versioned data contract.

• How to validate data in BigQuery

  How to validate data quality in Google BigQuery: the six checks every table needs, how to write them without scanning terabytes, and how to run them on a schedule as a versioned data contract.

• How to validate data in MySQL

  How to validate data quality in MySQL: the six checks every table needs, the SQL for each, and the coercion, collation and zero-date traps that let bad data slip through.

• How to validate data in PostgreSQL

  How to validate data quality in PostgreSQL: the six checks every table needs, the SQL for each, the null and locking traps to avoid, and how to turn the checks into a versioned data contract.

• How to validate data in SQL Server

  How to validate data quality in Microsoft SQL Server: the six checks every table needs, the T-SQL for each, and how to run them on a schedule as a versioned data contract.