What to do when your team doesn’t care about data errors in production? The “deploy and forget” ostrich mindset is one of the most corrosive patterns in data engineering teams. Here’s How to Change That.
Peter Piper on the Four Ps of AI Data Quality: Purge, Patch, Push Back, or Pass
How does a data team prevent poor data from poisoning AI when they have piles of raw and imperfect data?
The 2026 Open Source Data Profiling Software Landscape
Data profiling has re-emerged as an essential first step in protecting AI-driven organizations from data-induced failures. Most open-source profiling tools stop at describing data; almost none automatically convert profiling insights into actionable data hygiene checks.
The 2026 Data Quality and Data Observability Commercial Software Landscape
With 50+ vendors to choose from, data quality and data observability software has never been more powerful, more plentiful, or more confusing—until now.
Sure, Go Ahead And Feed That Data To The LLM … What Could Possibly Go Wrong?
Welcome to Analysis-A-Palooza: The Festival No Data Engineer Asked For
Webinar: Data Quality, DataOps, and Large Language Models
AI is changing the world — in this webinar we show how Large Language Model drive the need for DataOps, Data Quality, and Data Observability
The 2026 Open-Source Data Quality and Data Observability Landscape
We explore the new generation of open source data quality software that uses AI to police AI, automate test generation at scale, and provides the transparency and control—all while keeping your CFO happy.
DataOps Data Quality TestGen Expands: Now Supporting BigQuery and Apache Iceberg
DataOps TestGen Enterprise is now compatible with Google BigQuery and can be used to profile and test file-based data accessible through Redshift Spectrum and Snowflake external tables using Apache Iceberg and other file formats.
Process Guardianship: The Most Valuable Data Engineering Work You’re Probably Not Doing
When people think of data engineers, the description usually stops at “building high-quality pipelines that deliver analyst-ready data.” That is true, but incomplete. In modern organizations, data engineers hold a deeper responsibility. They are not just the builders of pipelines—they are the curators of the business logic itself.
Flip the Script on Data Quality: Shift Left, Shift Down, and Take Control
The manufacturing industry learned decades ago that catching defects early in the production process saves exponentially more money than fixing them after products ship. Today’s data engineering teams face a strikingly similar challenge.
