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
You’re Thinking About Data Products All Wrong
When data team leaders hear “data products,” they immediately think of the stuff their team creates. But focusing on the “what” completely misses the mark. Data products aren’t about what you create, but “how” you build, maintain, and continually improve your data deliverables to your customers.
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.
Webinar: The FITT Way To Data Products: A New Data Architecture For A Product-Centric World
This webinar unveils the battle-tested FITT (Functional, Idempotent, Tested, Two-stage) data architecture that eliminates endemic burnout, constant firefighting, and hero-driven development that keeps engineers trapped in operational chaos.
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.
FITT vs. Fragile: SQL & Orchestration Techniques For FITT Data Architectures
Transform data engineering from a high-stress, “hero saves the day” kind of job into something systematic and predictable that actually scales as your team and business grow. Stop babysitting pipelines: SQL & ELT the FITT Way.
