FITT vs. Fragile: SQL & Orchestration Techniques For FITT Data Architectures
SQL & Orchestration Techniques For Functional, Idempotent, Tested, Two-Stage Data Architectures If you're tired of your data transformations randomly breaking, producing different results for no apparent reason, or taking forever to debug when something goes...
Data Quality Test Coverage In a Medallion Data Architecture
Data quality test coverage has become one of the most critical challenges facing modern data engineering teams, particularly as organizations adopt the increasingly popular Medallion data architecture. While this multi-layered approach to data processing offers...
Critical Data Elements: Your Shortcut to Data Governance That Actually Works
The Harsh Reality of Data Governance 💥 80% of data governance initiatives fail. Not because of tools. Not because of frameworks. But because the business isn't involved, and no one agrees on what data truly matters. That's where Critical Data Elements (CDEs) change...
We’ve Been Using FITT Data Architecture For Many Years, And Honestly, We Can Never Go Back
TL;DR: Functional, Idempotent, Tested, Two-stage (FITT) data architecture has saved our sanity—no more 3 AM pipeline debugging sessions. Picture this: It's 2:47 AM, your Slack is buzzing with alerts, and the CFO's quarterly report is broken because somewhere in your...
Webinar: Test Coverage: The Software Development Idea That Supercharges Data Quality & Data Engineering
In software engineering, test coverage is non-negotiable. So why do most data teams still ship data without knowing what’s tested—and what isn’t? Explore how leading data teams are applying the proven discipline of test coverage to data and analytics—automating...
Scaling Data Reliability: The Definitive Guide to Test Coverage for Data Engineers
Scaling Data Reliability: The Definitive Guide to Test Coverage for Data Engineers The parallels between software development and data analytics have never been more apparent. Just as software teams would never dream of deploying code that has only been partially...
The Data Quality Revolution Starts with You
The Data Quality Revolution Starts with One Person (Yes, That's You!) Picture this: You're sitting in yet another meeting where someone asks, "Can we trust this data?" and the room falls silent. Sound familiar? If you're nodding along, congratulations—you've just...
When Timing Goes Wrong: How Latency Issues Cascade Into Data Quality Nightmares
When Timing Goes Wrong: How Latency Issues Cascade Into Data Quality Nightmares As data engineers, we've all been there. A dashboard shows anomalous metrics, a machine learning model starts producing bizarre predictions, or stakeholders complain about inconsistent...
Webinar: A Guide to the Six Types of Data Quality Dashboards
In this exciting webinar, Christopher Bergh discussed various types of data quality dashboards, emphasizing that effective dashboards make data health visible and drive targeted improvements by relying on concrete, actionable tests. He highlighted the importance of...
Data Quality Testing: A Shared Resource for Modern Data Teams
Data Quality Testing: A Shared Resource for Modern Data Teams In today's AI-driven landscape, where data is king, every role in the modern data and analytics ecosystem shares one fundamental responsibility: ensuring that incorrect data never reaches business...
