ABOUT THIS BLOG

In this blog, we’ll discuss all things data quality and data observability. We’ll present interviews with leading data scientists, and we’ll discuss our own working building data observability solutions for data engineers and data scientists working in a broad range of industries.

Displaying Posts From: John Bennett
Five Neat Tricks with Data Culpa Alerts

Five Neat Tricks with Data Culpa Alerts

Every data team needs to keep an eye on its data. At the same time, no data team wants to be deluged with alerts.  Ideally, alerts should direct your attention to the most important changes taking place in your data. If they call attention to every change, you’ll...

Where Most Data Observability Solutions Fall Short

Where Most Data Observability Solutions Fall Short

Observability is the analysis of a system based on its outputs. By analyzing the outputs of a system at various points, it should be possible to infer the internal state of the system and to diagnose problems the system is experiencing. This sounds useful for data...

Introducing Data Culpa Validator

Introducing Data Culpa Validator

What’s your data doing? Can you tell? Data teams tell us they need better visibility into data pipelines, integrations, repositories, and data lakes. You can hand-code a bunch of unit tests to check for known boundary cases. But coverage will always be limited. And...

A Blog about Data Quality

A Blog about Data Quality

At Data Culpa, we're building solutions to help data engineers and data scientists catch data quality problems before they jeopardize data pipeline results. Nearly every business today is becoming more data-centric. Data not only drives operations and decision making;...

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