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: Mitch Haile
Effective Data Monitoring Requires a Relative Baseline

Effective Data Monitoring Requires a Relative Baseline

Recently I was talking to a customer who had used a competitor’s product for data monitoring. It sounded like the product had the users specify parameters about the data; e.g., “this column should never be null.” This is all well and good, except that the product then...

The Data Quality Hierarchy of Needs

The Data Quality Hierarchy of Needs

Just as Maslow identified a hierarchy of needs for people, data teams have a hierarchy of needs, beginning with data freshness; including volumes, schemas, and values; and culminating with lineage. In this blog post, which was published in the Data Science area of the...

Validating Data for Pipelines with Data Culpa

Validating Data for Pipelines with Data Culpa

Consistent pipeline behavior is critical for any data process. You can use Data Culpa Validator to ensure consistent operation of pipelines as well as data at rest in databases, data lakes, and data warehouses. This introduction shows you how to validate your results...

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