The New Rules of Data Quality

Introducing a better way to manage data quality at scale with testing and observability.

There are two types of data quality issues in this world: those you can predict (known unknowns) and those you can’t (unknown unknowns). Here’s how some of the best data teams are taking a more comprehensive approach to tackling both of them at scale. For the past several years, data teams have leveraged the equivalent of unit testing to detect data quality issues. In 2021, as companies ingest more and more data and pipelines become increasingly complex, this single point-of-failure approach doesn’t cut it any more.

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In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

Introducing a new approach to preventing broken analytics dashboards and increasing trust in your data. In this guide, you'll learn Data Observability: How to Fix Data Quality at Scale

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To trigger an alert when data breaks, data teams can leverage a tried and true tactic from our friends in software engineering: monitoring and observability. In this article, we walk through how you can create your own data quality monitors for freshness and distribution from scratch using SQL.

Data Observability: The Next Frontier of Data Engineering. Introducing a better approach to building data pipelines