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Data Quality Testing 101
According to a recent survey by Gartner, bad data costs organizations an average of $12.9 million per year, eroding trust, wasting resources, and valuable time otherwise spent on revenue-driving activities.
To realize the potential of data, data engineering and analytics teams need a way to easily check for common issues, from mistaken transform logic to misspelled values and even changes in the source data itself.
In this ebook, we’ll walk you through how modern data teams are getting up and running with data quality testing, the first step in the modern data reliability journey. We’ll discuss:
Reduce data quality issues, gain stakeholder trust
Stop wasting time troubleshooting broken data pipelines and get proactive about data quality.
Common use cases for data testing, including data validation in / at ingestion and schema checking
7 must-have data quality tests
Circuit breaking with Airflow and other orchestrators
Using dbt and Great Expectations to run basic data checks
How to scale data quality beyond testing by investing in data observability
And much more!
Data Quality Testing 101
Your guide to scaling data reliability across your pipelines with proper data quality checks.
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