Data Pipeline Monitoring 101
Minimize Data Downtime, Maximize Data Trust
As data becomes increasingly important to modern companies, it’s crucial for it to be trusted and accessible. This eBook will show how data engineering teams can do just that by:
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Understanding the limitations of current data monitoring and other data quality approaches
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Building a case for reducing data downtime
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Learning how to build custom data monitors with machine learning algorithms
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Evaluating commercial data pipeline monitoring and data observability solutions
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Operationalizing data pipeline monitoring
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And more!
The average organizations struggle with data quality such as:
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Suffers approximately 70 data incidents a year for every 1,000 tables in their environment.
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Data professionals are spending a whopping 40% of their time evaluating or checking data quality
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Nearly 50% of data professionals estimate their business stakeholders are impacted by issues the data team doesn’t catch most of the time, or all the time. They also estimate poor data quality impacts 26% of their companies revenue.