Data Pipeline Monitoring 101

Unfortunately, poor data quality and inefficient processes are cold realities for most organizations. Consider:

The average organization suffers approximately 70 data incidents a year for every 1,000 tables in their environment.
Data professionals are spending a whopping 40% of their time evaluating or checking data quality
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.

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:

Understanding the limitations of current data monitoring and other data quality approaches
Building a case for reducing data downtime
Learning how to build custom data monitors with machine learning algorithms
Evaluating commercial data pipeline monitoring and data observability solutions
Operationalizing data pipeline monitoring
And more!

Stop missing incidents and spending precious engineering hours maintaining static tests and learn how data pipeline monitoring can help take your team to the next level by accessing this eBook today.

Minimize Data Downtime, Maximize Data Trust

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