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Book Overview & Buying
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Table Of Contents
Building Modern Data Applications Using Databricks Lakehouse
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In this chapter, we covered several techniques for implementing pipeline and data quality observability so that data teams can react as soon as problems arise and thwart major downstream disruptions. One of the major keys to becoming a successful data team is being able to react to issues quickly. We saw how alert notifications are built into many aspects of the Databricks Data Intelligence Platform and how we can configure different types of alert destinations to send notifications when conditions are not met.
We covered monitoring capabilities built into the Databricks platform, such as the pipeline event log that makes it easy for pipeline owners to query the data pipeline’s health, auditability, and performance, as well as data quality, in real time. We also saw how Lakehouse Monitoring is a robust and versatile feature that allows data teams to automatically monitor the statistical metrics of datasets and notify team members when thresholds have been crossed...