Connect Great Expectations and Databricks

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Great Expectations and Databricks

What are the benefits of connecting Great Expectations and Databricks

Connecting Databricks to Great Expectations can provide numerous benefits, such as easily validating, monitoring and profiling data on an automated basis, having the ability to create automated data pipelines with data engineering and data science libraries, and being able to easily create and integrate advanced visualizations.

How to connect Great Expectations and Databricks

Great Expectations and Databricks can be connected through the utilization of the Great Expectations library for Apache Spark. This library allows users to seamlessly integrate data quality testing into their Databricks workflow by automating the execution of expectations and validating the results within Databricks. Through this connection, users can easily assess and improve their data quality.

What are the benefits of connecting Great Expectations and Databricks in a data catalog

Connecting Great Expectations and Databricks to Secoda will allow users to quickly find and understand data, improve data governance and build trust in data. Additionally, users will be able to quickly identify data errors, reduce data latency and improve data quality.

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