Verify Data in Databricks

Verify Data in Databricks with Secoda. Learn more about how you can automate workflows to turn hours into seconds. Do more with less and scale without the chaos.

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Overview

One way to ensure data governance at scale is by verifying data found in Databricks through Secoda. Databricks is a cloud-based data engineering platform that offers a collaborative environment for big data and machine learning. It is built on Apache Spark and provides automated cluster management and IPython-style notebooks, facilitating the analysis and processing of large datasets. By verifying resources in Databricks, such as metrics, dictionary terms, documents, and tables, end-users can have confidence in using the best source for their work. Additionally, datasets can be automatically tagged as 'audit-verified' when changes are recorded and verified against governance policies, allowing for effective audit purposes.

How it works

Verifying data in Databricks is a fundamental aspect of ensuring data quality and reliability within a unified analytics platform. Leveraging Databricks' robust capabilities, data engineers and analysts can implement various validation techniques to confirm the accuracy, consistency, and completeness of their datasets. This verification process often involves employing SQL queries, Python, or Scala code to perform data checks, ranging from simple row counts to complex integrity validations and anomaly detection.

Databricks allows users to integrate with external libraries and tools for advanced data quality assessment, such as Apache Spark's DataFrame API and MLflow for machine learning-based data validation. By verifying data in Databricks, organizations can enhance the trustworthiness of their analytical insights, empowering stakeholders to make data-driven decisions with confidence.

Integration with Databricks allows users to verify data through Secoda. The Automation feature consists of Triggers and Actions. Triggers enable the scheduling of workflows, allowing for regular or custom activations. Actions encompass various operations such as filtering and updating metadata. These actions can be stacked to create customized workflows based on specific team requirements. Secoda integration facilitates bulk updates to metadata in Databricks.

About Secoda

Secoda's integration with Databricks allows users to verify data through the platform. By consolidating data catalog, lineage, documentation, and monitoring, Secoda serves as a comprehensive data management solution. With its AI-powered data governance capabilities, Secoda seamlessly integrates with Databricks, providing users with a reliable and efficient way to ensure data accuracy and quality.

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