Replicate staging to production 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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Integration with Databricks allows for seamless collaboration and analysis of big data and machine learning tasks. Databricks, a cloud-based data engineering platform, is built on Apache Spark and offers automated cluster management and IPython-style notebooks. This integration simplifies the process of analyzing and processing large datasets, providing a collaborative environment for data scientists and engineers. By automating the detection of legacy systems and setting rules to ensure completeness, Secoda mitigates the risks associated with system and data migration, reducing the potential for manual errors and downtime. With Secoda, resources can be easily identified and tagged at scale, streamlining the migration process.
The Secoda Automations feature allows for the replication of staging data to production in Databricks. This integration consists of two components: Triggers and Actions. Triggers are responsible for initiating the workflow, and can be scheduled based on specific timeframes or customized intervals. Actions encompass various operations, such as metadata updates and filtering, and can be stacked to create customized workflows that meet the specific requirements of your team. Through Secoda, bulk updates to metadata in Databricks can be efficiently performed during the staging to production replication process.
Secoda and Databricks can be integrated to facilitate data migration from staging to production. By leveraging Databricks' capabilities and Secoda's data management platform, organizations can effectively replicate and manage their data catalog, lineage, documentation, and monitoring in a centralized manner. This integration enables seamless data migration processes and enhances overall data knowledge within the company.
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