Integrate Databricks with your workflow to automate data engineering and machine learning tasks. Connect your lakehouse platform to streamline analytics and AI model deployment.
The connector is listed in the integration directory. Example actions require a reviewed build and may need additional provider permissions or setup.
Enable seamless automation between Databricks and your tools to orchestrate data pipelines, manage clusters, and deploy ML models without manual intervention.
Connect your Databricks workspace. Authenticate your Databricks account using secure API tokens to establish a connection between your workspace and automation platform for seamless data operations.
Trigger workflows from data events. Set up triggers based on job completions, cluster status changes, or notebook executions to initiate automated workflows across your connected applications and tools.
Automate cluster management. Create, start, stop, or terminate clusters based on workload demands or schedules to optimize resource utilization and reduce operational costs without manual oversight.
Execute notebooks programmatically. Run Databricks notebooks on demand or on schedule, passing parameters and collecting results to integrate data transformations into your broader automation workflows.
Schedule and manage jobs. Configure, trigger, and monitor Databricks jobs through automated workflows, ensuring data pipelines run reliably and results are delivered to downstream systems on time.
Sync data between systems. Move processed data from Databricks to warehouses, applications, or analytics tools, keeping all systems synchronized with the latest insights and transformations.
Monitor job status and performance. Track job execution status, collect performance metrics, and receive notifications about failures or anomalies to maintain reliable data operations across your organization.
Deploy and manage ML models. Automate the deployment of machine learning models from Databricks to production environments, updating endpoints and versioning models as your data science team iterates.
Create end-to-end data workflows that trigger Databricks jobs when new data arrives, process information through notebooks, and deliver results to analytics dashboards or business applications without manual intervention.
Build workflows that train models in Databricks, evaluate performance metrics, and deploy successful models to production endpoints while notifying teams through communication tools of each deployment milestone.
Implement automated cluster lifecycle management that spins up resources based on workload schedules, processes data efficiently, and terminates clusters during idle periods to optimize infrastructure spending.
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Describe what you need. Cody handles the build, the connections, and the deployment.