Databricksv1.0.2

Monitor and manage Databricks clusters and jobs to ensure data pipelines run smoothly. Automatically start or stop clusters based on workload schedules and check job run statuses for failures or delays.

Use Databricks SQL endpoints to query large datasets and generate reports. List tables and run notebooks to analyze data trends, supporting decision-making with up-to-date insights.

Manage MLflow experiments and registered models within Databricks for machine learning workflows. Track model versions and experiments to streamline development and deployment processes.

Organize and list files in DBFS (Databricks File System) to maintain data storage. Use this for auditing storage usage or preparing datasets for processing in data lakes.

Set up and manage Databricks workspaces, including Git repos and notebooks, for team collaboration. List workspace objects and update Git repos to synchronize code changes across projects.

Offer a subscription-based service where customers use Databricks through Membrane for data analytics. Provide managed access to clusters and jobs, charging based on usage or tiered plans.

Provide consulting to help businesses integrate Databricks with their systems using Membrane. Offer setup, customization, and ongoing support for data workflows and automation.

Deliver processed data insights to clients by leveraging Databricks for data transformation and analysis. Use Membrane to automate data pipelines and ensure secure, scalable delivery.