Databricks Online Training | Databricks Online Course
- 1. Creating & Managing Notebooks in Databricks
- 2. Introduction to Databricks Notebooks Databricks Notebooks are interactive, web-based environments for data analysis, machine learning, and data engineering. They support multiple languages: Python, SQL, Scala, and R. Notebooks facilitate collaboration and version control.
- 3. Creating a New Notebook In the Databricks workspace, click on the “Create” button. 1. Select “Notebook” from the dropdown menu. 2. Provide a name for your notebook. 3. Choose the default language (e.g., Python, SQL). 4. Attach the notebook to an existing cluster or create a new one. 5.
- 4. Notebook Interface Overview Command Mode: Navigate and manage cells. Edit Mode: Write and modify code. Toolbar: Access options like Run, Save, and Schedule. Sidebar: Navigate between notebooks, clusters, and jobs.
- 5. Using Magic Commands Magic commands allow execution of different languages within the same notebook. Common magic commands: %python %sql %scala %sh %fs %md
- 6. Example:
- 7. Collaborating with Others Share notebooks with team members via the “Share” button. Set permissions: View, Edit, or Run. Use comments to discuss specific parts of the code.
- 8. Version Control and Revisions Databricks automatically tracks changes to notebooks. Access previous versions via the “Revision History”. Restore or compare different versions as needed.
- 9. Scheduling Notebook Jobs Automate notebook execution by scheduling jobs. Steps: Click on the “Schedule” icon. 1. Provide job details: name, schedule frequency. 2. Select the cluster to run the job. 3. Set up email notifications for job status. 4.
- 10. Managing Notebook Libraries Import external libraries using %pip install or %conda install. Manage dependencies to ensure consistent environments across notebooks. Use Databricks Repos to integrate with Git for version control.
- 11. Best Practices Organize notebooks in folders for better management. Use markdown cells for documentation. Limit the use of hard-coded paths; use variables instead. Regularly clear outputs to reduce notebook size.
- 12. Summary and Next Steps Databricks Notebooks are powerful tools for collaborative data work. Explore advanced features like widgets, parameterization, and integration with MLflow. Refer to Databricks documentation and community forums for continuous learning.
- 13. Contact & Online Training 📢We Provide Online Training on Databricks and Big Data Technologies! ✅Hands-on Training with Real-World Use Cases ✅Live Sessions with Industry Experts ✅Job Assistance ✅Certification Guidance 🌐Visit our website: https://www.accentfuture.com/ 📩For inquiries, contact us at: contact@accentfuture.com, 📞+91-96400 01789 (Call/WhatsApp)