Lilly Tech Systems
Databricks for Enterprise
Master the Databricks Lakehouse Platform for enterprise-scale data engineering, machine learning, and AI. Learn workspace management, Unity Catalog governance, MLflow experiment tracking, and Mosaic AI for generative applications.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is Databricks? Understand the Lakehouse architecture, key components, and why enterprises choose Databricks.
2. Workspace
Databricks workspace setup, clusters, notebooks, jobs, and workspace administration for enterprise teams.
3. Unity Catalog
Unified data governance with Unity Catalog — access controls, data lineage, and multi-cloud metadata management.
4. MLflow on Databricks
Experiment tracking, model registry, model serving, and end-to-end ML lifecycle management with managed MLflow.
5. Mosaic AI
Mosaic AI Model Training, Agent Framework, Vector Search, and building enterprise generative AI applications.
6. Best Practices
Cost optimization, security hardening, performance tuning, and production architecture patterns for Databricks.
What You'll Learn
By the end of this course, you'll be able to:
Lakehouse Architecture
Design and implement enterprise data lakehouses combining the best of data warehouses and data lakes.
Data Governance
Implement centralized governance with Unity Catalog for access control, lineage, and compliance.
ML at Scale
Build, track, and deploy ML models using managed MLflow with experiment tracking and model serving.
Generative AI
Leverage Mosaic AI for foundation model training, RAG applications, and AI agent development.