Module 1: Introduction to Databricks and Lakehouse Architecture

• Data lakehouse concept: combining data lake flexibility with data warehouse reliability
• Databricks platform architecture: workspaces, clusters, and jobs
• Databricks on AWS, Azure, and GCP: deployment and key differences
• Navigating the Databricks UI: workspace, compute, workflows, and Delta Lake

Module 2: Apache Spark on Databricks

• Spark cluster configuration: driver, executors, and autoscaling
• Databricks Runtime versions and ML runtime selection
• Spark DataFrames: reading, transforming, and writing data
• Catalyst optimizer and Tungsten execution engine internals

Module 3: Delta Lake on Databricks

• Delta Lake architecture: transaction log, checkpoints, and Parquet files
• CRUD operations on Delta tables: merge, update, and delete
• Time travel: querying historical data with version and timestamp
• Delta table optimisation: OPTIMIZE, ZORDER, and VACUUM commands

Module 4: Databricks SQL

• Databricks SQL warehouses: serverless and classic configurations
• Writing and running SQL queries in the Databricks SQL editor
• Creating and managing Delta tables with SQL DDL
• Databricks SQL dashboards and visualisation tools

Module 5: Machine Learning on Databricks

• MLflow on Databricks: experiment tracking and model registry
• Feature Store: creating and serving ML features
• AutoML: automated model training and evaluation
• Deploying models with Model Serving endpoints

Module 6: Data Engineering Workflows

• Databricks Workflows: job creation and multi-task orchestration
• Delta Live Tables (DLT): declarative ETL pipeline development
• DLT expectations: data quality constraints and monitoring
• Photon engine: accelerated query execution for ETL workloads

Module 7: Unity Catalog and Data Governance

• Unity Catalog: centralised governance for data and AI assets
• Three-level namespace: catalog, schema, and table structure
• Fine-grained access control: table, column, and row-level security
• Data lineage: tracking data flow across tables and notebooks

Module 8: Capstone Project and Assessment

• End-to-end lakehouse pipeline: ingestion, Delta tables, and serving
• Delta Live Tables ETL pipeline with data quality expectations
• MLflow model training, registration, and serving project
• Final assessment and course certification

Experience in the Industry Learn from Databricks-certified data engineers and ML engineers who have built production lakehouse platforms, Delta Live Tables pipelines, and ML workflows on Databricks for data-driven enterprises.

Backgrounds at the Top Our Databricks trainers have delivered data platform projects at technology companies, financial services firms, and healthcare organisations where Databricks is the central platform for data engineering and machine learning.

Clear & Effective Teaching Databricks architecture, Spark DataFrames, Delta Lake, Databricks SQL, MLflow, Delta Live Tables, Unity Catalog, and governance are explained clearly with real lakehouse data platform examples.

Hands-On Learning Focus Students build end-to-end lakehouse pipelines, work with Delta tables, create DLT pipelines, track ML experiments with MLflow, and configure Unity Catalog governance through structured lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest Databricks platform releases, Databricks AI and GenAI capabilities, Unity Catalog enhancements, and evolving lakehouse architecture best practices.