Live Online Databricks Course for Data Engineering

Live Course Module: Databricks Course for Data Engineering

Total Duration: 40 Hours (4 Weeks)

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WEEK 1: Introduction to Databricks and Spark Fundamentals

Duration: 8 Hours (4 Sessions × 2 Hrs)**

Topics:

1. Introduction to Databricks Platform (2 hrs)

   • What is Databricks? Architecture & components

   • Databricks vs traditional Spark

   • Workspace, clusters, notebooks, and jobs overview

   • Integration with cloud platforms (AWS, Azure, GCP)

2. Setting Up the Databricks Environment (2 hrs)

   • Creating a Databricks account and workspace

   • Cluster setup and management

   • Using Databricks notebooks (Python, SQL, Scala)

   • Working with DBFS (Databricks File System)

3. Introduction to Apache Spark in Databricks (2 hrs)

   • Spark architecture overview (Driver, Executors, Cluster Manager)

   • Spark DataFrames and Spark SQL basics

   • Transformations, actions, and lazy evaluation

4. Hands-on Lab + Mini Project (2 hrs)

   • Load and process a dataset using Spark on Databricks

   • Perform basic transformations and queries

Learning Outcomes:

✅ Understand Databricks architecture and environment setup
✅ Use Databricks notebooks and Spark DataFrames
✅ Execute basic data processing and analytics workflows

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WEEK 2: Data Engineering with Delta Lake and Spark SQL

Duration: 10 Hours (5 Sessions × 2 Hrs)**

Topics:

1. Working with Spark SQL in Databricks (2 hrs)

   • Querying structured and semi-structured data

   • Joins, aggregations, and window functions

   • Using temporary and managed tables

2. Delta Lake Fundamentals (2 hrs)

   • What is Delta Lake and why it matters

   • ACID transactions, schema enforcement, and time travel

   • Creating and managing Delta tables

3. Data Ingestion and Transformation (2 hrs)

   • Ingesting data from S3, ADLS, and GCS

   • Data cleansing and ETL using Spark APIs

   • Managing data quality and schema evolution

4. Performance Tuning in Databricks (2 hrs)

   • Partitioning, caching, and optimizing queries

   • Auto Optimize, Z-Ordering, and Delta caching

   • Cluster performance tuning

5. Mini Project + Q&A (2 hrs)

   • Build a Delta Lake pipeline with ingestion, transformation, and analytics

Learning Outcomes:

✅ Work efficiently with Spark SQL
✅ Build reliable data pipelines with Delta Lake
✅ Optimize Spark performance and storage

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WEEK 3: Workflow Orchestration and Advanced Data Pipelines

Duration: 10 Hours (5 Sessions × 2 Hrs)**

Topics:

1. Databricks Jobs and Workflows (2 hrs)

   • Scheduling and orchestrating jobs in Databricks

   • Triggers, dependencies, and multi-task workflows

   • Notifications and logging

2. ETL/ELT Pipeline Design (2 hrs)

   • Designing batch and streaming pipelines

   • Streaming data with Structured Streaming and Delta Live Tables

   • Handling incremental data and CDC (Change Data Capture)

3. Integration with Other Tools (2 hrs)

   • Databricks integration with Airflow, dbt, and Power BI

   • Connecting with Azure Data Factory / AWS Glue

   • Using REST APIs and Databricks CLI

4. Data Governance and Security (2 hrs)

   • Access control (IAM, ACLs, Unity Catalog)

   • Data lineage and audit logging

   • Managing secrets and credentials

5. Mini Project (2 hrs)

   • Create and automate a complete ETL workflow in Databricks

Learning Outcomes:

✅ Automate data pipelines using Databricks Jobs
✅ Implement streaming and batch ETL pipelines
✅ Secure and govern data workflows in enterprise environments

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WEEK 4: Advanced Analytics, ML Integration & Capstone Project

Duration: 12 Hours (6 Sessions × 2 Hrs)**

Topics:

1. Introduction to Databricks Machine Learning (2 hrs)

   • Overview of MLflow and model lifecycle management

   • Tracking experiments and managing models

2. Data Lakehouse Architecture (2 hrs)

   • Unifying data warehousing and data lakes

   • Lakehouse implementation using Delta + Databricks SQL

   • BI integrations and performance tuning

3. Databricks SQL Dashboards and BI (2 hrs)

   • Creating SQL endpoints

   • Building interactive dashboards

   • Integrating Databricks with visualization tools

4. Cost Optimization and Cluster Management (2 hrs)

   • Cluster types: All-purpose, Job, and SQL

   • Auto-scaling and cost management strategies

   • Monitoring and logging performance metrics

5. Capstone Project Development (2 hrs)

   • End-to-end data engineering project using Databricks

   • Include ingestion, transformation, Delta Lake, and dashboard layer

6. Capstone Review & Presentation (2 hrs)

   • Project presentation, peer review, and instructor feedback

   • Industry best practices and interview guidance

Learning Outcomes:

✅ Implement ML-ready data pipelines using Databricks
✅ Deploy data lakehouse architectures
✅ Build production-grade, cost-optimized Databricks workflows

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🧩 CAPSTONE PROJECT EXAMPLE

Project Title: Building a Unified Data Lakehouse on Databricks
Objective:
Develop an end-to-end cloud data engineering pipeline that ingests raw data from multiple sources (S3/ADLS), processes and stores it in Delta Lake, and builds an analytical dashboard using Databricks SQL.

Tech Stack:
Databricks, Delta Lake, Apache Spark, Airflow/dbt, MLflow, Power BI

Deliverables:

• Automated ETL/ELT pipeline

• Optimized Delta Lake architecture

• Analytical dashboard with insights