Duration: 40 hours / 5 days

10 Lessons

Day 1: Introduction to Databricks and Apache Spark

Day 2: Databricks Architecture and Components

Day 3: Spark RDD Transformations and Actions / Spark Fundamentals / Transformations (Hands on Session)

Day 4: Actions (Hands On Session) / Hands On Session: Basic Word Count Application

Day 5: Spark SQL and Databricks SQL / Data Manipulation and Processing

Day 6: NSE Case Study

Day 7-8: Spark Streaming and Structured Streaming

Day 9: Delta Lake Case Study

Day 10: Memory Optimization or Performance Optimization

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Duration: 5 Days

19 Lessons

This bootcamp is designed for data engineers seeking advanced skills in building and managing data pipelines using Databricks. Participants will learn how to leverage Databricks’ Unified Analytics Platform to perform data engineering tasks efficiently, including data ingestion, transformation, orchestration, and optimization.

DAY 1: INTRODUCTION TO DATABRICKS AND SPARK

Module 1: Introduction to Databricks

Module 2: Introduction to Apache Spark

Module 3: Data Ingestion in Databricks

Module 4: Data Exploration and Transformation

DAY 2: ADVANCED DATA ENGINEERING TECHNIQUES

Module 1: Advanced Spark SQL Operations

Module 2: Data Partitioning and Optimization

Module 3: Introduction to Delta Lake

Module 4: Orchestrating Workflows with Databricks Jobs

DAY 3: DATA ENGINEERING BEST PRACTICES

Module 1: Introduction to Structured Streaming

Module 2: Managing Data Pipelines with MLflow

Module 3: Data Engineering Best Practices

Modules 4: Real-World Data Engineering Use Cases

DAY 4: ADVANCED TOPICS IN DATABRICKS

Module 1: Introduction to Machine Learning Pipelines in Databricks

Module 2: Advanced Databricks Features

Module 3: Scaling Data Engineering Workloads

Module 4: Monitoring and Performance Tuning

DAY 5: CAPSTONE PROJECT AND FINAL ASSESSMENT

Module 1: Capstone Project

Module 2: Project Presentations and Evaluation

Module 3: Course Conclusion and Certification

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