Data Engineering with Databricks and Spark

Course Description:

The Data Engineering with Databricks and Spark course is a comprehensive 16-hour training program designed to equip participants with the skills needed to design, build, and manage robust data pipelines and ETL workflows using Databricks and Apache Spark. This course provides hands-on experience in working with Spark’s RDDs, DataFrames, and Datasets, as well as advanced topics like structured streaming, Delta Lake, and data governance in the Lakehouse architecture.
Participants will explore how to implement scalable and efficient data pipelines, process large volumes of data in real-time, and optimize data workflows. The course also focuses on productionizing dashboards, implementing security best practices, and using Databricks jobs for orchestration.
By the end of the course, participants will be able to:
1. Understand the Spark and Databricks ecosystem, including its architecture and key features.
2. Work with RDDs, DataFrames, and Datasets for data processing and transformations.
3. Build scalable ETL pipelines using Spark SQL and Structured Streaming.
4. Implement Delta Lake for ACID transactions, schema evolution, and streaming ingestion.
5. Utilize the Medallion architecture for organizing data in the Lakehouse.
6. Configure and manage task orchestration with Databricks Jobs.
7. Apply role-based access control (RBAC) and secure data in the Lakehouse.
8. Design and productionize dashboards and queries using Databricks SQL.

Prerequisites:

This course is designed for intermediate-level learners. Participants should have:
1. Basic programming knowledge, preferably in Python or Scala.
2. Familiarity with SQL for querying structured data.
3. Understanding of data processing concepts and tools.
4. Knowledge of distributed computing and big data concepts (optional but helpful).

Audience Profile:

This course is ideal for:
1. Data Engineers: Professionals building and managing data pipelines and workflows.
2. Big Data Developers: Individuals working on Spark-based data processing solutions.
3. Data Scientists: Those seeking to enhance their data engineering skills for advanced analytics.
4. IT Professionals: Teams exploring the Databricks platform for enterprise data solutions.
5. Students and Enthusiasts: Learners interested in big data and modern data architectures.

Course Duration: 16 hours