This course is designed to prepare students for success in the Databricks Certified Developer for Apache Spark exam by providing hands-on training in building, managing, and optimizing data pipelines on the Databricks Lakehouse Platform. Whether you’re a data analyst, data engineer, or cloud analytics professional, this course will help you gain practical experience with PySpark, Spark SQL, DataFrames, structured streaming, and performance tuning, giving you the skills needed to pass the certification and work effectively with large-scale data workloads.
What You Will Learn:
Section 1: Spark Fundamentals and Databricks Essentials
- Understand the core components of Apache Spark’s execution architecture.
- Set up a Databricks account and explore notebooks for development.
- Learn Spark Core, API architecture, and Python variables in a Spark context.
- Work with DataFrames and understand transformations, actions, and lazy evaluation.
Section 2: Working with Data
- Read and write data in CSV, JSON, ORC, Parquet, and Delta formats.
- Define explicit DataFrame schemas, partition data, and optimize tables with Z-Ordering.
- Apply column transformations, filters, conditional columns, and aggregate functions.
Section 3: Advanced Data Processing
- Group, join, and combine data efficiently using Spark SQL and PySpark APIs.
- Use window functions for running totals and row numbering.
- Handle duplicates, nulls, and summary statistics for robust data pipelines.
Section 4: Performance Tuning and Optimization
- Understand Spark query execution, partitions, caching, and storage levels.
- Apply techniques like repartitioning, coalesce, and adaptive query execution (AQE).
- Explain query plans and optimize performance for large-scale workloads.
Section 5: Structured Streaming
- Build real-time data pipelines with structured streaming.
- Perform windowed aggregations, stream-to-stream and stream-to-static joins.
- Explore exactly-once semantics, fault tolerance, and output sinks in Databricks.
Section 6: Spark Connect, Deployment, and Pandas API
- Understand local, client, and cluster deployment modes for Spark applications.
- Leverage the Pandas API and Pandas UDFs for scalable data processing.
Who Should Take This Course:
Ideal for beginners, data analysts, cloud professionals, and aspiring Spark developers looking to gain hands-on experience with Apache Spark on Databricks and prepare for the certification exam. No prior Spark or Databricks experience is required.
