• views: 291
• date: 4 March 2026
• posted by: 0nelove

2 practice tests included. Hands-on PySpark, DataFrames & Structured Streaming for the Databricks Spark Certification
What you'll learn
✓ Feel exam-ready with practice questions and topic breakdowns covering all 7 sections of the Databricks Certified Associate Developer for Apache Spark exam.
✓ Explain Spark's core architecture including driver, executors, lazy evaluation, shuffles, and the job/stage/task execution hierarchy.
✓ Manipulate Spark DataFrames using the API to filter, join, aggregate, handle missing data, and apply UDFs and Spark SQL functions.
✓ Get hands-on experience writing and running real Spark code in Databricks, reinforcing key concepts through practical exercises and examples.
✓ Understand and apply Structured Streaming in Spark to handle real-time data pipelines using windowing, watermarking, and output sinks.
Requirements
● A basic understanding of Python and SQL is recommended. No prior experience with Apache Spark or Databricks is needed
Description
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.
By the end of this course, you'll be able to
• Confidently work with Spark DataFrames, SQL, and structured streaming.
• Build, optimize, and troubleshoot data pipelines at scale.
• Apply performance tuning techniques to improve Spark workloads.
• Prepare thoroughly and confidently for the Databricks Certified Developer for Apache Spark exam.
Who this course is for
■ This course is for anyone from beginners with basic Python and SQL knowledge to experienced data professionals looking to learn Apache Spark on Databricks and earn the Databricks Certified Associate Developer certification.



   
   
   

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