Udemy

Databricks Certified Associate Developer - Apache Spark

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  • 15,406 Students
  • Updated 2/2025
4.4
(1,904 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
14 Hour(s) 26 Minute(s)
Language
English
Taught by
Durga Viswanatha Raju Gadiraju, Pratik Kumar, Madhuri Gadiraju, Phani Bhushan Bozzam
Rating
4.4
(1,904 Ratings)

Course Overview

Databricks Certified Associate Developer - Apache Spark

A Step by Step Hands-on Guide to prepare for Databricks Certified Associate Developer for Apache Spark using Pyspark

Master PySpark and Pass the Databricks Certification Exam with Confidence

The Databricks Certified Associate Developer for Apache Spark 2025 is one of the most sought-after certifications for Data Engineers and Big Data professionals. This exam evaluates not just your knowledge of PySpark DataFrame APIs, but also how well you can implement them in real-world data engineering projects.

This course is designed to help you prepare effectively and pass the certification exam with confidence. I have personally taken and passed this exam with a 90% score, and I will guide you through every concept you need to master.

Unlike other courses, this program provides a structured and hands-on learning experience to help you not only pass the certification but also apply PySpark concepts in real-world scenarios.

Why Take This Course?

This course stands out because it is:

  • Comprehensive and Up-to-Date: Covers all the latest topics for the Databricks Certified Associate Developer for Apache Spark 2025 exam, including Adaptive Query Execution, DataFrame APIs, and Spark Architecture.

  • Hands-On with Real-World Scenarios: Practical exercises using Databricks on Azure to solidify your understanding.

  • Structured and Exam-Focused: Avoids unnecessary theory and focuses on the key topics that will help you pass the certification exam.

  • Includes a Mock Test: Get a full-length practice test to assess your preparation and familiarize yourself with the exam format.

  • Real-World Readiness: This course goes beyond just the certification—it prepares you for real-world data engineering challenges using PySpark and Databricks.

What You Will Learn in This Course?

This course is structured to provide a step-by-step guide to preparing for the Databricks Certified Associate Developer exam, ensuring you master both theoretical concepts and practical implementation.

1. Setting Up Your Databricks Environment

  • Step-by-step setup of Databricks on Azure

  • Creating and managing Databricks Clusters

  • Uploading datasets and course materials for hands-on practice

2. Mastering PySpark DataFrame APIs

  • DataFrame Basics: Creating and manipulating DataFrames

  • Column Operations: Selecting, renaming, and transforming columns

  • Filtering & Sorting: Using PySpark APIs for filtering and sorting data

  • Aggregations: Performing group-by, aggregations, and summaries

  • Joining DataFrames: Understanding different join operations in PySpark

  • Reading and Writing Data: Working with JSON, Parquet, and Delta formats

  • Partitioning Strategies: Optimizing data storage and query performance

3. Working with User-Defined Functions (UDFs) and Spark SQL

  • Understanding User-Defined Functions (UDFs) and their use cases

  • Working with built-in Spark SQL functions for transformations

4. Apache Spark Architectural Concepts

  • Spark Execution Model and How Jobs Are Executed!!!

  • Understanding Lazy Evaluation and DAGs (Directed Acyclic Graphs)

  • Shuffling & Partitioning to optimize performance

5. Adaptive Query Execution (AQE) and Performance Optimization

  • Introduction to Adaptive Query Execution (AQE) and how it improves performance

  • Optimizing DataFrames using caching, broadcasting, and partitioning

  • Debugging and monitoring Spark jobs using Databricks UI

6. The Databricks CLI and DBFS (Databricks File System)

  • Using Databricks CLI to interact with your workspace

  • Managing files in DBFS and setting up data for practice

7. Exam Tips, Strategies, and Mock Test

  • Exam Blueprint Breakdown: Understanding exam topics and weightage

  • Time Management Tips: How to approach exam questions efficiently

  • Common Pitfalls & Mistakes: Avoiding errors that could cost you points

  • Full-Length Mock Test: Simulating the actual exam experience

How This Course is Different from Others?

This course is not just another Udemy course on Databricks certification. Here’s what makes it unique:

  • Exam-Focused, Real-World Ready: It prepares you for both the certification exam and real-world data engineering jobs.

  • Structured Learning Path: The course is designed to gradually build your knowledge, rather than jumping randomly between topics.

  • Hands-On Experience: Instead of just watching videos, you will work on real-world PySpark exercises using Databricks.

  • Preconfigured Databricks Archive: All course materials, notebooks, and datasets are provided in Databricks Archive format, making it easy for you to set up and start learning immediately.

  • Beyond the Single-Node Cluster: While we will use a Databricks Single Node Cluster for practice, we will also explore multi-node clusters to understand real-world applications.

Who Should Take This Course?

This course is perfect for:

  • Aspiring Databricks Certified Associate Developers who want to pass the certification exam.

  • Data Engineers looking to enhance their PySpark and Apache Spark skills.

  • Software Engineers and Analysts transitioning into Big Data and Data Engineering.

  • Anyone preparing for the Databricks Associate Developer certification exam and seeking a structured approach.

Whether you are new to Databricks or an experienced professional, this course will help you master PySpark DataFrame APIs and ensure you are fully prepared for the exam.

Prerequisites for This Course

This course is designed to be beginner-friendly but assumes some knowledge of:

  • Basic Python programming

  • Fundamentals of SQL

  • Basic understanding of DataFrames and structured data

If you are completely new to PySpark, don’t worry! The course starts with the basics and gradually progresses to advanced topics.

How is This Course Delivered?

  • Video Lectures: Detailed explanations with practical examples.

  • Hands-On Labs: Exercises and real-world scenarios in Databricks.

  • Quizzes & Assignments: To reinforce your learning.

  • Mock Exam: Full-length practice test with exam-style questions.

  • Downloadable Notebooks: Preconfigured Databricks Archive for easy practice.

Join Now and Start Your Databricks Certification Journey!

This course is designed to provide everything you need to pass the Databricks Certified Associate Developer for Apache Spark 2025 exam with confidence.

By the end of this course, you will not only be prepared for the certification exam but also gain real-world skills that you can apply immediately in a data engineering role.

Enroll now and take the next step in your career with Databricks and PySpark!

Course Content

  • 10 section(s)
  • 172 lecture(s)
  • Section 1 Getting Started with Databricks Certified Associate Developer for Apache Spark
  • Section 2 Setup Databricks Environment using Azure
  • Section 3 Create Spark Dataframes using Python Collections and Pandas Dataframes
  • Section 4 Selecting and Renaming Columns in Spark Data Frames
  • Section 5 Manipulating Columns in Spark Data Frames
  • Section 6 Filtering Data from Spark Data Frames
  • Section 7 Dropping Columns from Spark Data Frames
  • Section 8 Sorting Data in Spark Data Frames
  • Section 9 Performing Aggregations on Spark Data Frames
  • Section 10 Joining Spark Data Frames

What You’ll Learn

  • Databricks Certified Associate Developer for Apache Spark exam details
  • Setting up Databricks Platform for practice to also to prepare for Databricks Certified Associate Developer for Apache Spark Exam
  • Selecting, renaming and manipulating columns using Spark Data Frame APIs
  • Filtering, dropping, sorting, and aggregating rows using Spark Data Frame APIs
  • Joining, reading, writing and partitioning DataFrames using Spark Data Frame APIs
  • Working with UDFs and Spark SQL functions using Spark Data Frame APIs
  • Spark Architecture and Adaptive Query Execution (AQE)


Reviews

  • A
    Aadarsh Jain
    5.0

    It is cleared my confusion and now I can answer question confidently.

  • A
    Ankur Gujral
    3.0

    Material is good but the speaker is too fast in explaining. If the assumption is that people should be aware of git cloning/dbfs etc. already, it should be called out at the onset.

  • V
    Vinod Kushwah
    2.0

    It’s incomplete course, it needs to be updated. And instructor took 14 hours and repeats a lot, after sometime a person can get irritated with his voice.

  • R
    Rohan Prakash Kadam
    5.0

    Well Explained!

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