Databricks Certified Associate Developer for Apache Spark 4
Published 2/2026
Created by Ansh Lamba JSR
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch Level: All Levels | Genre: eLearning | Language: English | Duration: 173 Lectures ( 27h 59m ) | Size: 26.3 GB

[2026] The COMPLETE Apache Spark 4.0 Hands-On Guide - Master PySpark, Spark SQL, Structured Streaming, and Optimization

What you'll learn
✓ Understand Spark Architecture and its components - Driver, Executors, Cluster Manager, and how they interact.
✓ Learn about Spark's lazy evaluation model, DAG execution, Catalyst optimizer, Stage and Task execution, and how to monitor Spark applications using the Spark UI
✓ Master PySpark DataFrames and Manipulation techniques including filtering, grouping, exploding arrays, window functions, and advanced aggregations.
✓ Perform joins (inner, left, broadcast, multi-key, anti), unions, and complex transformations such as pivoting, handling nested JSON data, & nested arrays,.
✓ Create Broadcast Variables, Accumulators & User Defined Functions (UDFs & Pandas UDFs).
✓ Execute SQL queries directly on files (CSV, JSON, PARQUET, DELTA & more) and understand all Spark save modes (append, overwrite, and partitioning by column).
✓ Learn how to register DataFrames as temporary views, and work confidently with Date & Time functions including Unix epoch conversions.
✓ Tune Spark performance using partitioning, repartition, coalesce, AQE, shuffle optimization and Salting.
✓ Understand Driver OOM and Executor OOM errors, how executor memory pool works.
✓ Gain insights into Spark Garbage Collection, storage levels like MEMORY_ONLY, DISK_ONLY, caching strategies.
✓ Explore the window operations in streaming data such as tumbling, sliding, and session.
✓ Spark Connect and Deployment modes (local, cluster, client), and how to choose the right one for your application.
✓ Pandas API on Spark and how to use vectorized UDFs for performance optimization.
✓ Delta Lake Optimizations including Z-ordering, data skipping, Liquid Clustering for faster queries.
✓ Build real-time pipelines using Structured Streaming with watermarking & exactly-once guarantees, along with Trigger Types and ForEachBatch operations.

Requirements
● Basic Python knowledge will be required.
● Basic SQL knowledge will be required.
● No prior knowledge in Apache Spark is required (Will cover everything from SCRATCH)

Description
This is a COMPLETE Apache Spark 4.0 Bootcamp you need in 2026 to become a PRO Spark Developer.

Whether you're a beginner or a working professional looking to upskill, this course will guide step by step with a hands-on, practical, and engaging lectures (doodle illustrations).

GAIN STRONG HANDS-ON WITH

• Spark Architecture & Components - Understand how Driver, Executors, and Cluster Manager work together behind the scenes. Learn DAG execution, lazy evaluation, Catalyst optimizer, stages, tasks & how to monitor everything using Spark UI.

• PySpark DataFrames & Manipulation - Master filtering, grouping, joins, window functions, exploding arrays, nested JSON handling, pivoting, advanced aggregations and complex transformations.

• SparkSQL - Run SQL queries directly on CSV, JSON, Parquet, Delta & more. Work with temp views, save modes, partitioning, date-time functions & Unix epoch conversions like a pro.

• Memory Management & Garbage Collection - Understand how executor memory pool works and why Driver/Executor OOM errors happen. Learn storage levels (MEMORY_ONLY, DISK_ONLY), caching strategies & Spark Garbage Collection in depth.

• Performance Tuning & Optimization - Learn partitioning, repartition, coalesce, AQE, broadcast joins, bucketing, shuffle optimization and Salting techniques to boost your Spark application's performance.

• Structured Streaming - Build real-time pipelines with watermarking & exactly-once guarantees. Implement tumbling, sliding & session windows along with triggers and ForEachBatch operations.

• Spark Connect & Deployment Modes - Understand Spark Connect and how it changes client-server communication. Learn local, client & cluster deployment modes and how to choose the right one for real-world projects.

• Pandas API on Spark - Use Pandas API on Spark for scalable data processing with familiar Pandas syntax. Learn vectorized UDFs & Pandas UDFs to boost performance efficiently.

WHAT MAKES THIS COURSE UNIQUE?

• Super Engaging Lectures - No boring theory here! I explain every concept in a clear and beginner-friendly way using real-life examples and doodle visuals.

• Deep Dive into Every Topic - I don't just scratch the surface. You'll understand the "why" and "how" behind every feature.

• Strong Hands-On Focus - You learn by doing. Each chapter includes so many practical labs to solidify your understanding and build real-world skills.

DISCLAIMER - This course is independently created and not affiliated with or endorsed by Databricks Inc. All content is original, designed for educational purposes only, and does not include real certification exam questions. It is based on public documentation, real-world scenarios, and personal experience. All trademarks belong to their respective owners. For the most accurate and updated information, refer to the official Databricks documentation.

Who this course is for
■ Anyone who wants to learn Apache Spark and become a Spark Developer

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