Updated June 2026

Practical Apache Spark for Data Pipelines

Class Duration

21 hours of live training delivered over 3-5 days.

Student Prerequisites

Familiarity with Python (PySpark)
Basic data processing knowledge
Access to a GCP account with Dataproc serverless configured (provided if needed)

Target Audience

Data engineers, Python developers, and data professionals who need to develop, manage, and optimize batch and streaming data pipelines with Apache Spark 4 on GCP Dataproc serverless.

Description

The course equips participants with practical skills to develop, manage, and optimize Apache Spark 4 pipelines on GCP Dataproc serverless. By the end, attendees will understand Spark batch and streaming use-cases, master its execution model, Spark Connect architecture, and core data structures—including the VARIANT type for semi-structured data—and build reusable, performance-tuned pipelines for diverse data workloads.

Learning Outcomes

Understand use-cases and benefits of Spark Batch and Structured Streaming.
Gain working knowledge of Spark's execution model and the Spark Connect client-server architecture.
Develop reusable PySpark code for batch and streaming contexts.
Build and optimize Spark pipelines on GCP Dataproc serverless.
Master core data structures—DataFrames, ANSI-mode Spark SQL, and the VARIANT type—plus operations and performance tuning.

Training Materials

Comprehensive courseware is distributed online at the start of class. All students receive a downloadable MP4 recording of the training.

Software Requirements

Students will need access to a GCP account with Dataproc serverless configured. If students are unable to configure access, cloud environment can be provided.

Training Topics

Spark Overview

Introduction to Apache Spark and its ecosystem
What's New in Spark 4: ANSI mode, VARIANT, Spark Connect
Spark Fundamentals Overview
Pipeline Development Overview
Advanced Spark and Optimization Overview

Spark Architecture and Use-Cases

Spark topology: driver, cluster manager, worker nodes, executors
Spark Connect client-server architecture
Use-cases for Batch and Structured Streaming
Spark's role in data engineering

Core Data Structures

DataFrames and Spark SQL basics (ANSI mode by default)
VARIANT type for semi-structured data
Datasets and RDDs as legacy context
Core operations: filtering, aggregations, joins

Spark Execution Model

Partitioning
Lazy Execution
Fault Tolerance
Checkpointing
Serialization

Batch and Streaming Pipelines

Designing Batch Pipelines
Structured Streaming Fundamentals
Stateful Processing with transformWithState
Python Data Source API for Custom Connectors
Building Reusable Code Components

Advanced Features

Broadcast Variables
Accumulators
Serialization Challenges

Performance Tuning

Resource management: memory, CPU, partitioning
Adaptive Query Execution (AQE)
Optimization: caching, shuffle reduction

Case Study and Wrap-up

Discuss real-world Spark applications
Review takeaways