Build scalable data pipelines and process large-scale datasets using Apache Spark on Databricks.

This course covers Spark architecture, DataFrame and SQL APIs, data ingestion and transformation, as well as advanced topics such as Structured Streaming and Delta Lake for batch and real-time processing workflows.

  • Why get trained: Learn how to build and optimize data pipelines using Apache Spark, DataFrame API, Structured Streaming and Delta Lake on the Databricks platform.
  • Why it matters: Spark and Databricks skills enable teams to process massive datasets efficiently and support real-time analytics, machine learning and data engineering workloads.
  • Who should attend: Data engineers, data analysts and developers working with big data who need to build scalable data processing solutions using Apache Spark and Databricks.

Build the capability to develop and optimize large-scale data processing workflows using Apache Spark on Databricks with Trainocate. HRD Corp Claimable.

Overview

This course serves as an appropriate entry point to learn Apache Spark Programming with Databricks.

Below, we describe each of the four, four-hour modules included in this course.

Introduction to Apache Spark

This course offers essential knowledge of Apache Spark, with a focus on its distributed architecture and practical applications for large-scale data processing. Participants will explore programming frameworks, learn the Spark DataFrame API, and develop skills for reading, writing, and transforming data using Python-based Spark workflows.

Developing Applications with Apache Spark

Master scalable data processing with Apache Spark in this hands-on course. Learn to build efficient ETL pipelines, perform advanced analytics, and optimize distributed data transformations using Spark’s DataFrame API. Explore grouping, aggregation, joins, set operations, and window functions. Work with complex data types like arrays, maps, and structs while applying best practices for performance optimization.

Stream Processing and Analysis with Apache Spark

Learn the essentials of stream processing and analysis with Apache Spark in this course. Gain a solid understanding of stream processing fundamentals and develop applications using the Spark Structured Streaming API. Explore advanced techniques such as stream aggregation and window analysis to process real-time data efficiently. This course equips you with the skills to create scalable and fault-tolerant streaming applications for dynamic data environments.

Monitoring and Optimizing Apache Spark Workloads on Databricks

This course explores the Lakehouse architecture and Medallion design for scalable data workflows, focusing on Unity Catalog for secure data governance, access control, and lineage tracking. The curriculum includes building reliable, ACID-compliant pipelines with Delta Lake. You’ll examine Spark optimization techniques, such as partitioning, caching, and query tuning, and learn performance monitoring, troubleshooting, and best practices for efficient data engineering and analytics to address real-world challenges.

Skills Covered

  • Introduction to Apache Spark
  • Developing Applications with Apache Spark
  • Stream Processing and Analysis with Apache Spark
  • Monitoring and Optimizing Apache Spark Workloads on Databricks

Prerequisites

  • Basic programming knowledge
  • Familiarity with Python
  • Basic understanding of SQL queries (SELECT, JOIN, GROUP BY)
  • Familiarity with data processing concepts
  • No prior Spark or Databricks experience required

Target Audience

  • Everyone who is interested

Course Curriculum

Module 1: Introduction to Apache Spark

  • Spark Runtime Architecture
  • Exploring Apache Spark Architecture in Databricks
  • Introduction to Spark DataFrames and SQL
  • Reading and Writing Data with DataFrames
  • Distributed System Programming Fundamentals
  • Basic ETL with the DataFrame API
  • Flight Data ETL with the DataFrame API
  • Analyzing Transaction Data with DataFrames

Module 2: Developing Applications with Apache Spark

  • DataFrame API Basics
  • Demo: (Optional) Basic ETL with the DataFrame API
  • Grouping and Aggregating Data
  • Demo: Grouping and Aggregating Data
  • Lab: Grouping and Aggregating E-Commerce Data
  • Relational Operations
  • Demo: Data Relational Operations in Apache Spark
  • Working with Complex Data
  • Demo: Working with Complex Data Types in Apache Spark
  • Lab: Working with Complex Data Types in E-Commerce Data

Module 3: Stream Processing and Analysis with Apache Spark

  • Introduction to Stream Processing
  • Spark Structured Streaming
  • Demo: Introduction to Spark Structured Streaming
  • Lab: Introduction to Spark Structured Streaming
  • Advanced Stream Processing and Analysis
  • Demo: Window Aggregation in Spark Structured Streaming
  • Lab: Window Aggregation in Spark Structured Streaming

Module 4: Monitoring and Optimizing Apache Spark Workloads on Databricks

  • Apache Spark and Databricks
  • Using Apache Spark with Delta Lake
  • Demo: Introduction to Delta Lake
  • Lab: Introduction to Delta Lake
  • Optimizing Apache Spark
  • Demo: Optimizing Apache Spark
  • Lab: Optimizing Apache Spark

Dates & Locations

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July 28, 2026 - July 31, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 900
RM 6750
Trainocate exam and cert

Exam & Certification

Databricks Certified Associate Developer for Apache Spark.

The Databricks Certified Associate Developer for Apache Spark certification exam assesses the understanding of the Apache Spark Architecture and Components and the ability to apply the Spark DataFrame API to complete basic data manipulation tasks within a Spark session. These tasks include selecting, renaming and manipulating columns; filtering, dropping, sorting, and aggregating rows; handling missing data; combining, reading, writing and partitioning DataFrames with schemas; and working with UDFs and Spark SQL functions.

In addition, the exam will assess the basics of the Spark architecture like execution/deployment modes, the execution hierarchy, fault tolerance, garbage collection, lazy evaluation, Shuffling and usage of Actions and broadcasting, Structured Streaming, Spark Connect, and common troubleshooting and tuning techniques. Individuals who pass this certification exam can be expected to complete basic Spark DataFrame tasks using Python.

This exam covers:

  1. Apache Spark Architecture and Components – 20%

  2. Using Spark SQL – 20%

  3. Developing Apache Spark™ DataFrame/DataSet API Applications – 30%

  4. Troubleshooting and Tuning Apache Spark DataFrame API Applications – 10%

  5. Structured Streaming – 10%

  6. Using Spark Connect to deploy applications – 5%

  7. Using Pandas API on Apache Spark – 5%

Training & Certification Guide

Frequently Asked Questions

DTB-ASPD teaches you how to build scalable big data pipelines and distributed data processing applications using Apache Spark on Databricks.

The course focuses on Spark architecture, DataFrame APIs, Spark SQL, ETL workflows, Structured Streaming, Delta Lake, and Spark optimization techniques using the Databricks Lakehouse Platform.

Key learning areas:

  • Apache Spark architecture
  • Spark DataFrame API
  • Spark SQL
  • ETL and distributed data processing
  • Structured Streaming
  • Delta Lake and Lakehouse architecture
  • Spark optimization and performance tuning

Pro Tip: Focus on understanding distributed processing concepts and Spark execution workflows rather than only learning syntax.

Apache Spark Programming with Databricks is designed for professionals working with large-scale data processing, analytics, and modern data engineering workflows.

The course is ideal for learners who want to develop scalable big data processing solutions using Apache Spark and Databricks.

Best suited for:

  • Data Engineers
  • Data Analysts
  • Python Developers
  • Analytics Engineers
  • Big Data Professionals

Prerequisites include:

  • Basic Python knowledge
  • Basic SQL knowledge
  • Familiarity with data processing concepts

Pro Tip: Strong SQL and Python fundamentals significantly improve your ability to work effectively with Spark DataFrames and transformations.

You will learn how to build, optimize, and manage scalable Apache Spark data processing workflows on Databricks.

The course emphasizes practical data engineering and distributed computing skills required for modern analytics and AI environments.

Skills gained:

  • Building ETL pipelines with Spark
  • Using Spark SQL and DataFrames
  • Processing real-time streaming data
  • Working with Delta Lake
  • Optimizing Spark workloads
  • Monitoring and troubleshooting Spark jobs

Pro Tip: Spark optimization and troubleshooting skills are highly valuable because performance tuning is critical in enterprise big data environments.

Apache Spark is a distributed data processing framework used for big data analytics, machine learning, and real-time data processing.

Spark enables organizations to process massive datasets efficiently across distributed computing clusters, making it widely used for analytics, AI, and enterprise data engineering workloads.

Common Spark use cases include:

  • ETL pipelines
  • Data engineering workflows
  • Real-time analytics
  • Machine learning pipelines
  • Stream processing

Pro Tip: Understanding distributed computing concepts is more important long-term than memorizing Spark commands alone.

Yes, DTB-ASPD aligns closely with the Databricks Certified Associate Developer for Apache Spark certification.

The certification validates practical Spark programming and distributed data processing skills using Python and Apache Spark APIs.

Exam areas include:

  • Spark architecture and components
  • Spark SQL
  • DataFrame APIs
  • Structured Streaming
  • Spark optimization and tuning
  • Spark Connect and Pandas API on Spark

Pro Tip: Hands-on Spark coding practice is essential because the certification heavily emphasizes practical implementation tasks.

Traditional database courses focus on relational databases, while DTB-ASPD focuses on distributed big data processing at scale.

Apache Spark and Databricks are designed for handling large-scale analytics, streaming, and AI workloads beyond traditional database processing capabilities.

Key comparison:

  • Traditional SQL/database courses:
    • Focus: Relational databases and transactional workloads
    • Scale: Structured enterprise databases
  • DTB-ASPD / Apache Spark:
    • Focus: Distributed analytics and big data processing
    • Scale: Massive distributed datasets and real-time workloads

Pro Tip: SQL knowledge remains important because Spark SQL is widely used in Databricks environments.

DTB-ASPD supports data engineering, big data analytics, and AI infrastructure-related roles.

Organizations increasingly require professionals who can process large-scale data efficiently for analytics, AI, and machine learning workloads.

Relevant roles:

  • Data Engineer
  • Big Data Engineer
  • Analytics Engineer
  • Data Platform Engineer
  • ETL Developer
  • AI Data Infrastructure Engineer

Databricks and Spark expertise continue growing in demand as organizations scale analytics and AI initiatives.

Pro Tip: Combining Spark expertise with cloud platforms, AI, or machine learning knowledge can significantly strengthen your long-term career opportunities.

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