Apache Spark Development training course

A lightning-fast unified analytics engine for big data and machine learning

JBI training course London UK

"Good introduction to Apache Spark. The trainer was great at talking us through the information, specifically optimisation methods." RL, Financial Crime Technologist, Apache Spark, April 2021

Public Courses

17/08/26 - 2 days
£1495 +VAT
Enrol 
28/09/26 - 2 days
£1495 +VAT
Enrol 
09/11/26 - 2 days
£1495 +VAT
Enrol 

Customised Courses

* Train a team
* Tailor content
* Flex dates
From £1200 / day
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JBI training course London UK
  • Understand the need for Spark in data processing
  • Understand the Spark architecture and how it distributes computations to cluster nodes
  • Become familiar with basic installation/setup/layout of Spark
  • Use Spark for interactive and ad-hoc operations
  • Use DataSet/DataFrame/Spark SQL to efficiently process structured data
  • Understand the basics of RDDs (Resilient Distributed Datasets), data partitioning, pipelining and computations
  • Understand performance implications and optimisations when using Spark
  • Understand Spark's data caching and usage
  • Become familiar with Spark Graph Processing and SparkML machine learning

Course Details

Module 1 - Introduction to Spark - Getting started

  1. What is Spark and what is its purpose?
  2.  Overview, Motivations, Spark Systems
  3. Spark Ecosystem
  4. Spark vs. Hadoop
  5. Typical Spark Deployment and Usage Environments
  6. Components of the Spark unified stack
  7. Resilient Distributed Dataset (RDD)
  8. Downloading and installing Spark standalone
  9. Python overview
  10. Launching and using the Python shell 

Module 2 - Resilient Distributed Dataset and DataFrames

  1. Understand how to create parallelized collections and external datasets
  2. Work with Resilient Distributed Dataset (RDD) operations
  3. Utilize shared variables and key-value pairs
  4. RDD Concepts, Partitions, Lifecycle, Lazy Evaluation
  5. Working with RDDs - Creating and Transforming (map, filter, etc.)
  6. Caching - Concepts, Storage Type, Guidelines
  7.  Introduction and Usage
  8. Creating and Using a DataSet
  9. Working with JSON
  10. Using the DataSet DSL
  11. Using SQL with Spark
  12. Data Formats
  13. Optimizations: Catalyst and Tungsten
  14. DataSets vs. DataFrames vs. RDDs

Module 3 - Spark application programming

  1. Understand the purpose and usage of the SparkContext
  2. Initialize Spark with the Python programming language
  3. Describe and run some Spark examples
  4. Pass functions to Spark
  5. Create and run a Spark standalone application
  6. Submit applications to the cluster
  7. Overview, Basic Driver Code, SparkConf
  8. Creating and Using a SparkContext/SparkSession
  9. Building and Running Applications
  10. Application Lifecycle
  11. Cluster Managers
  12. Logging and Debugging

Module 4 - Introduction to Spark libraries

  1. Understand and use the various Spark libraries

Module 5 - Spark configuration, monitoring and tuning

  1. Understand components of the Spark cluster
  2. Configure Spark to modify the Spark properties, environmental variables, or logging properties
  3. Monitor Spark using the web UIs, metrics, and external instrumentation
  4. Understand performance tuning considerations
  5. The Spark UI
  6. Narrow vs. Wide Dependencies
  7. Minimizing Data Processing and Shuffling
  8. Caching - Concepts, Storage Type, Guidelines
  9. Using Caching
  10. Using Broadcast Variables and Accumulators

Module 6 - Spark STREAMING (optional)

  1. Overview and Streaming Basics
  2. Structured Streaming
  3. DStreams (Discretized Steams),
  4. Architecture, Stateless, Stateful, and Windowed Transformations
  5. Spark Streaming API
  6. Programming and Transformations
JBI training course London UK

Python or Java/Scala developers who need to learn about how to develop Big Data and ML solutions with Apache Spark

5 star

4.8 out of 5 average

"Good introduction to Apache Spark. The trainer was great at talking us through the information, specifically optimisation methods." RL, Financial Crime Technologist, Apache Spark, April 2021



“JBI  did a great job of customizing their syllabus to suit our business  needs and also bringing our team up to speed on the current best practices. Our teams varied widely in terms of experience and  the Instructor handled this particularly well - very impressive”

Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022

 

 

JBI training course London UK

Certification


Every delegate will be entitled to a certificate of achievement on completion of the course.

If you are missing your certificate - please use the link below to apply - you can also use this link to sign up for the JBI Training newsletter to receive technology tips directly from our instructors - Analytics, AI, ML, DevOps, Web, Backend and Security.
 



More about this course

Our Apache Spark training course provides you with a solid technical introduction to the Spark architecture and how Spark works.

You will learn the basic building blocks of Spark, including RDDs and the distributed compute engine, as well as higher-level constructs that provide a simpler and more capable interface, including Spark SQL and DataFrames.

FAQs

JBI Training offers four Apache courses covering the most widely used Apache data processing and streaming technologies. Available courses are Apache Spark Development (two days), Apache Spark 3 — Databricks Certified Associate Developer (five days), Apache Kafka Essentials (two days), and Apache Storm (two days). All courses are available as scheduled classroom sessions in London, as live online instructor-led training, or as customised onsite programmes for data engineering and platform teams.
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics and transformation workloads. It processes data in memory across a cluster of machines, making it significantly faster than older batch processing frameworks such as Hadoop MapReduce for most workloads. Spark is used for large-scale ETL and data transformation pipelines, machine learning at scale using the MLlib library, graph processing, stream processing using Spark Structured Streaming, and interactive data analysis. It is the most widely adopted distributed data processing framework in the industry and is available on all major cloud platforms including Azure Databricks, AWS EMR, and Google Dataproc.
The Apache Spark Development course is a two-day practical introduction to Spark for data engineers and developers who need to build and run Spark workloads. It covers the Spark architecture, the DataFrame API, Spark SQL, data transformation and aggregation, reading and writing data in various formats, and an introduction to Structured Streaming. The Apache Spark 3 — Databricks Certified Associate Developer course is a comprehensive five-day programme that covers Spark 3 in full depth and prepares delegates for the Databricks Certified Associate Developer for Apache Spark certification examination. It includes advanced Spark topics, Databricks-specific features, performance tuning, and certification-focused preparation. The five-day course is suited to data engineers who want a thorough grounding in Spark 3 and a recognised professional credential.
Apache Kafka is an open-source distributed event streaming platform designed to handle high-throughput, fault-tolerant, real-time data streams. It acts as a highly scalable message broker that allows applications to publish, subscribe to, store, and process streams of events in real time. Kafka is widely used for building real-time data pipelines, event-driven microservices architectures, activity tracking, operational monitoring, and stream processing applications. JBI's two-day Apache Kafka Essentials course covers the Kafka architecture and core concepts — including topics, partitions, producers, consumers, and consumer groups — setting up and configuring Kafka, producing and consuming messages, Kafka Connect for integrating with external systems, Kafka Streams for stream processing, and operational and monitoring considerations for running Kafka in production.
Apache Storm is an open-source distributed real-time computation system designed for processing unbounded streams of data with very low latency. It processes individual events as they arrive, making it well-suited to use cases that require immediate, sub-second processing of each event — such as fraud detection, real-time alerting, and financial transaction processing. Spark Structured Streaming processes data in micro-batches, introducing a small amount of latency in exchange for higher throughput and easier integration with the rest of the Spark ecosystem. The choice between Storm and Spark Streaming depends on latency requirements, existing tooling, and the nature of the streaming workload. JBI's two-day Apache Storm course covers Storm's topology model, spouts and bolts, fault tolerance, state management, and practical stream processing use cases.
The Databricks Certified Associate Developer for Apache Spark is a professional certification that validates a developer's ability to use the Spark DataFrame API, Spark SQL, and Spark's core processing capabilities at an associate level. It is widely recognised in the data engineering community and is particularly relevant for professionals working in Azure Databricks, AWS, or Google Cloud environments. JBI's five-day Apache Spark 3 — Databricks Certified Associate Developer course is specifically designed to prepare delegates for this examination, covering the full scope of the certification syllabus with hands-on exercises, practice questions, and exam technique guidance alongside comprehensive technical content.
Yes. All Apache courses at JBI can be delivered as customised onsite or online programmes for corporate data engineering and platform teams. Content and exercises can be tailored to the team's existing data stack, cloud environment, and specific use cases — for example, a team using Azure Databricks can receive Spark training focused on the Databricks environment, or a team building an event-driven microservices architecture can receive Kafka training focused on their specific integration patterns. JBI has delivered data engineering and Apache ecosystem training for teams at organisations including the BBC, NHS, RBS, Sky, EDF, and Cisco.
Yes. The Apache ecosystem evolves continuously — with regular Spark releases introducing new features to the DataFrame API, Structured Streaming, and MLlib, and ongoing Kafka developments including updates to Kafka Streams, the KRaft consensus protocol replacing ZooKeeper, and new connector capabilities. JBI's Apache training content is continuously reviewed and updated to reflect the latest stable versions of Spark and Kafka, current Databricks platform features, and evolving best practices in data engineering and real-time streaming. Delegates learn skills that are current and directly applicable to the versions and tools used in professional data engineering environments today.

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