Scala for Spark is a practical, hands-on training program designed to help developers and data professionals build the Scala programming skills needed to develop efficient Apache Spark applications. The course combines essential Scala programming concepts with Spark development, covering functional programming, collections, functions, classes, objects, traits, pattern matching, RDDs, DataFrames, Spark SQL, and distributed data processing. Participants will gain practical experience using Scala with Spark to process large datasets, develop scalable applications, and build solutions for modern big data and analytics environments.
Course Overview:
Scala for Spark is a comprehensive, hands-on training program designed to help software engineers develop scalable data processing applications using Scala and Apache Spark. The course begins with a review of Scala programming fundamentals, including syntax, program structure, flow control, and functions, before introducing the internal architecture of Spark and its Resilient Distributed Datasets (RDDs).
Participants will learn how to build and run Spark applications, process large datasets, and work with Spark Streaming for real-time data processing. The course covers streaming architecture, fault tolerance, key-value RDDs, filtering, regular expressions, network datasets, Spark driver scripts, continuous applications, real-time tracking, linear regression, and the Spark Machine Learning Library.
The program also explores Spark cluster environments, including dependency management with SBT, Amazon EMR, RDD partitioning, logging, optimization, and Spark job management. Participants will gain practical experience integrating Spark Streaming with technologies such as Apache Kafka, Apache Flume, and Cassandra, including working with Kafka topics, custom receivers, and real-time data services.
By the end of the course, participants will be able to develop, package, deploy, troubleshoot, and optimize Scala-based Spark applications for real-time and large-scale data processing. The training provides practical knowledge of Spark development and prepares participants to apply Scala and Spark technologies in modern Big Data, streaming, machine learning, and distributed computing environments.
Course Objectives:
By the end of this course, participants will be able to:
- Develop Apache Spark applications using Scala.
- Understand Spark architecture and the fundamentals of Resilient Distributed Datasets (RDDs).
- Configure a development environment for Scala and Apache Spark.
- Process large datasets using Spark transformations and actions.
- Build applications for real-time data processing with Spark Streaming.
- Work with key-value RDDs, network datasets, and continuous data streams.
- Develop Spark driver scripts and streaming applications.
- Apply partitioning and other techniques to improve Spark application performance.
- Package and manage Spark applications using SBT.
- Run Spark applications in cluster environments, including Amazon EMR.
- Integrate Spark Streaming with Apache Kafka, Apache Flume, and Cassandra.
- Apply Spark’s Machine Learning Library to streaming and data processing tasks.
- Package and deploy applications using Spark Submit.
- Troubleshoot, debug, tune, and optimize Spark jobs and clusters.
Pre-requisites:
- Programming and scripting experience
Target Audience:
- Software Engineers
- Software Developers
- Scala Developers
- Spark Developers
- Big Data Developers
- Data Engineers
- Data Scientists
- Big Data Engineers
- Data Analysts working with large-scale data
- Backend Developers building distributed applications
- IT Professionals working with real-time data processing and streaming technologies
Course Duration:
- 21 hours – 3 days
Course Content:
Introduction
Scala Programming in Depth Review
- Syntax and structure
- Flow control and function
Spark Internals
- Resilient Distributed Datasets (RDD)
- Spark script to graph to cluster
Overview of Spark Streaming
- Streaming architecture
- Intervals in streaming
- Fault tolerance
Preparing the Development Environment
- Installing and configuring Apache Spark
- Installing and configuring the Scala IDE
- Installing and configuring JDK
Spark Streaming Beginner to Advanced
- Working with key/value RDD’s
- Filtering RDD’s
- Improving Spark scripts with regular expressions
- Sharing data on a cluster
- Working with network data sets
- Implementing BFS algorithms
- Creating Spark driver scripts
- Tracking in real time with scripts
- Writing continuous applications
- Streaming linear regression
- Using Spark Machine Learning Library
Spark and Clusters
- Bundling dependencies and Spark scripts using the SBT tool
- Using EMR for illustrating clusters
- Optimizing by partitioning RDD’s
- Using Spark logs
Integration in Spark Streaming
- Integrating Apache Kafka and working with Kafka topics
- Integrating Apache Fume and working with pull-based/push-based Flume configurations
- Writing a custom receiver class
- Integrating Cassandra and exposing data as real-time services
In Production
- Packaging an application and running it with Spark-Submit
- Troubleshooting, tuning, and debugging Spark Jobs and clusters
