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Home/Courses/AI & Data Science/Apache Spark using Scala

AI & Data Science

Apache Spark using Scala

This course provides an in-depth understanding of Apache Spark using Scala, Spark’s native language.

Duration5 days

DaysMonday to Friday

TimingMorning or evening

LocationAbu Dhabi centre

Next group

2026 & 2027

Course fee: Contact us
Duration: 5 days
Group rate: Ask an advisor

Overview Objectives Course outline Who should attend

Course overview

This course provides an in-depth understanding of Apache Spark using Scala, Spark’s native language. Participants will learn Spark's core architecture, RDDs, DataFrames, Spark SQL, structured streaming, and machine learning with MLlib, focusing on how Scala enhances Spark’s performance and scalability. Hands-on labs will cover building efficient big data pipelines, optimizing Spark jobs, and deploying real-world applications.

Training methodology

Hands-on Coding Labs – Writing and executing Spark applications in Scala

Live Demonstrations – Showcasing Spark’s capabilities with real-world data

Use Case Studies – Applying Spark in business and industry scenarios

Collaborative Learning – Group problem-solving and interactive discussions

Capstone Project – Implementing an end-to-end Spark-based big data pipeline

Target competencies

Apache Spark’s Core Components & Execution Model
Working with Spark RDDs, DataFrames & Spark SQL
Distributed Data Processing using Scala in Spark
Performance Optimization & Job Execution Tuning
Real-Time Streaming with Spark Structured Streaming
Machine Learning with Spark MLlib
Deploying & Managing Spark Applications in Production

Course objectives

By the end of the course, participants will be able to:

Understand Apache Spark’s architecture, components, and Scala’s role
Implement Spark transformations and actions using Scala
Process structured and semi-structured data using DataFrames and Spark SQL
Optimize Spark jobs for efficient performance
Develop machine learning models using MLlib
Stream and process real-time data using Spark Structured Streaming
Deploy Spark applications on cloud and on-premises clusters

Course outline

Module 1

Module 1: Introduction to Apache Spark & Scala

Overview of Big Data & Distributed Computing
What is Apache Spark? – Evolution, Use Cases, and Benefits
Spark Ecosystem: Core, SQL, Streaming, MLlib, GraphX
Setting up Scala Development Environment for Spark
Introduction to Scala Syntax, Data Types, and Functional Programming

Module 2

Module 2: Apache Spark Architecture & Execution Model

Understanding Spark Cluster Components – Driver, Executors, Tasks
RDDs vs. DataFrames vs. Datasets – Key Differences
Spark Execution Model: Transformations & Actions
Memory Management & Data Partitioning in Spark
Deploying Spark Applications in Local & Cluster Mode

Module 3

Module 3: Working with Spark RDDs in Scala

Introduction to Resilient Distributed Datasets (RDDs)
Creating, Transforming, and Persisting RDDs
Lazy Evaluation & Fault Tolerance in Spark
Common RDD Operations: map, filter, flatMap, reduceByKey
Performance Optimization: Shuffling, Partitioning, and Caching

Module 4

Module 4: DataFrames & Spark SQL in Scala

Introduction to DataFrames & Datasets
Creating DataFrames from CSV, JSON, Parquet & JDBC
Performing Data Transformations and Aggregations
Using Spark SQL for Querying Structured Data
Performance Optimization using Catalyst Optimizer

Module 5

Module 5: Advanced Data Engineering with Apache Spark

Partitioning & Bucketing for Performance Optimization
Handling Schema Evolution & Nested Data Structures
Integration with Cloud Storage (AWS S3, Azure Blob, Google Cloud Storage)
Managing Data Workflows with Apache Airflow & Apache Kafka

Module 6

Module 6: Machine Learning with Spark MLlib in Scala

Introduction to Spark MLlib for Scalable Machine Learning
Feature Engineering & Data Preprocessing in Spark
Training & Evaluating ML Models
Building End-to-End ML Pipelines in Spark
Hyperparameter Tuning & Model Deployment

Module 7

Module 7: Real-Time Data Processing with Spark Streaming

Introduction to Spark Structured Streaming
Streaming Data Sources – Kafka, AWS Kinesis, Azure Event Hubs
Stateful & Event-Time Processing with Structured Streaming
Handling Late Arriving & Out-of-Order Data
Real-World Streaming Use Cases – Fraud Detection, IoT Analytics

Module 8

Module 8: Performance Optimization & Debugging in Spark

Understanding Spark UI & Job Execution Plan
Best Practices for Memory Management & Shuffle Optimization
Broadcast Variables & Accumulators for Performance
Debugging Spark Applications & Handling Failures
Optimizing Scala Code for Spark Performance

Module 9

Module 9: Deploying Apache Spark Applications

Running Spark on Standalone, YARN, and Kubernetes Clusters
Deploying Spark Applications on AWS EMR, Azure HDInsight, GCP Dataproc
CI/CD Pipelines for Spark Applications
Using Databricks for Scalable Spark Deployments

Course at a glance

Duration5 days

Modules9

LanguageEnglish / Arabic

FormatClassroom

TimingMorning or evening · Mon–Fri

You'll be able to

Understand Apache Spark’s architecture, components, and Scala’s role
Implement Spark transformations and actions using Scala
Process structured and semi-structured data using DataFrames and Spark SQL

See all 7 objectives →

Who should attend

Big Data Engineers working on distributed data processing

Scala Developers looking to integrate Apache Spark

Data Scientists needing scalable ML workflows

Software Engineers developing real-time analytics solutions

Cloud & DevOps Professionals working with Spark clusters

Data Analysts handling massive datasets in Spark

Training a team?

Run this course at your offices

We deliver it in-house across the UAE, on dates that fit your operations.

Get an in-house quote →

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AI & Data Science

Apache Spark using Scala

This course provides an in-depth understanding of Apache Spark using Scala, Spark’s native language.

Duration5 days

DaysMonday to Friday

TimingMorning or evening

LocationAbu Dhabi centre

COMPUBASE TRAINING CENTER ABU DHABI

AI & Data Science courses
Course overview This course provides an in-depth understanding of Apache Spark using Scala, Spark’s native language. Participants will learn Spark's core architecture, RDDs, DataFrames, Spark SQL, structured streaming, and machine learning with MLlib, focusing on how Scala enhances Spark’s performance and scalability. Hands-on labs will cover building efficient big data pipelines, optimizing Spark jobs, and deploying real-world applications. Training methodology Hands-on Coding Labs – Writing and executing Spark applications in Scala Live Demonstrations – Showcasing Spark’s capabilities with real-world data Use Case Studies – Applying Spark in business and industry scenarios Collaborative Learning – Group problem-solving and interactive discussions Capstone Project – Implementing an end-to-end Spark-based big data pipeline Target competencies Apache Spark’s Core Components & Execution Model Working with Spark RDDs, DataFrames & Spark SQL Distributed Data Processing using Scala in Spark Performance Optimization & Job Execution Tuning Real-Time Streaming with Spark Structured Streaming Machine Learning with Spark MLlib Deploying & Managing Spark Applications in Production Course objectives By the end of the course, participants will be able to: Understand Apache Spark’s architecture, components, and Scala’s role Implement Spark transformations and actions using Scala Process structured and semi-structured data using DataFrames and Spark SQL Optimize Spark jobs for efficient performance Develop machine learning models using MLlib Stream and process real-time data using Spark Structured Streaming Deploy Spark applications on cloud and on-premises clusters Course outline Module 1: Introduction to Apache Spark & Scala Overview of Big Data & Distributed Computing What is Apache Spark? – Evolution, Use Cases, and Benefits Spark Ecosystem: Core, SQL, Streaming, MLlib, GraphX Setting up Scala Development Environment for Spark Introduction to Scala Syntax, Data Types, and Functional Programming Module 2: Apache Spark Architecture & Execution Model Understanding Spark Cluster Components – Driver, Executors, Tasks RDDs vs. DataFrames vs. Datasets – Key Differences Spark Execution Model: Transformations & Actions Memory Management & Data Partitioning in Spark Deploying Spark Applications in Local & Cluster Mode Module 3: Working with Spark RDDs in Scala Introduction to Resilient Distributed Datasets (RDDs) Creating, Transforming, and Persisting RDDs Lazy Evaluation & Fault Tolerance in Spark Common RDD Operations: map, filter, flatMap, reduceByKey Performance Optimization: Shuffling, Partitioning, and Caching Module 4: DataFrames & Spark SQL in Scala Introduction to DataFrames & Datasets Creating DataFrames from CSV, JSON, Parquet & JDBC Performing Data Transformations and Aggregations Using Spark SQL for Querying Structured Data Performance Optimization using Catalyst Optimizer Module 5: Advanced Data Engineering with Apache Spark Partitioning & Bucketing for Performance Optimization Handling Schema Evolution & Nested Data Structures Integration with Cloud Storage (AWS S3, Azure Blob, Google Cloud Storage) Managing Data Workflows with Apache Airflow & Apache Kafka Module 6: Machine Learning with Spark MLlib in Scala Introduction to Spark MLlib for Scalable Machine Learning Feature Engineering & Data Preprocessing in Spark Training & Evaluating ML Models Building End-to-End ML Pipelines in Spark Hyperparameter Tuning & Model Deployment Module 7: Real-Time Data Processing with Spark Streaming Introduction to Spark Structured Streaming Streaming Data Sources – Kafka, AWS Kinesis, Azure Event Hubs Stateful & Event-Time Processing with Structured Streaming Handling Late Arriving & Out-of-Order Data Real-World Streaming Use Cases – Fraud Detection, IoT Analytics Module 8: Performance Optimization & Debugging in Spark Understanding Spark UI & Job Execution Plan Best Practices for Memory Management & Shuffle Optimization Broadcast Variables & Accumulators for Performance Debugging Spark Applications & Handling Failures Optimizing Scala Code for Spark Performance Module 9: Deploying Apache Spark Applications Running Spark on Standalone, YARN, and Kubernetes Clusters Deploying Spark Applications on AWS EMR, Azure HDInsight, GCP Dataproc CI/CD Pipelines for Spark Applications Using Databricks for Scalable Spark Deployments
www.compubasetraining.ae · +971 2 677 1117

Apache Spark using Scala

Course overview

Training methodology

Target competencies

Course objectives

Course outline

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