Building Batch Data Analytics Solutions on AWS - 1-Day- Instructor Led

Unlock the power of your data with this hands-on course designed to help you architect and optimize modern data analytics solutions on AWS. Learn to compare data lakes, warehouses, and hybrid architectures then go deep into building batch analytics pipelines, optimizing storage, and choosing the right compute for any business scenario.

From ingestion to transformation, security to cost optimization — you'll gain the skills to deliver fast, secure, and actionable insights at scale.

👉 Don’t just collect data — make it work for you. Enroll now and become the go-to analytics expert in your team.

This course includes presentations, interactive demos, practice labs, discussions, and class exercises.

In this course, you will learn to:

• Compare the features and benefits of data warehouses, data lakes, and modern data architectures

• Design and implement a batch data analytics solution

• Identify and apply appropriate techniques, including compression, to optimize data storage

• Select and deploy appropriate options to ingest, transform, and store data

• Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case

• Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights

• Secure data at rest and in transit

• Monitor analytics workloads to identify and remediate problems

• Apply cost management best practices

Intended audience

This course is intended for: • Data platform engineers

• Architects and operators who build and manage data analytics pipelines

Prerequisites

Students with a minimum one-year experience managing open-source data frameworks such as Apache Spark or Apache Hadoop will benefit from this course.

We suggest the AWS Hadoop Fundamentals course for those that need a refresher on Apache Hadoop.

We recommend that attendees of this course have:

• Completed either AWS Technical Essentials or Architecting on AWS

• Completed either Building Data Lakes on AWS or Getting Started with AWS Glue

Course outline

• Module A: Overview of Data Analytics and the Data Pipeline

• Data analytics use cases

• Using the data pipeline for analytics

Module 1: Introduction to Amazon EMR

• Using Amazon EMR in analytics solutions

• Amazon EMR cluster architecture

• Interactive Demo 1: Launching an Amazon EMR cluster

• Cost management strategies

Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage

• Storage optimization with Amazon EMR

• Data ingestion techniques

Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR

• Apache Spark on Amazon EMR use cases

• Why Apache Spark on Amazon EMR

• Spark concepts

• Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the • Spark shell

• Transformation, processing, and analytics

• Using notebooks with Amazon EMR

• Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR

Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive

• Using Amazon EMR with Hive to process batch data

• Transformation, processing, and analytics

• Practice Lab 2: Batch data processing using Amazon EMR with Hive

• Introduction to Apache HBase on Amazon EMR

Module 5: Serverless Data Processing

• Serverless data processing, transformation, and analytics

• Using AWS Glue with Amazon EMR workloads

• Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions

Module 6: Security and Monitoring of Amazon EMR Clusters

• Securing EMR clusters

• Interactive Demo 3: Client-side encryption with EMRFS

• Monitoring and troubleshooting Amazon EMR clusters

• Demo: Reviewing Apache Spark cluster history

Module 7: Designing Batch Data Analytics Solutions

• Batch data analytics use cases

• Activity: Designing a batch data analytics workflow

Module B: Developing Modern Data Architectures on AWS

• Modern data architectures