Building Batch Data Analytics Solutions on AWS

Name: Building Batch Data Analytics Solutions on AWS
Price: 675.00 USD
Availability: InStock
Rating: 4.5 (1200 reviews)

Price
$675.00 USD

Duration
1 Day

Delivery Methods
Virtual Instructor Led
Private Group

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Course Overview

Building Batch Data Analytics Solutions on AWS

More than 70% of big data workloads now run in the cloud—and Amazon EMR is one of the most popular services used to support them.

In the Building Batch Data Analytics Solutions on AWS course, you'll learn how to design, build, and manage scalable batch data pipelines using Amazon EMR, Apache Spark, and Hadoop. You’ll explore how EMR integrates with services like AWS Glue, Lake Formation, and Step Functions, as well as open-source tools like Hive, Hue, and HBase. This course covers the full pipeline—from ingestion and transformation to security and cost control—with hands-on labs that help you translate concepts into real-world skills and actionable insights.

Course Objectives

This instructor-led course provides technical professionals with the tools and knowledge to build, manage, and optimize scalable data analytics solutions using Amazon EMR. Participants gain practical skills to run secure and efficient data processing workflows on AWS.

You’ll learn how to:

Launch and configure clusters using Amazon EMR for batch workloads
Transform and analyze batch data using Spark, Hive, and AWS Glue
Secure data in transit and at rest using AWS-native tools
Monitor and optimize performance using built-in EMR tools
Apply cost management strategies to large-scale workloads

Who Should Attend?

Data platform engineers
Architects and operators who build and manage data analytics pipelines

Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

What is the Building Batch Data Analytics Solutions on AWS course?

This hands-on AWS training course teaches you how to design and manage batch data analytics pipelines using Amazon EMR, Apache Spark, Hadoop, and AWS-native tools.

How will this training help me prepare data for BI reporting?

This course teaches you how to build batch data analytics solutions using Amazon Redshift, focusing on the ingestion, transformation, and modeling of large datasets. You'll gain practical skills to create structured, query-optimized tables that power dashboards, reporting platforms, and real-time insights. By working through data analytics pipelines, you'll be ready to deliver fast, clean, and reliable data to your business intelligence tools.

Is this course hands-on?

Yes. You’ll work directly with Spark shell, EMR Notebooks, Hive, and AWS tools like Step Functions and EMRFS through interactive demos and labs.

Does this course align with an AWS certification?

While not tied to a specific exam, the course builds foundational skills relevant to the AWS Certified Data Analytics – Specialty certification.

Course 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.

Agenda

Module A: Introduction to Data Analytics and Pipelines

Overview of batch data workflows
Define components of a modern AWS-based data pipeline
Identify analytics use cases across business functions

Module 1: Using Amazon EMR for Batch Analytics

Understand how Amazon EMR supports Spark, Hadoop, Hive, and HBase
Interactive Demo: Launching an EMR cluster
Explore cost management and auto scaling options

Module 2: Data Ingestion and Storage Optimization

Compare techniques for data ingestion
Optimize data storage with S3, compression, and tiering
Integrate with AWS Glue and AWS Lake Formation

Module 3: Apache Spark on EMR for Data Processing

Implement transformation and analytics with Apache Spark
Interactive Demo: Run Spark commands using Spark shell
Practice Lab: Use EMR Notebooks for low-latency analytics

Module 4: Batch Data Processing with Hive

Query and transform structured data using Hive on Amazon EMR
Practice Lab: Run Hive jobs for batch processing tasks

Module 5: Serverless Data Orchestration and Glue Integration

Automate workflows with AWS Step Functions
Catalog and transform data using AWS Glue
Practice Lab: Orchestrate Spark jobs using Step Functions

Module 6: Securing and Monitoring EMR Clusters

Protect data using EMRFS encryption and IAM
Interactive Demo: Enable client-side encryption in EMRFS
Monitor performance using logs, CloudWatch, and Spark History Server

Module 7: Designing Batch Analytics Solutions

Apply cost, performance, and security tradeoffs to pipeline design
Activity: Design a real-world batch data analytics solution

Module B: Building Modern Data Architectures on AWS

Combine open-source and AWS services in flexible architectures
Use Hive, HBase, and Redshift for complex batch analytics
Integrate EMR with AWS Glue and Lake Formation
Practice Lab: Process and analyze batch data using Hive and HBase
Practice Lab: Coordinate Spark jobs using AWS Step Functions
Explore real-world scenarios for enterprise-scale analytics pipelines
Discuss how to structure architectures to support data lakes and data warehouses