Big Data on AWS Training in United States of America

  • Learn via: Classroom / Virtual Classroom / Online
  • Duration: 3 Days
  • Price: Please contact for booking options
We can host this training at your preferred location. Contact us!

Big Data on AWS introduces you to cloud-based big data solutions such as Amazon Elastic MapReduce (EMR), Amazon Redshift, Amazon Kinesis and the rest of the AWS big data platform. In this course, we show you how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue. We also teach you how to create big data environments, work with Amazon DynamoDB, Amazon Redshift, and Amazon Kinesis, and leverage best practices to design big data environments for security and cost-effectiveness.

Target Audience

  • Individuals responsible for designing and implementing big data solutions, namely Solutions Architects and SysOps Administrators
  • Data Scientists and Data Analysts interested in learning about big data solutions on AWS

Delivery Method

This course is delivered through a mix of:

  • Instructor-Led Training (ILT)
  • Hands-On Labs

Hands-On Activity

This course allows you to test new skills and apply knowledge to your working environment through a variety of practical exercises.

We recommend that attendees of this course have the following prerequisites:

  • Basic familiarity with big data technologies, including Apache Hadoop, MapReduce, HDFS, and SQL/NoSQL querying
  • Students should complete the Big Data Technology Fundamentals web-based training or have equivalent experience
  • Working knowledge of core AWS services and public cloud implementation
  • Students should complete the AWS Essentials course or have equivalent experience
  • Basic understanding of data warehousing, relational database systems, and database design

This course teaches you how to:

  • Fit AWS solutions inside of a big data ecosystem
  • Leverage Apache Hadoop in the context of Amazon EMR
  • Identify the components of an Amazon EMR cluster
  • Launch and configure an Amazon EMR cluster
  • Leverage common programming frameworks available for Amazon EMR including Hive, Pig, and Streaming
  • Leverage Hue to improve the ease-of-use of Amazon EMR
  • Use in-memory analytics with Spark on Amazon EMR
  • Choose appropriate AWS data storage options
  • Identify the benefits of using Amazon Kinesis for near real-time big data processing
  • Leverage Amazon Redshift to efficiently store and analyze data
  • Comprehend and manage costs and security for a big data solution
  • Identify options for ingesting, transferring, and compressing data
  • Leverage Amazon Athena for ad-hoc query analytics
  • Leverage AWS Glue to automate ETL workloads.
  • Use visualization software to depict data and queries using Amazon QuickSight
  • Orchestrate big data workflows using AWS Data Pipeline

Day 1

  • Overview of Big Data
  • Ingestion
  • Big Data streaming and Amazon Kinesis
  • Using Kinesis to stream and analyze Apache server logs
  • Storage Solutions
  • Querying Big Data using Amazon Athena
  • Using Amazon Athena to analyze log data
  • Introduction to Apache Hadoop and Amazon EMR

Day 2

  • Using Amazon Elastic MapReduce
  • Storing and Querying Data on DynamoDB
  • Hadoop Programming Frameworks
  • Processing Server Logs with Hive on Amazon EMR
  • Streamlining Your Amazon EMR Experience with Hue
  • Running Pig Scripts in Hue on Amazon EMR
  • Spark on Amazon EMR
  • Processing New York Taxi dataset using Spark on Amazon EMR

Day 3

  • Using AWS Glue to automate ETL workloads
  • Amazon Redshift and Big Data
  • Visualizing and Orchestrating Big Data
  • Visualizing
  • Managing Amazon EMR Costs
  • Securing Big Data solutions
  • Big Data Design Patterns


Contact us for more detail about our trainings and for all other enquiries!

Upcoming Trainings

Join our public courses in our United States of America facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

10 February 2025 (3 Days)
United States of America
Classroom / Virtual Classroom
14 February 2025 (3 Days)
United States of America
Classroom / Virtual Classroom
10 February 2025 (3 Days)
United States of America
Classroom / Virtual Classroom
14 February 2025 (3 Days)
United States of America
Classroom / Virtual Classroom
12 March 2025 (3 Days)
United States of America
Classroom / Virtual Classroom
23 March 2025 (3 Days)
United States of America
Classroom / Virtual Classroom
23 March 2025 (3 Days)
United States of America
Classroom / Virtual Classroom
12 March 2025 (3 Days)
United States of America
Classroom / Virtual Classroom
Big Data on AWS Training Course in the United States

The United States of America (USA) is a country in North America and a federal republic of 50 states. At almost 9.8 million square kilometers, the United States is one of the world’s biggest and most populous countries. While America’s capital city is Washington, D.C., some of its well known cities are New York, Los Angeles, Miami, Chicago, Orlando, Las Vegas, Dallas, San Francisco and Kansas City.

The most iconic symbol of the country is probably the Statue of Liberty in New York and it was gifted by France. Despite the fact that English is the most widely used language in the United States, there is no official language. Independent since July 4, 1776, USA’s motto is “In God We Trust” and their current president is Joe Biden. Some of the best places to visit in the United States are Grand Canyon, Yosemite, Maui, New Orleans, Honolulu, Zion National Park, Kauai, Lake Tahoe, Aspen, Big Sur and Santa Fe.

Achieve your IT goals through our versatile courses, spanning programming, data analytics, software development, business skills, cloud computing, cybersecurity, project management. Benefit from the flexibility of hosting training at your preferred location within United States, where our experienced instructors will provide hands-on learning and practical expertise.
By using this website you agree to let us use cookies. For further information about our use of cookies, check out our Cookie Policy.