Big Data on AWS

Live Classroom
Duration: 3 days
Live Virtual Classroom
Duration: 3 days
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The Big Data on AWS course introduces participants to cloud-based big data solutions, such as, Amazon EMR, Amazon Redshift, Amazon Kinesis and the rest of the AWS big data platform. The course guides the participants on how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools, like Hive and Hue. The course also teaches participants how to create big data environments, work with Amazon DynamoDB, Amazon Redshift, Amazon Quicksight, Amazon Athena and Amazon Kinesis. It also discusses best practices to design big data environments for security and cost-effectiveness.

What You'll Learn

  • Fit AWS solutions inside 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 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
  • Secure 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


  • Overview of big data
  • Big data ingestion and transfer
  • Big data streaming and Amazon Kinesis
  • Lab 1: Using Amazon Kinesis to stream and analyze Apache server log data
  • Big data storage solutions
  • Big data processing and analytics
  • Lab 2: Using Amazon Athena to query log data from Amazon S3

  • Apache Hadoop and Amazon EMR
  • Lab 3: Storing and querying data on Amazon DynamoDB
  • Using Amazon EMR
  • Hadoop programming frameworks
  • Lab 4: Processing server logs with Hive on Amazon EMR
  • Web interfaces on Amazon EMR
  • Lab 5: Running Pig scripts in Hue on Amazon EMR
  • Apache Spark on Amazon EMR
  • Lab 6: Processing NY Taxi data using Spark on Amazon EMR

  • Using AWS Glue to automate ETL workloads
  • Amazon Redshift and big data
  • Visualizing and orchestrating big data
  • Lab 7: Using TIBCO Spotfire to visualize data
  • Managing big data costs
  • Securing your Amazon deployments
  • Big data design patterns
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Who should attend

The course is highly recommended for:

  • Data scientists
  • Data analysts
  • Big data analysts
  • Solution architects
  • Individuals responsible for designing and implementing big data solutions


Participants need to have basic familiarity with big data technologies, including Apache Hadoop, MapReduce, HDFS and SQL/NoSQL querying. They need to have a fundamental understanding of big data, along with having a working knowledge of core AWS services, public cloud implementation. Besides, participants need to have a basic understanding of data warehousing, relational database systems and database design. Participants need to be an AWS Certified Cloud Practitioner or an AWS Certified Solution Architect – Associate or an AWS Certified Developer – Associate or an AWS Certified SysOps Administrator – Associate.

Interested in this Course?


    This course helps participants prepare for the AWS Certified Big Data – Specialty certification examination. This exam validates the technical skills and experience in designing and implementing AWS services to derive value from data. The exam is for individuals who perform complex big data analyses, and validates an individual’s ability to implement core AWS Big Data services according to basic architectural best practices, design and maintain big data, and leverage tools to automate data analysis.


    The exam comprises of multiple choice, multiple answer questions and participants get 3 hours to take the examination. The exam is available in English.

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