Big Data on AWS

This Big Data on AWS class introduces you to cloud-based big data solutions such as Amazon 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, Amazon Quicksight, Amazon Athena and Amazon Kinesis, and leverage best practices to design big data environments for security and cost-effectiveness.

This course is intended for individuals responsible for designing and implementing big data solutions, namely Solutions Architects, and data scientists and data analysts interested in learning about the services and architecture patterns behind big data solutions on AWS.

Authorized AWS Training

Webucator has partnered with an Authorized AWS training delivery partner to offer official AWS courses utilizing Amazon Authorized Instructors.

Location

Public Classes: Delivered live online via WebEx and guaranteed to run . Join from anywhere!

Private Classes: Delivered at your offices , or any other location of your choice.

Goals
  1. Fit AWS solutions inside of a big data ecosystem
  2. Leverage Apache Hadoop in the context of Amazon EMR
  3. Identify the components of an Amazon EMR cluster
  4. Launch and configure an Amazon EMR cluster
  5. Leverage common programming frameworks available for Amazon EMR including Hive, Pig, and Streaming
  6. Leverage Hue to improve the ease-of-use of Amazon EMR
  7. Use in-memory analytics with Spark on Amazon EMR
  8. Choose appropriate AWS data storage options
  9. Identify the benefits of using Amazon Kinesis for near real-time big data processing
  10. Leverage Amazon Redshift to efficiently store and analyze data
  11. Comprehend and manage costs and security for a big data solution
  12. Secure a Big Data solution
  13. Identify options for ingesting, transferring, and compressing data
  14. Leverage Amazon Athena for ad-hoc query analytics
  15. Leverage AWS Glue to automate ETL workloads
  16. Use visualization software to depict data and queries using Amazon QuickSight
  17. Orchestrate big data workflows using AWS Data Pipeline
Outline
  1. Overview of Big Data
  2. Big Data Ingestion and Transfer
  3. Big Data Streaming and Amazon Kinesis
  4. Lab: Using Amazon Kinesis to Stream and Analyze Apache Server Log Data
  5. Big Data Storage Solutions
  6. Big Data Processing and Analytics
  7. Lab: Using Amazon Athena to Query Log Data From Amazon S3
  8. Apache Hadoop and Amazon EMR
  9. Lab: Storing and Querying Data on Amazon DynamoDB
  10. Using Amazon EMR
  11. Hadoop Programming Frameworks
  12. Lab: Processing Server Logs With Hive on Amazon EMR
  13. Web Interfaces on Amazon EMR
  14. Lab: Running Pig Scripts in Hue on Amazon EMR
  15. Apache Spark on Amazon EMR
  16. Lab: Processing NY Taxi data using Spark on Amazon EMR
  17. Using AWS Glue to automate ETL workloads
  18. Amazon Redshift and Big Data
  19. Visualizing and Orchestrating Big Data
  20. Lab: Using TIBCO Spotfire to Visualize Data
  21. Managing Big Data Costs
  22. Securing Your Amazon Deployments
  23. Big Data Design Patterns
Class Materials

Each student in our Live Online and our Onsite classes receives a comprehensive set of materials, including course notes and all the class examples.

Class Prerequisites

Experience in the following is required for this AWS class:

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

Courses that can help you meet these prerequisites:

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Length: 3 Days
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