Course Number: AWS-117

Duration: 3 days (19.5 hours)

Format: Live, hands-on

AWS Big Data Training Overview

This Big Data on AWS training course teaches attendees how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools, including Hive and Hue. Students learn how to create big data environments and work with Amazon DynamoDB, Amazon Redshift, Amazon Quicksight, Amazon Athena, and Amazon Kinesis. This course also covers best practices for designing big data environments for security and cost-effectiveness.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

In addition, some courses are available as live, online classes for individuals. See a schedule of online courses.

Objectives

  • 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
  • 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

Prerequisites

All students should have:

  • 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
  • Taken Accelebrate's AWS Technical Essentials course or have equivalent experience
  • Basic understanding of data warehousing, relational database systems, and database design

Outline

Expand All | Collapse All

Introduction
Data Ingestion, Storage, and Processing
  • Overview of Big Data
  • Big Data Ingestion and Transfer
  • Big Data Streaming and Amazon Kinesis
  • Big Data Storage Solutions
  • Big Data Processing and Analytics
Amazon EMR and Hadoop Frameworks
  • Apache Hadoop and Amazon EMR
  • Using Amazon EMR
  • Hadoop Programming Frameworks
  • Web Interfaces on Amazon EMR
  • Apache Spark on Amazon EMR
ETL, Data Warehousing, Cost, and Security
  • Using AWS Glue to automate ETL workloads
  • Amazon Redshift and Big Data
  • Visualizing and Orchestrating Big Data
  • Managing Big Data Costs
  • Securing Your Amazon Deployments
  • Big Data Design Patterns
Conclusion

Training Materials:

All AWS training students will receive comprehensive courseware.

Software Requirements:

A system free of restrictive firewalls that prevent SSH and RDP into AWS virtual machines.



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