Big Data on AWS (BDAWS)

Course Description Agenda Course Outline
 

About this Course

Big Data on AWS introduces you to cloud-based big data solutions and Amazon Elastic MapReduce (EMR), 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 Pig and Hive. We also teach you how to create big data environments, work with Amazon DynamoDB and Amazon Redshift, understand the benefits of Amazon Kinesis, and leverage best practices to design big data environments for security and cost-effectiveness.

A qui s'adresse cette formation

  • 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

Class Prerequisites

  • Basic familiarity with big data technologies, including Apache Hadoop and HDFS
  • Working knowledge of core AWS services and public cloud implementation
  • Basic understanding of data warehousing, relational database systems, and database design

What You Will Learn

By the end of this course, you should be able to:

  • Understand Apache Hadoop in the context of Amazon EMR
  • Understand the architecture of an Amazon EMR cluster
  • Launch an Amazon EMR cluster using an appropriate Amazon Machine Image and Amazon EC2 instance types
  • Choose appropriate AWS data storage options for use with Amazon EMR
  • Know your options for ingesting, transferring, and compressing data for use with Amazon EMR
  • Use common programming frameworks available for Amazon EMR including Hive, Pig, and Streaming
  • Work with Amazon Redshift to implement a big data solution
  • Leverage big data visualization software
  • Choose appropriate security options for Amazon EMR and your data
  • Perform in-memory data analysis with Spark and Shark on Amazon EMR
  • Choose appropriate options to manage your Amazon EMR environment cost-effectively
  • Understand the benefits of using Amazon Kinesis for big data

Outline: Big Data on AWS (BDAWS)

Day 1
  • Overview of Big Data, Apache Hadoop, and the Benefits of Amazon EMR
  • Amazon EMR Architecture
  • Using Amazon EMR
  • Launching and Using an Amazon EMR Cluster
  • Hadoop Programming Frameworks
Day 2
  • Using Hive for Advertising Analytics
  • Using Streaming for Life Sciences Analytics
  • Overview: Spark and Shark for In-Memory Analytics
  • Using Spark and Shark for In-Memory Analytics
  • Managing Amazon EMR Costs
  • Overview of Amazon EMR Security
  • Data Ingestion, Transfer, and Compression
  • Using Amazon Kinesis for Real-Time Big Data Processing
Day 3
  • Using Amazon Kinesis for Real-Time Big Data Processing
  • AWS Data Storage Options
  • Using DynamoDB with Amazon EMR
  • Overview: Amazon Redshift and Big Data
  • Using Amazon Redshift for Big Data
  • Visualizing and Orchestrating Big Data
  • Using Tableau Desktop or Jaspersoft BI to Visualize Big Data
Classroom training
Modality: G

Durée 3 jours

Prix
  • Canada: US$ 1 950,-
Dates and Booking
Formation en ligne
Modality: U

Durée 3 jours

Prix
  • Canada: US$ 1 950,-
Dates and Booking
 
pointer une ville pour s'enregistrerAgenda
This is an Instructor-Led Classroom course
Fast Lane s’engage à mettre en œuvre les formations garanties quelque soit le nombre de participants, en dehors des cas de force majeurs ou d’événements exceptionnels, comme un accident ou un maladie de l’instructeur.
Formation en mode FLEX™.
  *   This class is delivered by a partner.
Canada

Actuellement aucune session planifiée  For enquiries please write to info@fastlaneca.com.

Etats-Unis
Aug 29-31, 2017 Philadelphia, PA 09:00 US/Eastern * Enroll
Aug 29-31, 2017 Virtual Class 09:00 US/Eastern * Enroll
Aug 29-31, 2017 Herndon/Reston, VA 09:00 US/Eastern * Enroll
Sep 20-22, 2017 San Francisco, CA 09:00 US/Pacific * Enroll
Sep 20-22, 2017 Virtual Class 12:00 US/Eastern * Enroll
Oct 11-13, 2017 Virtual Class 09:00 US/Eastern * Enroll
Oct 11-13, 2017 Herndon/Reston, VA 09:00 US/Eastern * Enroll
Oct 25-27, 2017 San Francisco, CA 09:00 US/Pacific * Enroll
Oct 25-27, 2017 Virtual Class 12:00 US/Eastern * Enroll
Nov 20-22, 2017 Virtual Class 09:00 US/Eastern * Enroll