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AWS-BIGDAT – Big Data on AWS

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Course Outline:

Course Outline

This course covers the following concepts on each day:

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

Course Audience & Prerequisites:

Audience:

This course is intended for:

  • 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.

Prerequisites:

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

  • Basic familiarity with big data technologies, including Apache Hadoop, 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.

Course Offerings:

Instructor Led Online

Students receive the same quality as an in-class course, with a live instructor and the ability to participate in hands-on labs through real-life examples

ILOs help cut costs by reducing time and travel as they can be taken from home or the office and require only the use of a computer, high-speed wired internet and a headset.

  • Course Outline
  • Course Audience & Prerequisites
  • Course Offerings
  • Related Courses

Course Outline

This course covers the following concepts on each day:

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

Audience:

This course is intended for:

  • 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.

Prerequisites:

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

  • Basic familiarity with big data technologies, including Apache Hadoop, 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.

Instructor Led Online

Students receive the same quality as an in-class course, with a live instructor and the ability to participate in hands-on labs through real-life examples

ILOs help cut costs by reducing time and travel as they can be taken from home or the office and require only the use of a computer, high-speed wired internet and a headset.