Big Data on AWS

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In this course, you will learn how to:
This course is designed to teach you how to:

Use Apache Hadoop with Amazon EMR

• Launch and configure an Amazon EMR cluster.
• Use common programming frameworks for Amazon EMR, including Hive, Pig, and Streaming.
• Use Hue to improve the ease-of-use of Amazon EMR.
• Use in-memory analytics with Spark on Amazon EMR.
• Understand how services like AWS Glue, Amazon Kinesis, Amazon Redshift, Amazon Athena, and.
• Amazon QuickSight can be used with big data workloads.

Intended 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:

• Basic familiarity with big data technologies, including Apache Hadoop, HDFS, and SQL/NoSQL querying.
• Completed Data Analytics Fundamentals free digital training or equivalent experience.
• Working knowledge of core AWS services and public cloud implementation.
• Completed the AWS Technical Essentials classroom training or have equivalent experience.
• Basic understanding of data warehousing, relational database systems, and database design.

Day One
Module 1: Overview of Big Data

• What is big data.
• The big data pipeline.
• Big data architectural principals.

Module 2: Big Data ingestion and transfer

• Overview: Data ingestion.
• Transferring data.

Module 3: Big data streaming and Amazon Kinesis

• Stream processing of big data.
• Amazon Kinesis.
• Amazon Kinesis Data Firehose.
• Amazon Kinesis Video Streams.
• Amazon Kinesis Data Analytics.
• Hands-on lab 1: Streaming and Processing Apache Server Logs Using Amazon Kinesis.

Module 4: Big data storage solutions

• AWS data storage options.
• Storage solutions concepts.
• Factors in choosing a data store.

Module 5: Big data processing and analytics

• Big data processing and analytics.
• Amazon Athena.
• Hands-on lab 2: Using Amazon Athena to Analyze Log Data.

Day Two
Module 6: Apache Hadoop and Amazon EMR

• Introduction to Amazon EMR and Apache Hadoop.
• Best practices for ingesting data.
• Amazon EMR.
• Amazon EMR architecture.
• Hands-on lab 3: Storing and Querying Data on Amazon DynamoDB.

Module 7: Using Amazon EMR

• Developing and running your application.
• Launching your cluster.
• Handling output from your completed jobs.

Module 8: Hadoop programming frameworks

• Hadoop frameworks.
• Other frameworks for use on Amazon EMR.
• Hands-on lab 4: Processing Server Logs with Hive on Amazon EMR.

Module 9: Web interfaces on Amazon EMR

• Hue on Amazon EMR.
• Monitoring your cluster.
• Hands-on lab 5: Running Pig Scripts in Hue on Amazon EMR.

Module 10: Apache Spark on Amazon EMR

• Apache Spark.
• Using Spark.
• Hands-on lab 6: Processing NY Taxi Data Using Apache Spark.

Day Three
Module 11: Using AWS Glue to automate ETL workloads

• What is AWS Glue?.
• AWS Glue: Job orchestration.

Module 12: Amazon Redshift and big data

• Data warehouses vs. traditional databases.
• Amazon Redshift.
• Amazon Redshift architecture.

Module 13: Securing your Amazon deployments

• Securing your Amazon deployments.
• Amazon EMR security overview.
• AWS Identity and Access Management (IAM) overview.
• Securing data.
• Amazon Kinesis security overview.
• Amazon DynamoDB security overview.
• Amazon Redshift security overview.

Module 14: Managing big data costs

• Total cost considerations for Amazon EMR.
• Amazon EC2 pricing models.
• Amazon Kinesis pricing models.
• Cost considerations for Amazon DynamoDB.
• Cost considerations and pricing models for Amazon Redshift
• Optimizing cost with AWS.

Module 15: Visualizing and orchestrating big data

• Visualizing big data.
• Amazon QuickSight.
• Orchestrating a big data workflow.
• Hands-on lab 7: Using TIBCO Spotfire to visualize data.

Module 16: Big data design patterns

• Common architectures.

Module 17: Course wrap-up

• What’s next?.