Course Description:

In course one of the AWS Big Data Specialty Data Collection learning path we explain the various data collection methods and techniques for determining the operational characteristics of a collection system. We explore how to define a collection system able to handle the frequency of data change and the type of data being ingested. We identify how to enforce data properties such as order, data structure, and metadata, and to ensure the durability and availability for our collection approach Intended audience: This course is intended for students looking to increase their knowledge of data collection methods and techniques with Big Data solutions.

Prerequisites:

While there are no formal pre-requisites students will benefit from having a basic understanding of analytics services available in AWS. Recommended courses - Analytics Fundamentals https://cloudacademy.com/amazon-web-services/analytics-fundamentals-for-aws-course/

Learning objectives:

Recognize and explain the operational characteristics of a collection system.
Recognize and explain how a collection system can be designed to handle the frequency of data change and type of data being ingested.
Recognize and identify properties that may need to be enforced by a collection system.

This course includes:

45 minutes of high-defnition videos
Live hands-on demos

What You'll Learn:

Introduction to Collecting Data: In this lesson we'll prepare you for what we'll be covering in the course; the Big Data collection services of AWS Data Pipeline, Amazon Kinesis and AWS Snowball.
Introduction to Data Pipeline: In this lesson we'll discuss the basics of Data Pipeline.
AWS Data Pipeline Architecture: In this lesson we'll go into more detail about the architecture that underpins the AWS Data Pipeline Big Data Service.
AWS Data Pipeline Core Concepts: In this lesson we'll discuss how we define data nodes, access, activities, schedules and resources.
AWS Data Pipeline Reference Architecture: In this lesson we'll look at a real life scenario of how data pipeline can be used.
Introduction to AWS Kinesis: In this lesson we'll take a top level view of Kinesis and it's uses.
Kinesis Streams Architecture: In this lesson we'll look at the architecture that underpins Kinesis.
Kinesis Streams Core Concepts: In this lesson we'll dig deeper into the data records.
Kinesis Streams Firehose Architecture: In this lesson we'll look at firehose architecture and the differences between it and Amazon Kinesis Streams.
Firehose Core Concepts: Let's take a deeper look at some detals about the Firehose service.
Kinesis Wrap-Up: In this summary we'll look at the differences between Kinesis and Firehose.
Introduction to Snowball: Overview of the Snowball Service.
Snowball Architecture: Let's have a look at the architecture that underpins the AWS Snowball big data service
Snowball Core Concepts: In this lesson we'll look at the details of how Snowball is engineered to support data transfer.
Snowball Wrap-Up: A brief summary of Snowball and our course.

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