CloudAcademy
  1. Home
  2. Training Library
  3. Amazon Web Services
  4. Courses
  5. AWS Big Data Specialty - Data Collection

AWS Data Pipeline Reference Architecture

play-arrow
Start course
Overview
DifficultyIntermediate
Duration1h 7m
Students711

Description

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.

Transcript

Okay, as we come to the end of this module on AWS Data Pipeline, let's have a quick look at an example of a Reference Architecture from AWS where AWS Data Pipeline can be used. If we look at this scenario, what we're looking at is sensor data being streamed from devices such as power meters or cell phones through using Amazon simple queuing services and to a Dynamode DB database. From there, AWS Data Pipeline is used to move the data from Dynamode DB to Amazon EMR and trigger an EMR job to execute some polling processes on that data. From there, the data can be moved from EMR into read-shift to enable our standard business intelligence tools to use it for reporting purposes. That brings us to the end of the AWS Data Pipeline module. I look forward to speaking with you again.

About the Author

Students1187
Courses3

Shane has been emerged in the world of data, analytics and business intelligence for over 20 years, and for the last few years he has been focusing on how Agile processes and cloud computing technologies can be used to accelerate the delivery of data and content to users.

He is an avid user of the AWS cloud platform to help deliver this capability with increased speed and decreased costs. In fact its often hard to shut him up when he is talking about the innovative solutions that AWS can help you to create, or how cool the latest AWS feature is.

Shane hails from the far end of the earth, Wellington New Zealand, a place famous for Hobbits and Kiwifruit. However your more likely to see him partake of a good long black or an even better craft beer.