Hands-On Amazon Redshift for Data Warehousing

Video description

Build scalable, serverless data warehouses with machine learning and massively parallel processing in the cloud with Amazon Redshift

About This Video

  • Kick traditional data warehouse technologies into touch with a combination of cloud hosting and cutting-edge optimization algorithms
  • Go from data warehouse fundamentals to a fully functioning peta-scale data warehouse in just 3 hours, and learn everything you need to build your own cloud data warehouse
  • Learn the do's and don'ts of data warehousing with this simple hands-on guide to building data warehouses on AWS

In Detail

Amazon Redshift is a low-cost cloud data platform that can scale from gigabytes to petabytes on a high-performance, column-oriented SQL engine. Amazon Redshift brings the power of scale-out architecture to the world of traditional data warehousing.

In this course, you will explore this low-cost, cloud-based storage, which can be scaled up or down to meet your true size and performance needs. You will learn to configure a sample data warehouse. Next, you will explore Redshift's internal workings and architecture, and learn what makes it so fast. You will get hands-on experience connecting, querying, and building BI and data viz products and learn how to secure, maintain, and administer your new platform.

By the end of this course, you will be able to scale from gigabytes to petabytes on this high-performance, column-oriented SQL engine.

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Table of contents

  1. Chapter 1 : Data Warehousing for the Internet Age
    1. The Course Overview 00:02:51
    2. Do We Still Need a Data Warehouse? 00:06:20
    3. Data Technologies Compared: Relational, Data Warehouse, NoSQL, and Big Data 00:05:04
    4. Providing Business Intelligence on Internet-Scale Data 00:05:38
    5. Cloud-Native Data Warehousing 00:03:34
  2. Chapter 2 : Getting Started with Redshift
    1. Launching a Redshift Data Warehouse on AWS 00:05:45
    2. Launching a Redshift Data Warehouse Using Cloudformation 00:08:08
    3. Redshift Technology Deep Dive: Columnar Filesystem 00:05:15
    4. Redshift Technology Deep Dive: Massively Parallel Processing 00:04:20
  3. Chapter 3 : Creating a Redshift Data Warehouse from Disparate Datasets
    1. Sourcing Appropriate Data Sets 00:05:02
    2. Ingesting Various Sizes of Data Set into Redshift 00:09:15
    3. Connecting to and Querying the Data Warehouse 00:05:22
    4. Redshift Technology Deep Dive: Query Caching 00:04:47
  4. Chapter 4 : Optimizing Redshift for Scale
    1. Ingesting Enormous Volumes of Data by Copying Directly from S3 00:07:16
    2. Optimizing Redshift Data Types for Query Performance at Scale 00:05:02
    3. Evenly Distributing Data Across Your Cluster to Improve Filters and Joins 00:06:43
  5. Chapter 5 : Connecting Redshift with Disconnected Data Using Redshift Spectrum
    1. Exploratory Analytics for Disconnected Data 00:06:43
    2. Loading a Disconnected Dataset 00:07:56
    3. Glue Data Catalog - Creating a Schema for the External Dataset 00:06:55
  6. Chapter 6 : Visualizing Your Results with Amazon QuickSight
    1. The BI Use Case for Data Warehousing 00:04:59
    2. Introducing Amazon Quicksight 00:06:06
    3. What Is Spice and How Can It Be Used to Accelerate Analysis? 00:04:51
    4. Loading Data into SPICE 00:05:55

Product information

  • Title: Hands-On Amazon Redshift for Data Warehousing
  • Author(s): Colibri Digital
  • Release date: January 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781838558888