The member will see how to load and update their warehouse using AWS Partner Matillion ETL for Redshift. You will start an EC2 instance of AWS Redshift ETL partner Matillion ETL. You will use your new instance as an create a data loading ETL pipeline and will load data and then transform that data using Matillion.
- [Voiceover] Another really useful aspect of working…with AWS Redshift is the huge number of partners…that Amazon has for this product.…So what this means to you is quicker time to market.…So you could either do things manually, for example,…you can write SQL scripts to manually load up…your Redshift cluster, or if you have a more complex…and more varied data as I often find my customers…heading towards, you can use an ETL or an extract…transform and load partner,…you can take advantage of working with partner companies,…these are not Amazon companies but they're partners…who build products that are designed to work…with Redshift.…
So some that I've used with some good successes…for ETL or extract, transform and load,…that's more sophisticated entry of data…into the Redshift cluster, a company called Matillion,…and again there's many others.…Analytics, one that I've used is Dataiku,…so that's machine learning and that kind of processing,…and Visualization I've used Tableau.…So let me show you a little bit about how this works.…
Author
Lynn Langit
Released
7/18/2016Starting with top-level categories of storage, data, computer, and services, Lynn guides you through planning your ideal AWS architecture, providing service demos using the AWS Console, command-line interface, and other tools. Learn when to use which service for which business case, such as Docker or Lambda or DynamoDB or Aurora? She shows how to script creation of services such as S3 buckets and EC2 instances, create and populate a managed data warehouse, and develop a data processing pipeline that works for you. Chapter 6 covers the AWS Internet of Things (IoT) services.
These exercises can help you build proof-of-concepts, minimum viable products, and deployable solutions to scale and support big data initiatives at your company.
- Setting up your AWS account
- Using AWS tools
- Defining your minimum viable products
- Choosing computer, storage, and data services
- Using S3, EC2, or Docker for website hosting
- Developing an AWS website
- Using a data warehouse
- Developing a data processing pipeline
- Developing an Internet of Things project with AWS
Skill Level Intermediate
Duration
Views
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Introduction
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Welcome42s
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Exercise files1m 27s
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Knowledge checks23s
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1. Start Your AWS Project
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Set up AWS accounts7m 23s
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Set up cost management3m 11s
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Knowledge check21s
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2. Plan Your AWS Architecture
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Define your MVP2m 10s
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Choose services: Compute11m 51s
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Choose services: Storage11m 55s
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Choose services: Data7m 17s
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Knowledge check21s
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3. Develop the AWS Website
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Use S3 for website hosting4m 23s
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Use EC2 for website hosting7m 55s
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Add RDS Aurora database7m 18s
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Knowledge check22s
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4. Develop the AWS Data Warehouse
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Why use a data warehouse?6m 24s
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Use Redshift6m 33s
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Run a baseline query3m 35s
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Use BIME for visualization1m 42s
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Knowledge check22s
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5. Develop AWS Data Processing Pipeline
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Why use data streaming?6m 28s
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Knowledge check21s
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6. Develop AWS IoT Project
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Understand IoT concepts5m 19s
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Knowledge check22s
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Conclusion
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Next steps33s
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Video: Use Matillion ETL for Redshift