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AWS First-Party Service Integration with Teradata Vantage

AWS First-Party Service Integration with Teradata Vantage
When it comes to enterprise analytics in the cloud, Teradata Vantage is incomparable–and when combined with the global footprint of Amazon Web Services (AWS), customers can use Vantage to achieve the highest levels of advanced analytics.
 
Teradata Vantage on AWS enables users to scale as needed and integrate seamlessly with favorite business intelligence and visualization tools. Users can transform data into game-changing insights–whether in Vantage itself, Amazon S3, Amazon EMR, Hadoop, or other data stores.
 
Teradata is both an Advanced Technology Partner and an Advanced Consulting Partner within the AWS Partner Network (APN). We’ve been working together to optimize the combination of Teradata software with AWS services and resources since 2015. In fact, Teradata recently served as a launch partner for the Amazon EBS Multi-Attach feature and co-presented with Amazon EBS subject matter experts at AWS re:Invent.
 
Vantage integrates with many AWS first-party services, including:
 
Amazon CloudFormation Amazon EMR AWS Direct Connect
Amazon CloudWatch Amazon Kinesis AWS EKS
Amazon EBS Amazon QuickSight AWS Glue
Amazon EBS Snapshots Amazon S3 AWS KMS
Amazon EC2 Amazon SageMaker AWS Lambda
 
Mythbusters-Cloud-Services-Blog-1-AWS-V1-(1).pngIntegration with AWS first-party services is important because customers want the ability to have as much cloud-native functionality as possible for their Vantage environments. It enables users to tap into new sources of innovation across all aspects of the analytic process from start to finish.
 
Let’s look at a few examples:
 
AWS Glue and Teradata Vantage
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. Users simply point AWS Glue to their data stored on AWS, and AWS Glue discovers and stores the associated metadata (e.g., table definition and schema) in the AWS Glue Data Catalog. Once cataloged, the data is immediately searchable, queryable, and available for ETL.
 
AWS Glue is serverless and consists of a central metadata repository known as the AWS Glue Data Catalog, an ETL engine that automatically generates Python or Scala code, and a flexible scheduler that handles dependency resolution, job monitoring, and retries. Vantage is not natively supported by AWS Glue, but data can still be imported into Amazon S3 using custom database connectors. The following figure shows how the data flows between Vantage and Amazon S3.
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To learn more about Teradata Vantage and AWS Glue, read this joint blog post.
 
Amazon QuickSight and Teradata Vantage
Amazon QuickSight is a fast, cloud-powered business intelligence service that makes it easy to deliver insights to everyone in the organization. As a fully managed service, QuickSight lets users easily create and publish interactive dashboards that include ML Insights. Dashboards can then be accessed from any device, and embedded into applications, portals, and websites.

Using Vantage and Amazon QuickSight, users:
 
  • Get started quickly–Sign in, choose a data source, and create visualizations in minutes
  • Access data from multiple sources–Use Vantage, upload files, or connect to AWS data sources
  • Take advantage of dynamic visualizations–Smart visualizations are dynamically created based on the fields selected
  • Get answers fast–Generate fast, interactive visualizations on large data sets
  • Tell a story with data–Create data dashboards and point-in-time visuals, share insights, and collaborate with others
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Amazon SageMaker and Teradata Vantage
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.
 
There are two use cases for SageMaker and Vantage:
 
  1. Data resides on Vantage. SageMaker will be used for both the model definition and subsequent scoring. Under this use case Vantage will provide data into the Amazon S3 environment so that SageMaker can consume training and test data sets for the purpose of model development. Vantage would further make data available via Amazon S3 for subsequent scoring by SageMaker. Under this model, Vantage is a data repository only.
  2. Data resides on Vantage. SageMaker will be used for the model definition, and Vantage for the subsequent scoring. Under this use case Vantage will provide data into the Amazon S3 environment so that SageMaker can consume training and test data sets for the purpose of model development. Vantage will need to import the SageMaker model into a Vantage table for subsequent scoring by Vantage Advanced SQL engine or Machine Learning engine. Under this model Vantage is a data repository and a scoring engine.
To learn more about Teradata Vantage on AWS, read the joint blog post on the AWS Partner Network (APN) site and check out the AWS ISV Workload Migration Program, of which Teradata is a member.

Portrait of Brian Wood

(Author):
Brian Wood

Brian Wood is director of cloud marketing at Teradata. He has over 15 years' experience leading all areas of technology marketing in cloud, wireless, IT, software, and data analytics. He earned an MS in Engineering Management from Stanford, a BS in Electrical Engineering from Cornell, and served as an F-14 Radar Intercept Officer in the US Navy.
  View all posts by Brian Wood

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