Q: What is the AWS Data Lab?
A: The AWS Data Lab is a program that offers accelerated, joint engineering engagements between customers and AWS technical resources to create tangible deliverables that accelerate data and analytics modernization initiatives. The Data Lab program has two offerings: the Build Lab and the Design Lab. The Build Lab is a two to five day intensive build with a technical customer team. The Design Lab is a one-half to two day engagement for customers who need a real-world architecture recommendation based on AWS expertise, but are not yet ready to build. During the lab, AWS Data Lab Solutions Architects and AWS service experts support the customer by providing prescriptive architectural guidance, sharing best practices, and removing technical roadblocks. Customers leave the engagement with a prototype that is custom fit to their needs, a path to production, deeper knowledge of AWS Databases, Analytics, and Machine Learning services, and new relationships with AWS service experts.
Q: How long are AWS Data Lab engagements?
A: The core hands-on session for a Build Lab ranges in length from two to five days, with a standard engagement length of four days. The Design Lab is a one-half to two day engagement. There are some preparatory and follow-up meetings in the weeks before and after these engagements.
Q: What is the difference between a Build Lab and a Design Lab?
A: In a Build Lab, your team will spend two to five days designing an architecture and building hands-on with your data in your own AWS account with the guidance of AWS service experts and your dedicated AWS Data Lab Solutions Architect. Your days will consist of: build, test, review progress, repeat. On the last day of your lab, your team will leave with a validated architecture and working prototype to use as a guide for your production deployment.
In a Design Lab, your team will spend one-half to two days in a non-build exercise, discussing architecture pattern and anti-pattern designs for your specific use case, best practices for building, and recommended strategies for design and delivery. Your team will leave with a document reflecting the Data Lab's recommendations for your design approach and architecture. Design Labs are a great option for customer teams that need a real-world architecture recommendation based on AWS expertise, but are not yet ready to build.
Q: Who does the building in a Build Lab?
A: Customers build solutions in their own AWS accounts with the guidance of AWS Data Lab Solutions Architects and AWS service experts. A customer team of 4-8 hands-on builders is recommended and your Data Lab Solutions Architect can help you identify in advance which skills will be needed for a successful build engagement.
Q: Who attends a Design Lab?
A: Since a Design Lab is a non-build exercise, customer attendees typically include a mix of architects, builders, non-builders from line of business owners, decision makers, and project influencers. Your Data Lab Solutions Architect will help you determine who makes the most sense to join based on your Design Lab goals.
Q: Where can I attend an AWS Data Lab engagement?
Q: Is there a deadline to apply for an AWS Data Lab engagement?
A: No. AWS Data Lab is an ongoing program and nominations are always welcome.
Q: How does a customer request a Build Lab or a Design Lab?
A: Reach out to your AWS Account Manager or Solutions Architect for more information. If you do not have an AWS account team, please contact Sales.
Q: What AWS services are in scope for a Build Lab or Design Lab?
A: Solutions designed and built in the AWS Data Lab span all AWS Databases, Analytics, and Machine Learning services. Common use cases involve a combination of Amazon Aurora, Amazon RDS, Amazon DynamoDB, Amazon DocumentDB, Amazon QLDB, Amazon Neptune, Amazon QuickSight, Amazon Redshift, Amazon ElastiCache, Amazon Elasticsearch Service, Amazon EMR, Amazon Athena, AWS Glue, AWS Database Migration Service, AWS Lake Formation, Amazon SageMaker, Amazon Forecast, Amazon Comprehend and Amazon Comprehend Medical, Amazon Textract, Amazon Transcribe, and Amazon Rekognition.