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The Data Flywheel is a comprehensive and additive approach for business and technology leaders to enable organizations to get the most value from their data.
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. 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 Flywheel Lab's recommendations for your design approach and architecture.
The Build Lab is a two to five day intensive build with a technical customer team. In a Build Lab, your team will spend two to five days building hands-on with your data in your own AWS account with the guidance of AWS service experts and your dedicated AWS Data Flywheel 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.
Leading up to your Data Flywheel Lab engagement, you will have a series of calls to walk through your business use case and understand your goals. “work backwards” from business challenges to identify and prioritize your org’s top Data Flywheel use cases based on business value, data availability and quality, and level of effort.
In the AWS Data Flywheel Lab, your team will be hyper-focused on the pre-defined use case that you selected for the lab. Experts guide your team through the process of developing Cloud, Data & ML solutions for a targeted set of use cases.
To cap off the engagement, our experts can assist with the deployment of Data/ML capabilities to production. Following your Data Flywheel Lab engagement, your team remains in communication with your Lab Solutions Architect until your project is successfully implemented