SDLF is a collection of reusable resources that accelerate the delivery of enterprise data lakes on the AWS Cloud and helps with faster deployment to production. It is used to implement the foundational structure of a data lake by following best practices.

The Serverless Data Lake Framework (SDLF) is a collection of reusable artifacts aimed at accelerating the delivery of enterprise data lakes on AWS, shortening the deployment time to production from several months to a few weeks.

SDLF is a collection of production-hardened, best-practices templates which accelerate your data lake implementation journey on AWS, so that you can focus on use cases that generate value for business. At a high level, SDLF is an infrastructure-as-code framework that enables customers to …

SDLF is a collection of reusable artifacts aimed at accelerating the delivery of enterprise data lakes on AWS, shortening the deployment time to production from several months to a few weeks.

The Serverless Data Lake Framework (SDLF) is a collection of reusable artifacts aimed at accelerating the delivery of enterprise data lakes on AWS, shortening the deployment time to production from several months to a few weeks.

See the release notes for SDLF 2.0.0 to learn about all the changes in this major version. The repository has been moved to a new name, under a new GiHub organization. Everything else remains the same - licence, code, workshop, documentation. For users of SDLF 1.x, version 1 is still available on the master branch.

2022年9月27日 · “ The Serverless Data Lake Framework (SDLF) is a collection of reusable artifacts aimed at accelerating the delivery of enterprise data lakes on AWS,...

The Serverless Data Lake Framework (SDLF) is a collection of reusable artifacts aimed at accelerating the delivery of enterprise data lakes on AWS, shortening the deployment time to production from several months to a few weeks.

SDLF is a collection of reusable artifacts aimed at accelerating the delivery of enterprise data lakes on AWS, shortening the deployment time to production from several months to a few weeks. This Serverless Data Lake Day workshop is prepared to assist you ingest, store, transform, create insights on unstructured data using AWS serverless services.

sdlf-foundations is defined in the sdlf-foundations folder of the SDLF repository. sdlf-foundations contains, as the name implies, foundational resources of a data lake. Data in a data lake can be broadly categorized across three distinct layers, with dedicated buckets: