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MLPER-04: Use a modern data architecture

PDFRSS

Get the best insights from exponentially growing data using a modern data architecture. This architecture enables easy movement of data between a data lake and purpose-built stores including a data warehouse, relational databases, non-relational databases, ML and big data processing, and log analytics. A data lake provides a single place to run analytics across mixed data structures collected from disparate sources. Purpose-built analytics services provide the speed required for specific use cases like real-time dashboards and log analytics.

Implementation plan

• Unify data governance and access - Integrate a data lake, a data warehouse, and purpose-built stores. This will enable unified governance and easy data movement. With a Modern Data Architecture on AWS, you can store data in a data lake and use data services around it. Use AWS Lake Formation to build a scalable and secure data lake. Build a high-speed analytic layer with purpose-built services, such as Amazon Redshift, Amazon Kinesis, and Amazon Athena. Integrate data across services and data stores with AWS Glue. Apply governance policies to manage security, access control, and audit trails across all the data stores using AWS IAM.

Documents

• Data Lake on AWS

• AWS Lake Formation

• Derive Insights from Modern Data

Blogs

• Build a Lake House Architecture on AWS

• Moving from notebooks to automated ML pipelines using Amazon SageMaker AI and AWS Glue

• Data preprocessing for machine learning on Amazon EMR made easy with AWS Glue DataBrew 

Videos

• Build and Govern Your Data Lakes with AWS Glue

• The lake house approach to data warehousing with Amazon Redshift

Examples

• Predictive Data Science with Amazon SageMaker AI and a Data Lake on AWS