Category: AWS Lake Formation

This is a guest post co-written with Jonatan Selsing and Moses Arthur from Novo Nordisk. This is the second post of a three-part series detailing how Novo Nordisk, a large pharmaceutical enterprise, partnered with AWS Professional Services to build a scalable and secure data and analytics platform. The first post of this series describes the […]

Building a data lake on Amazon Simple Storage Service (Amazon S3) provides numerous benefits for an organization. It allows you to access diverse data sources, build business intelligence dashboards, build AI and machine learning (ML) models to provide customized customer experiences, and accelerate the curation of new datasets for consumption by adopting a modern data […]

This post was co-written by Ashish Prabhu, Stephen Johnston, and Colin Ingarfield at Morningstar and Don Drake, at AWS. With “Empowering Investor Success” as the core motto, Morningstar aims at providing our investors and advisors with the tools and information they need to make informed investment decisions. In this post, Morningstar’s Data Lake Team Leads […]

Data lakes have come a long way, and there’s been tremendous innovation in this space. Today’s modern data lakes are cloud native, work with multiple data types, and make this data easily available to diverse stakeholders across the business. As time has gone by, data lakes have grown significantly and have evolved to data meshes […]

We recently announced support for AWS Lake Formation fine-grained access control policies in Amazon Athena queries for data stored in any supported file format using table formats such as Apache Iceberg, Apache Hudi and Apache Hive. AWS Lake Formation allows you to define and enforce database, table, and column-level access policies to query Iceberg tables […]

Data sharing is becoming an important element of an enterprise data strategy. AWS services like AWS Data Exchange provide an avenue for companies to share or monetize their value-added data with other companies. Some organizations would like to have a data sharing platform where they can establish a collaborative and strategic approach to exchange data […]

Data lakes provide a centralized repository that consolidates your data at scale and makes it available for different kinds of analytics. AWS Glue crawlers are a popular way to scan data in a data lake, classify it, extract schema information from it, and store the metadata automatically in the AWS Glue Data Catalog. AWS Lake […]

Data governance is the collection of policies, processes, and systems that organizations use to ensure the quality and appropriate handling of their data throughout its lifecycle for the purpose of generating business value. Data governance is increasingly top-of-mind for customers as they recognize data as one of their most important assets. Effective data governance enables […]

AWS Lake Formation helps with enterprise data governance and is important for a data mesh architecture. It works with the AWS Glue Data Catalog to enforce data access and governance. Both services provide reliable data storage, but some customers want replicated storage, catalog, and permissions for compliance purposes. This post explains how to create a […]

With AWS Lake Formation, you can build data lakes with multiple AWS accounts in a variety of ways. For example, you could build a data mesh, implementing a centralized data governance model and decoupling data producers from the central governance. Such data lakes enable the data as an asset paradigm and unleash new possibilities with […]