Organizations are increasingly designing and building data lakes on the Amazon Web Services (AWS) Cloud as part of their data modernization strategy. Data lakes can be repositories that store your structured and unstructured data at any scale and make it available to a wide range of internal and external users.
However, it can take significant time and effort to ingest data into data lakes that serve your growing business use cases. To help reduce costs and maximize the value that is generated from the data, many organizations plan a one-time ingestion of data into their data lake and then consume this data multiple times. Designing a data lake architecture that scales with data production, sharing, and consumption is critical for delivering value to business stakeholders when your data lake grows.
A scalable data lake architecture provides your organization with a solid foundation to gain value from your data lake while bringing more data into it. By continuously gaining data insights without being slowed down or interrupted because of scalability constraints, a scalable data lake also helps your organization remain competitive.
