As organizations are collecting and analyzing increasing amounts of data,
traditional on-premises solutions for data storage, data management, and
analytics can no longer keep pace. Data siloes that aren’t built to work well
together make storage consolidation for more comprehensive and efficient
analytics difficult. This, in turn, limits an organization’s agility, ability to derive
more insights and value from its data, and capability to seamlessly adopt more
sophisticated analytics tools and processes as its skills and needs evolve.

A data lake, which is a single platform combining storage, data governance, and
analytics, is designed to address these challenges. It’s a centralized, secure, and
durable cloud-based storage platform that allows you to ingest and store
structured and unstructured data, and transform these raw data assets as
needed. You don’t need an innovation-limiting pre-defined schema. You can use
a complete portfolio of data exploration, reporting, analytics, machine learning,
and visualization tools on the data. A data lake makes data and the optimal
analytics tools available to more users, across more lines of business, allowing
them to get all of the business insights they need, whenever they need them.

Until recently, the data lake had been more concept than reality. However,
Amazon Web Services (AWS) has developed a data lake architecture that allows
you to build data lake solutions cost-effectively using Amazon Simple Storage
Service (Amazon S3) and other services.