A data lake is a storage space for all forms of data in an organization, whether raw or processed, structured or unstructured. Data lakes can store data in any format or file, allowing businesses to hold unprocessed data indefinitely. Data lakes differ from data warehouses in their agility and flexibility: while data warehouses manage processed data, data lakes can store and analyze data that is entirely raw. Because data lakes contain all of an organization’s data in one location, they avoid the problem of data silos. An organization using a data lake benefits from its flexibility. Often, data lakes exist in the cloud.
Data lakes can become what is called a “data swamp” if not given proper maintenance, however. Although data lakes are convenient for quick and easy data storage, they require organization and strategic planning to be most effective.
Data lakes can help make it easier for organizations to analyze their data. With the right tools, an organization can apply advanced analytics to its data, sifting through different files and folders to find exactly the data it needs and extracting insights from that data. A data lake is an asset for advanced analysis and helpful insights. But without prior structure and planning, data can slip through the cracks or become an unusable mess. Organizations can also use third-party solutions to analyze and compute data in a lake.
Where great amounts of data exist, so do security concerns. Data lake security, at the least, should include strict user authentication, verification, and access controls to limit who can access what data. Multiple layers of encryption are also important. Data lakes must also remain compliant with data protection laws, such as GDPR and CCPA, which may limit the data that they can store or analyze.
