A data lake is a central location that holds a large amount of data in its native, raw format. Compared to a hierarchical data warehouse, which stores data in files or folders, a data lake uses a flat architecture and object storage to store the data. Object storage stores data with metadata tags and a unique identifier, which makes it easier to locate and retrieve data across regions, and improves performance. By leveraging inexpensive object storage and open formats, data lakes enable many applications to take advantage of the data.
Data lakes were developed in response to the limitations of data warehouses. While data warehouses provide businesses with highly performant and scalable analytics, they are expensive and proprietary and can't handle the modern use cases most companies are looking to address. Data lakes are often used to consolidate all of an organization’s data in a single, central location, where it can be saved “as is,” without the need to impose a schema (i.e., a formal structure for how the data is organized) up front like a data warehouse does. Data in all stages of the refinement process can be stored in a data lake: raw data can be ingested and stored right alongside an organization’s structured, tabular data sources (like database tables), as well as intermediate data tables generated in the process of refining raw data. Unlike most databases and data warehouses, data lakes can process all data types — including unstructured and semi-structured data like images, video, audio and documents — which are critical for today’s machine learning and advanced analytics use cases.
First and foremost, data lakes are open format, so users avoid lock-in to a proprietary system like a data warehouse, which has become increasingly important in modern data architectures. Data lakes are also highly durable and low cost, because of their ability to scale and leverage object storage. Additionally, advanced analytics and machine learning on unstructured data are some of the most strategic priorities for enterprises today. The unique ability to ingest raw data in a variety of formats (structured, unstructured, semi-structured), along with the other benefits mentioned, makes a data lake the clear choice for data storage.
When properly architected, data lakes enable the ability to:
