In this blog archive, you will find a compilation of data lake news articles. For big data storage needs, companies use data warehouses, data lakes, or a combination of both.
Acting as a central repository, data lakes leverage a flat architecture to store raw, untransformed data for use a later time. Data lakes are typically synonymous with Hadoop technology, although more companies are opting for cloud-based data offerings from the likes of AWS, GCP, and Azure. The data stored in a data lake is often uncurated and can originate from relational and non-relational sources. Data lakes serve as an alternative to data warehouses where data must be structured and packaged for consumption and optimized for fast SQL queries. Because data lakes store all data and support all data types regardless of source, data scientists have access to more comprehensive sets of data.
Any large pool of data where the schema and data requirements are left undefined until the moment it is queried can be referred to as a data lake. While data lakes can have the mechanisms and structure applied to help all users, business analysts may prefer data warehouses for quicker visualization and batch reporting. However, data lakes can adapt more quickly to changes, whereas data warehouses are complex in nature and take considerable development time. Data lakes typically require some semblance of structure and cataloging to generate the most value for all users.
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