Data lake or data warehouse? What is the difference and what works best? DATAVERSITY brought this interesting topic to our attention in their article, “Data Lake or Data Warehouse: Which Is Right for You?”
First off, data lakes and data warehouses are both widely used for storing big data, but they are not interchangeable terms. A data lake is a vast pool of raw data, the purpose for which is not yet defined. A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose.
The two types of data storage are often confused, but are much more different than they are alike. In fact, the only real similarity between them is their high-level purpose of storing data. The distinction is important because they serve different purposes and require different sets of eyes to be properly optimized. While a data lake works for one company, a data warehouse will be a better fit for another. But, you don’t have to choose just one. Many organizations use both a data lake and a data warehouse.
Melody K. Smith
Sponsored by Access Innovations, the world leader in taxonomies, metadata, and semantic enrichment to make your content findable.
