By IT Brew Staff
less than 3 min read
Definition:
Data lakes are repositories that gather a variety of structured and unstructured data from across an organization, and available to stakeholders throughout an organization for analysis, AI training, and more—provided those stakeholders extract and transform the data to meet their needs if it’s not already in the right format. Contrast that with data warehouses, where data has been structured according to a standard schema; stakeholders can then use attached dashboards and tools to generate reports and more.
A data lakehouse combines the flexible, low-cost storage of a data lake with the governance and data-refinement layers of a data warehouse. In practice, it means organizations can group significant amounts of unstructured, semi-structured, and structured data in one place, then expect that data will be processed in ways that make it accessible for analysis, modeling, and more.
Data lakehouses can serve as a single source of truth for organizations, with data engineering, business intelligence, and AI experts all fetching the data they need for different projects and tasks, especially data-intensive tasks like training machine-learning models. This architecture is also useful for governance and versioning, allowing organizations to effectively audit data and trace its lineage if necessary.
