Intelligent metadata layers can also be used to categorize and classify the data, enabling it to be cataloged and indexed like structured data.ĭata lakehouses are particularly well-suited to inform data-driven operations and decision-making by organizations that want to move from business intelligence (BI) to artificial intelligence (AI). This enables data users, such as data scientists, to access information more quickly and efficiently. Organizations that work with unstructured data can benefit from having a single data repository instead of requiring both a warehouse and a lake architecture.ĭata lakehouses allow for structured and schema just like those used in a data warehouse to be applied to the unstructured data type that is typically stored in a data lake. This led to the development of the data lakehouse, which unifies structured and unstructured data in a single repository. To address these issues, a hybrid architecture was needed that could store both structured and unstructured data. While the data lake is a powerful and flexible infrastructure for storing unstructured data, it lacks certain critical features, such as transaction support and data quality enforcement, leading to data inconsistency. However, as businesses have started to appreciate the value of unstructured data, such as images, videos, and voice recordings, a new type of framework called the data lake has emerged. If you’re familiar with the term data warehouse, you’ll know that it’s a system for storing structured data for business intelligence and reporting purposes.

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