It often led to issues such as data duplication, high maintenance costs, and security challenges.ĭata lakehouses emerged as a better solution to address those challenges. The result is that people often maintained both options simultaneously, linking them together to avoid the limitations of data lakes and data warehouses. Nonetheless, data lakes still had limitations, such as: They provided a low-cost and scalable option for analytics using various data types and formats. Further, they required data cleaning and transformation to accommodate such data types - this was time-consuming and expensive.ĭata lakes emerged in early 2010 as a solution to address the limitations of data warehouses. Yet, data warehouses couldn't support rapidly evolving unstructured and semi-structured data like pictures, videos or audio recordings. They were primarily designed to support data analytics and BI with efficient querying capabilities. Optimized access to ML and data science toolsĭata warehouses emerged in the 1980s as solutions for storing and managing structured data from various sources.Analytics-ready by supporting open data standards such as AVRO, ORC or Parquet.Data governance and auditing capabilities.Support for every form of data in any file format.The following is a list of features of a data lakehouse that inherits from data lakes and data warehouses. Some example projects in which it can be utilized include business intelligence (BI), data science, machine learning (ML), AI and SQL analytics. What to use data lakehouses for: Key featuresīecause it has the capabilities of both a data lake and a data warehouse, a data lakehouse can be used for several projects. In a data lakehouse, you also get data management, governance, ACID transactions and data quality-the primary offerings of data warehouses. Data lakehouses add in what data lakes lack.That means it is a cost-effective and flexible data storage solution, just as any data like is. This all-in-one platform enables storing data in raw formats, just like a data lake: in unstructured, semi-structured and structured ways.It addresses the limitations of data lakes and data warehouses when utilizing them separately. What is a Data Lakehouse?Ī data lakehouse is a data management solution that leverages the best features of a data lake and a data warehouse into a single, unified platform. This article explains data lakehouses, including how they emerged, how they shape up versus data lakes and data warehouses, their architecture, and finally, the pros and cons of using a data lakehouse. The features of a data lakehouse make it ideal for a range of data analytics use cases. It is popular among many organizations that incorporate the features of both data lakes and data warehouses. A data lakehouse is a modern data architecture.
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