While efficient and largely secure, data warehouses are costly and unable to ingest semi-structured or unstructured data.ĭata lakes emerged, able to handle all types of data - and with cheaper storage. They simply weren't built for the data challenges of today.ĭata warehouses support business intelligence and SQL applications. The good news? You have better choices than a low-cost data swamp of enterprise data or a rigid, limited data ingesting machine without artificial intelligence capabilities.ĭata lakes don't provide the data governance capabilities that you need to manage big data securely and data warehousing only ingests structured data without giving you the flexibility or scalability your growing business needs. Your data architecture must evolve to meet the needs of your business today while scaling to the promise of your business tomorrow. Compare: data lakehouse vs data warehouse vs data lake This is beneficial to data scientists, as data lakehouses support machine learning and business intelligence while also supporting SQL analytics, real-time data applications, and data science. Simply put: The data lakehouse is the only data architecture that stores all data - unstructured, semi-structured, AND structured - in your data lake while still providing the data quality and data governance standards of a data warehouse.
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