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The data lakehouse has captured the hopes of modern enterprises looking to combine the best of data lakes with the best of data warehouses. Like a data lake, it consolidates multi-structured data in flexible object stores. And like a data warehouse, it transforms and queries data at high speed.
While still in the early adoption cycle, businesses that implement data lakehouses can streamline their architectures, reduce cost and assist in governance of self-service analytics. From data mesh support to providing a unified access layer for analytics and data modernisation for the hybrid cloud, there are myriad use cases and even more to come.
But many don’t know where to start – and the risk of spending time and money only for it to go wrong is putting many off taking advantage of data lakehouses’ benefits. However, building and executing a data lakehouse strategy can be broken down into four simple steps.
Understand what a data lakehouse is, and isn’t
The data lakehouse seeks to combine the structure and performance of a data warehouse with the flexibility of a data lake. It’s a type of data architecture that uses data warehouse commands, often in structured query language (SQL), to query data lake object stores, on premises or in the cloud, at high speed.
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