Taking in the View at the Data Lakehouse
If you’re looking to modernize your data, there’s no avoiding the lake analogies. So we figured we’d jump right into the Data Lake, Data Warehouse, and the brew of both: Data Lakehouse. Join us as we explain what they mean for the uninitiated.
What is a data lake?
Simplified, it’s the storage where data is pulled from. A data lake holds all your data (structured and unstructured) in its original format. It has not yet been organized. A data lake is a hoarder house – it has everything you could ever want. The data sits and waits; since it’s in a raw format, it can be quickly transformed into something that can be used for data-driven insight. Like a hoarder house, it can be difficult to find what you need, and much of it is trash— but it’s there.
What is a data warehouse?
A data warehouse is a structured, more refined version of a data lake. If you’re packing up your house for a move, the data warehouse has your kitchenware, clothes, and furniture all boxed up, labeled, and ready to go. It’s organized. It’s stored. It’s defined and ready to be used.
How do they work together?
Before the move to the data warehouse, someone has ordered what to pack and what not to pack in the big move. The data in the data warehouse is not yet useful because it hasn’t been told what its purpose is. The excess of messy data can be challenging.
What is a Data Lakehouse?
Data Lakehouse, a term coined by Databricks, refers to a hybrid mixture of a data warehouse and data lake. The concept combines the best elements of both while avoiding the drawbacks. Data Lakes are useful because they are great for data storage; Data Warehouses are useful because they’re organized. Together, they’re affordable, convenient, and offer data teams a quicker time-to-analysis.
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