Tom Merritt explains how a data lakehouse tries to give you the best of both a data warehouse and a data lake. Data doesn’t live in the cloud, it just Continue Reading
Tom Merritt explains how a data lakehouse tries to give you the best of both a data warehouse and a data lake.
Data doesn’t live in the cloud, it just passes through it – in fact, it rains data, forming lakes, which combined with your data warehouse can become a lakehouse. If these sentences make sense to you, then send this Top 5 to a colleague who doesn’t get it.
Here are five things to know about data lakehouses.
1. What is a data warehouse? It usually refers to a home for structured data. If you have a question, you may find the answer in the data warehouse.
2. What is a data lake? It’s essentially where you throw the data you think might be important, but you don’t know what to do with it. You can run the data into the warehouse or send it right to the machine learning algorithm.
3. A data lakehouse tries to give you the best of both worlds. It’s easy and therefore low cost like a data lake. And, it has some of the benefits of being able to answer questions, like a data warehouse, without having to have the data warehouse.
4. The key to a data lakehouse is the metadata layer. An open source layer like Delta Lake tracks files, supports streaming I/O, data validation and more.
5. Data lakehouses are on the rise. According to Ventana research, 73% of organizations are combining their data lakes and data warehouses. Data lakehouses can be built on top of existing systems, including S3, HDFS and more.
While working on this Top 5, the autocorrect algorithm kept changing lakehouse to bakehouse, which I assume means data bakehouses will be the next evolution in data analysis.
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