## Metadata - Authors: [[James Serra's Blog | Big Data]] [[Data Warehouses]], [[Data lakes]], [[Lakehouse on GCP]] - Full Title:: Is the Traditional Data Warehouse Dead? - Category:: #🗞️Articles - URL:: https://www.jamesserra.com/archive/2017/12/is-the-traditional-data-warehouse-dead/ - Finished date:: [[2023-03-30]] ## Highlights > First lets talk about cost and dismiss the incorrect assumption that Hadoop is cheaper: Hadoop can be 3x cheaper for data refinement, but to build a data warehouse in Hadoop it can be 3x more expensive due to the cost of writing complex queries and analysis (based on a [WinterCorp report](https://www.scribd.com/document/172491475/WinterCorp-Report-Big-Data-What-Does-It-Really-Cost) and my experiences). ([View Highlight](https://read.readwise.io/read/01gwqm4cxpcqcyxyyfb416va8c)) > Data lakes offer a rich source of data for data scientists and self-service data consumers (“power users”) and serves analytics and big data needs well. But not all data and information workers want to become power users ([View Highlight](https://read.readwise.io/read/01gwqm5tyfb4t0hxjybfx7nd60)) > traditional relational data warehouse should be viewed as just one more data source available to a user on some very large federated data fabric. It is just pre-compiled to run certain queries very fast. And a data lake is another data source for the right type of people. A data lake should not be blocked from all users so you don’t have to tell everyone “please wait three weeks while I mistranslate your query request into a new measure and three new dimensions in the data warehouse”. ([View Highlight](https://read.readwise.io/read/01gwqmahdmv7qbte6bx7zkbcjg)) > But most business users get lost in that morass. So, someone has to model the data so it makes sense to business users ([View Highlight](https://read.readwise.io/read/01gwqmav4nynxknx6remqx79w7))