## Metadata - Author: [[monte-carlo-data|Monte Carlo Data]] - Full Title:: Data Lakes vs. Data Warehouses: The Truth Revealed - Category:: #🗞️Articles, [[Lakehouse on GCP|Lakehouse On Gcp]] - URL:: https://www.montecarlodata.com/blog-data-lake-vs-data-warehouse/ - Finished date:: [[2023-03-30]] ## Highlights > data warehouses typically require more structure and schema, which often forces better data hygiene and results in less complexity when reading and consuming data. ([View Highlight](https://read.readwise.io/read/01gwqkdgwwdxdcbdsbfhdkj4wd)) > data lakes are the do-it-yourself version of a data warehouse, allowing data engineering teams to pick and choose the various metadata, storage, and compute technologies they want to use depending on the needs of their systems ([View Highlight](https://read.readwise.io/read/01gwqkekfgf764mbam10e9q8d0)) > Data lakes are ideal for data teams and data scientists looking to build a more customized platform, often supported by a handful (or more) of [data engineers](https://www.montecarlodata.com/blog-the-future-of-the-data-engineer/). ([View Highlight](https://read.readwise.io/read/01gwqkf2433eag4je7p4dmtq6s)) > As more use cases emerge and more stakeholders (with differing skill sets!) are involved, it is almost impossible for a single solution to serve all needs ([View Highlight](https://read.readwise.io/read/01gwqkmwwpmvvk8xfmdxeds1m0))