The Lakebed app can quickly create data lakes from any number of sources for easy storage, permission management, and API access.
What is a data lake?
A data lake is very similar to a data warehouse, a term you may be familiar with. It’s a place to store an organization’s data for use. We’ve all been in big warehouse stores, like Ikea or Costco, and have seen the rows of shelving for products. Think of a data warehouse in the same way: aisles (rows) with shelving (columns) full of products (data). One of the ways data warehouses have not kept up with changing demands is those shelves (columns) are very structured (inflexible) so they don’t handle new products well. Let’s say one of those warehouse stores wanted to start selling snowmobiles, but they don’t have a shelving unit specifically designed for snowmobiles. They couldn’t store/display the new products without new shelves and rearranging the entire store. Similarly, if you want to store new data in your data warehouse you have to change the column structure to fit.
A data lake is fluid. You may have heard of noSQL or unstructured data; data lakes store company/an organization’s data in noSQL. Now when you want to store/display that snowmobile, you just dump it in the lake and the water level rises a bit. Post-processing, after upload, you might create a bucket of data (“all snowmobiles”) and you could use the data in that bucket.
A company/organization might create a data lake including website analytics, inventory, order history, and accounting data. Then they could create a bucket of data for “the last 7 days” to view website visits, production, and sales for the last week. Or, a bucket called “everything in Western Canada” could generate a report of marketing and sales activity in Western Canada. If the company adds a new product or a new sales region, the data storage is flexible and can adapt.
- The Lakebed app can quickly create data lakes from any number of sources for easy storage, permission management, and API access.