Data lake is usually known as the ingestion of data, regardless of source or structure. And in the big data lake approach means that there’re no silos.
It’s important to understand that the image of a data lake isn’t entirely correct, it’s not just some big chaotic data swamp. To use the image of the streams going into the lake: there’re filters in place before the water actually goes into the lake. Same with the data lake where quite some technologies, protocols & so forth involved to organize the data.
Sometimes data sets can seem to be non-related, however it’s still possible to detect patterns (using AI) between purchasing behavior & weather patterns, between customer data from one source & customer data from another, between traffic & pollution data, the list goes on.
What can you do with these patterns?
Data Analytics & real-time actions based on real-time analytics.Data lakes are fit to leverage big quantities of data in a consistent way.
Next, data lakes are highly scalable & flexible. The system & processes can easily be scaled to deal with ever more data.
Still thinking if you should get all your data under one roof and manage it effectively?
EDM++ can be the solution you need for a fast transition & efficient management of all your organization data.