Data lakes are fast becoming valuable tools for businesses that need to organize large volumes of highly diverse data from multiple sources. However, if you are not a data scientist, a data lake may seem more like an ocean that you are bound to drown in. Making a data lake manageable for everyone requires mindful designs that empower users with the appropriate tools. A recent webcast conducted by TDWI and Oracle, entitled "How to Design a Data Lake with Business Impact in...
The data lab is a separate environment built to allow your analysts and data scientists to figure out the value hidden in your data. The data lab helps you find the right questions to ask and, of course, put those answers to work for your business.
Today we’ll be discussing machine learning, data lakes, and how they can help you exploit data about your business, your customers, your partners, and anything else you need to get that competitive edge. Essentially, we’re here today to say—what’s the extra information you need to get ahead?
There are many different ways to build a recommendation engine and most will combine multiple techniques or approaches. In this article, I want to cover just one approach, association rules, which are fairly easy to understand and require minimal skills in mathematics. If you can work with simple percentages, there’s nothing more complex than that here.
Interactive data lake query at scale is not easy, but it's essential for truly understanding the value of your data. In this article, we’re going to take a look at some of the problems you need to overcome to make full productive use of all your data.
While you can run your business on the data stored in Oracle Autonomous Data Warehouse, there’s lots of other data out there which is potentially valuable. Using Big Data Cloud, it’s possible to store and process that data, making it ready to be loaded into or queried by Autonomous Data Warehouse Cloud. The point of integration for these two services is object storage.