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.
The rewards of big data can be compelling. At the same time, you'll want to consider machine learning challenges before you start your own project. In this article, we cover challenges which include addressing the skills gap, knowing how to manage your data, and operationalizing your data.
New advancements enable us to do more with our big data than we’ve ever been able to before. But there are two other advances that are playing a huge role in this revolution - open-source software and cloud computing.
The data lake based on Apache Spark clusters and object storage is truly the best option now. Take a deep dive into why that has happened and the history behind it, and why Apache Spark and object storage are truly the best choice for you.
GDPR is fast approaching – May 25, 2018. And the implications for big data are, well, big. Essentially, GDPR is a regulation intended to strengthen and unify data protection for all individuals within the European Union, and it applies regardless of where the company is located.
Data lakes can hold your structured and unstructured data, internal and external data, and enable teams across the business to discover new insights. Here, we walk you through 7 best practices so you can make the most of your lake.
Data is now the world's most valuable resource. Ajay Banga, CEO of Mastercard, said, "I believe that the prosperity that oil brought in the last 50 years, data will bring in the next 50, 100 years if you use it the right way."