Online Vs Offline Big Data

Big data is a big buzzword these days but many are not sure exactly what it means or how to determine a good strategy for their data. Being able to define your data management strategy can mean the difference between being a market winner or loser.

So what is Big Data? Simply put, it refers to data sets so massive in volume and complexity that they cannot be effectively managed by traditional software tools. The effort to harness Big Data involves many new technologies that handle data creation, storage, retrieval and analysis of data.

Technologies that support Big Data typically fall into two classes: Online Big Data technologies and Offline Big Data technologies. Online Big Data systems offer operational capabilities for real-time, interactive workloads where data is ingested and stored. Examples of these applications include social networking news feeds, real-time ad servers, analytics tools, and CRM applications.

In contrast, Offline Big Data systems offer analytical capabilities for retrospective, sophisticated analyses that may touch most or all of the data. Hadoop is an example of an Offline Big Data technology. But online vs offline Big Data isn’t about figuring out which one you need over the other. You most likely need both.

MongoDB is an online Big Data technology that serves as an operational data store for today’s Big Data applications. The database also integrates well with many offline Big Data solutions so that you can come up with a complete data solution. Enterprises from startups to large corporations are relying on MongoDB to get them to production on their Big Data applications faster and with less effort and risk.

Download our white paper on Big Data to learn more about the differences between Online Vs Offline Big Data and much more.