Operational Vs Analytical Big Data

Choose the right mix of technologies to leverage Big Data for greater success

In this modern era of Big Data where data is getting too unwieldy for older generations of technology to handle, there’s a new class of technologies sprouting up to meet the need. To succeed and pull away from the competition, you need a strong data management strategy that involves the right mix of technologies that meet your requirements.

These new technologies that have arisen in response to Big Data handle data creation and storage, retrieving and analyzing data. When you’re evaluating the different technologies to use, you typically encounter operational vs. analytical Big Data solutions. Operational Big Data systems provide operational features to run real-time, interactive workloads that ingest and store data. MongoDB is a top technology for operational Big Data applications with over 10 million downloads of its open source software.

Analytical Big Data technologies, on the other hand, are useful for retrospective, sophisticated analytics of your data. Hadoop is the most popular example of an Analytical Big Data technology.

But picking an operational vs analytical Big Data solution isn’t the right way to think about the challenge. They are complementary technologies and you likely need both to develop a complete Big Data solution.

MongoDB works well with Hadoop thanks to an API integration that makes it easy to integrate the two solutions. Many of our customers, such as the City of Chicago, have built amazing applications never before possible as a result of combining operational and analytical technologies.

Download our white paper on Big Data to learn more about the differences between operational vs analytical Big Data and much more.

Companies ranging from startups to Fortune 500s choose MongoDB to build, scale, and innovate.

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