There are many reasons to use a NoSQL database like MongoDB, but Pierre DeBois hones in one that doesn’t always get the attention it deserves: analytics.
As DeBois, founder of Zimana, writes, “NoSQL databases are gaining in popularity because they offer the scalability required for real-time processing of complex datasets.” As I’ve noted before, “Big Data” is sometimes the reason enterprises look to NoSQL, but sometimes they unnecessarily focus on MongoDB or another NoSQL database as an operational data store, and neglect its utility for analytics, too.
But as InformationWeek recently highlighted, identifying 10gen as one of the top Big Data vendors to watch in 2013, new functionality in MongoDB - specifically, the aggregation framework - make it a useful tool for a variety of analytics workloads:
[MongoDB’s] data aggregation framework fills an analytics void by letting users directly query data within MongoDB without using complicated batch-oriented MapReduce jobs.
This isn’t to suggest that MongoDB is a like-for-like replacement of map-reduce. It’s not. But for averaging field values or calculating totals, it’s super-fast and convenient.
And for the page load optimization work that Yottaa and Pingdom have engineered, MongoDB is an excellent fit, as DeBois describes. I’d love to hear examples of other companies using MongoDb for real-time analytics, and what your experience has been.
- Posted by Matt Asay, vice president of Corporate Strategy