For all its potential to improve query performance and reduce latency, in-memory processing has received relatively little attention in the database world beyond traditional relational systems like SAP SE’s HANA. But that is about to change. MongoDB Inc. officially joined the fray this morning with the introduction of an in-memory storage engine for its popular document store that promises to massively improve query throughput.

Providing the ability to avoid the delay of moving data back and forth from disk before every operation will make MongoDB much more attractive for high-performance use cases like fraud detection that require nearly instantaneous response times. The addition is as encouraging for customers as it is worrying for competitors like Aerospike Inc. and Couchbase Inc., which have enjoyed free rein with their in-memory NoSQL databases in the last few years.

MongoDB has raised twice as much venture capital as both of the companies combined and boasts a developer following to match that downloaded its namesake platform more than nine million times to date. At the same time, however, its rivals have had much longer time to polish their in-memory capabilities and, equally importantly, amass case studies they can show to potential customers. That competitive stalemate won’t be broken anytime soon.

As a result, MongoDB is refraining from putting all of its eggs in one basket. The company is rolling out a second storage engine to join the in-memory framework that places the emphasis on security instead of performance, enabling administrators to closely regulate access to encrypted data while it’s sitting around in storage waiting to be processed. The feature should prove particularly appealing to organizations in regulated industries that have to ensure their records comply with privacy regulations at all times.

Being able to take care of that chore directly through the native interface is a major convenience that MongoDB is topping off a new management tool called Compass that is debuting in conjunction with the goal of making it easier to search large deployments. Its centerpiece is a visual query builder that reduces the need to learn the database’s native syntax in order to find and manipulate information. That kills two birds with one stone, reducing the amount of effort involved in the process and lowering the entry barrier at the same time.

Image via Geralt