How to get MongoDB Data to Analytics? All you need to know

You might have GBs to TBs of data sitting in MongoDB—user events, logs, application & transaction details, product catalogs—but every time you try a complex analytical query, it’s either painfully slow or just times out.

In this post, we’ll go beyond the surface. We’ll dissect why this happens, show what’s typically done in the market today, and explain how Datazip’s OneStack can help you run those very same queries in seconds.

We’ll also share a benchmark scenario where the same query that took over a minute on MongoDB can be executed in seconds using our platform. Let’s dive in.

Our goal is simple: Help you understand the core reasons behind slow analytics on MongoDB and guide you towards a solution that actually works.

MongoDB is a NoSQL document database, primarily designed for operational workloads (think: quick inserts, flexible schemas, and supporting a large number of concurrent read/writes for a web or mobile app).

While it’s great at handling user-facing queries, it’s not built to handle heavy analytical workloads natively. Here’s why:

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