The Easiest IoT Analytics You'll Ever Meet

Effortlessly Explore & Visualize Your Data, Turn Your Insights Into Interactive Dashboards, Embed Them Wherever You Want. Then Scale It.

Track Everything, As It Happens, Without Code

Once you connect SlamData to your data source you can start querying, visualizing and understanding your data — your business. No developer needed.

You can leave your data wherever it is because our analytics work on your data WHERE IT IS. That’s another way of saying this: no more ETL, no more mapping, no more waiting, no more wondering.

Embed Everything, Anywhere: Charts and Full-Blown Dashboards

Once you find the data you’re looking for — or the chart that explains it all — you can share that data or embed it into an intranet or external customer-accessible dashboard.

Our security model supports multi-tenant security. The flexibility to serve your customers is unprecedented.

Scale It

We “push down” analytics to your data, that means computation is in your database.  What does that mean? SlamData scales along with your infrastructure. It’s lightweight.  

The Features Will Change the Way You Do Analytics On IoT. And How You Run Your IoT-Based Business.

Use-Case Spotlight

The Client

  • A regional provider of commercial wifi network setup and monitoring.
  • The hardware company that they work with uses MongoDB to capture network and device data.

The Problem(s)

  • Growth and innovation. They want it but can’t easily extend their base offering because they are limited by the reporting that comes out of the box with their supplier.
  • If they could easily see ALL of their data — not just the prescribed reports — then they could start offering new and different things — especially on a segment-by-segment basis.

The Breakthrough

  • SlamData offers this client and quick and easy way around the limitation they thought were “baked in”.
  • Now they can create a new dashboard in a few hours and deliver to a segment and see if it helps… if it’s monetizable. If not, they can go to the next segment or redo the dashboard.
  • Rapid prototyping arrived for this client when SlamData delivered unprecedented transparency and reporting flexibility.

What’s Your App Built On?

More Database Connectors Coming Soon

IoT Analytics New & Blogs

IoT Video

Request A Demo

SlamData allows us to connect directly to collections in our MongoDB database without the need for special drivers or other connectivity software.

Ken - Database Engineer

Who Is Using SlamData?

Whitepaper: The Characteristics of NoSQL Analytics Systems

by John De Goes, CTO and Co-Founder of SlamData


Semi­structured data, called NoSQL data in this paper, is growing at an unprecedented rate.  This growth is fueled, in part, by the proliferation of web and mobile applications, APIs, event­-oriented data, sensor data, machine learning, and the Internet of Things, all of which are disproportionately powered by NoSQL technologies and data models.

This paper carves out a single concern, by focusing on the system­-level capabilities required to derive maximum analytic value from a generalized model of NoSQL data. This approach leads to eight well­-defined, objective characteristics, which collectively form a precise capabilities­-based definition of a NoSQL analytics system.

These capabilities are inextricably motivated by use cases, but other considerations are explicitly ignored. They are ignored not because they are unimportant (quite the contrary), but because they are orthogonal to the raw capabilities a system must possess to be capable of deriving analytic value from NoSQL data.

Table of Contents

  • Overview
  • The Nature of NoSQL Data
    • APIs
    • NoSQL Databases
    • Big Data
    • A Generic Data Model for NoSQL
  • Approaches to NoSQL Analytics
    • Coding & ETL
    • Hadoop
    • Real-Time Analytics
    • Relational Model Virtualization
    • First-Class NoSQL Analytics
  • Characteristics of NoSQL Analytics Systems
    • Generic Data Model
    • Isomorphic Data Model
    • Multi-Dimensionality
    • Unified Schema/Data
    • Post-Relational
    • Polymorphic Queries
    • Dynamic Type Discovery & Conversion
    • Structural Patterns



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