Easy Analytics for Healthcare. No IT Required.

No Heavy Lifting. No Developer Needed. Connect SlamData and Get The Insights and Reports You Need for Your Project, Research, Study or App.


Insights Faster From More Complex Data

Chances are you have data from many different places.  That’s where SlamData excels. Our contribution is about helping you find the needle in the haystack. Our wheelhouse IS complex data. Don’t worry, no mapping required. 


Easy Charts, Powerful Dashboards, Embed Anywhere

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 dashboards.  Our security model enables multi-tenant security so your data and insight is secure.


Scale It

We push down analytics to your data so SlamData scales along with your infrastructure. Another “easy” part of working with SlamData. 

What’s Your App Built On? More Coming Soon!

MongoDB Analytics for Healthcare
Hadoop Analytics for Healthcare
Couchbase Analytics for Healthcare
MarkLogic Analytics for Healthcare

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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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