Analytics for Security Applications

Easily Explore And Visualize Your Application’s Data Then Build and Share Interactive Dashboards Anywhere.

And You Don’t Have To Move Your Data. At All.

See The Data Flow Through Your Security App As It Happens

Once you connect SlamData to your data source you can start querying, visualizing and understanding your data. We’re talking minutes, not days or hours.

With SlamData you will now have 100% transparency into you app — and your business.

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Create Charts And Dashboards, Put Them 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. This is the flexibility your IoT project needs to scale.

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Scale Effortlessly

We push down analytics to your data so SlamData scales along with your infrastructure.  And more sensors, more reports, more data, more data, more data. 

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

MongoDB Analytics for Security Apps
Hadoop Analytics for Security Apps
Couchbase Analytics for Security Apps
MarkLog Analytics for Security Apps

Security Analytics News 

Security Analytics Video

Schedule 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

Overview

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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