Full-Blown BI On MongoDB.

Query, Chart & Build Dashboards On Live Data.

MongoDB Analytics, MongoDB Business Intelligence

See Out-of-the-Box Analytics for MongoDB

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Enterprise Features Will Transform Your Business 

Connect And Go

SlamData delivers MongoDB analytics within a few minutes and requires no software developer. This isn’t a few nifty charts – it’s enterprise grade and 100% customizable.

Skip ETL = Save Big

Think you have to set up relational databases and create ETL workflows to mirror data from MongoDB? Think again. With SlamData, you can get the analytics and reporting you need without moving any data.

Stick With SQL

MongoDB’s query language is verbose. And it requires you to learn it! Your team already knows SQL so let them do what they already do well. 

Embed Charts & Dashboards

In fifteen minutes, you can create beautiful, secure, and scalable dashboards that run on live MongoDB data. Then embed them into your websites and applications.

FAQs

Does SlamData require users to move their data?

No. What is unique about SlamData’s approach is everything is done “In-database.” The web-based interface for SlamData is where users create the query — computation is done in the target database. ETL is a thing of the past.

Is SlamData a cloud service?

SlamData is built to be run on a computer or a server. It can run on Linux, Mac and Windows. If you want it to be a cloud service, it can be a cloud service. We offer assistance in setting up cloud deployments.

Do I have to know MongoDB's query language?

No. Everything you do with SlamData is done with SQL2. SQL2 is a super-set of SQL — we added a few operators to standard SQL to allows users to query rich and complex data structures.

Which databases or data sources does SlamData support now?

Currently SlamData supports MongoDB, Couchbase, MarkLogic and Hadoop. New connectors are released on a rolling schedule.

Do you need a developer to install SlamData?

Software developers are not required to run SlamData. A user can download SlamData, install and launch it, connect it to a database, query data, create a chart and embed in about 15 minutes. We’ve timed it.

Don’t expect to fire up your existing analytics tool, point it at MongoDB, and go.

This guide explains why a fundamentally different approach is necessary and what your options are.

Turnkey Solutions For Every Industry. Results Day 1.

SaaS Analytics

With SlamData you can see Immediately what your customers are doing — evaluate by cohort, look for outliers, track them over time. You can even use SlamData to create a dashboard or to deliver data BACK to your end users. SlamData Advanced offers Authentication, auditing and multi-tenant security.

Healthcare Analytics

With SlamData you can find patients, review their attributes, group patients, create dynamic reports and more. SlamData Advanced provide all the security features needed to ensure regulatory compliance.

IoT Analytics

SlamData lets you see your data as it’s created. You can build dynamic and interactive charts and dashboards so you can visualize what’s going on right now with 10, 10,000 or 10,000,000 devices.

Security Analytics

SlamData gives you insight into your data immediately. And as soon as you can define what the most important “view” of that data is for your clients, hit “publish” and share it with them. In fact, use our Multi-tenant feature to build dashboards for all of your clients.

It's incredible technology. At first we used it to save time but now we're using it to create opportunties we never thougt we'd have.

Jay - Software Developer

News, Blog and Updates

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

 

 

Who Is Using SlamData?