The First Business Intelligence Solution Built for NoSQL: MongoDB, Couchbase, MarkLogic and Spark/Hadoop

Skip Mapping Fields. Skip ETL. Have Fresh Insight On Live NoSQL Data.

The First Business Intelligence Solution Built for NoSQL

Skip Mapping Fields. Skip ETL. Have Fresh Insight On Live NoSQL Data.

“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, Massachusetts General Hospital

A Single Solution That Lets You Query, Visualize
& Share Insight From the Leading NoSQL Databases

A Single Solution That Lets You Query, Visualize
& Share Insight From the Leading NoSQL Databases

Demo: From Query to Chart On Live NoSQL
(View Time: 1m 18s)

Enterprise Analytics Out-of-the-Box Across Industries

Transformational Features for Enterprise Business Intelligence and Analytics

In-Database Analytics

SQL As the Lingua Franca

Shareable, Embeddable

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The Freshest Insight

News, Blog and Updates

Getting Started With SlamData – Part 1

Welcome to the SlamData getting started video. Let’s jump right in. By default, SlamData runs on port 20223. You can change the port it runs on by modifying the quasar-config.json file. By default, this file is located in the following directories on Windows, Mac and Linux.

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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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