News & Blog

SlamData Secures $6.7MM Series A to Support Modern Data in the Enterprise

Boulder, Colo., February 23, 2017 – SlamData Inc., the leading open source analytics company for modern unstructured data today announced that it has raised a $6.7M Series A funding round, led by Shasta Ventures. The investment will drive further development of the firm’s breakthrough analytics solution: a single application for natively exploring, visualizing and embedding analytics against unstructured data sources including NoSQL, Hadoop, and cloud API’s.

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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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Polyglot Persistence: Analytics Purgatory Or the Birth of A New Era of Analytics?

The exceptional thing that we did was to create an evolutionary approach to this problem. Not another silo or another one-off tool. We said, we’re going to create something that can do all the modern data structures that people are encountering like JSON, like XML, and other sorts of semi-structured data, but that also has the ability to be completely compatible with their existing legacy sources, the relational world.

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Battle of Open Source Analytics: Spark vs Drill vs Quasar

Modern companies want the ability to ask questions and derive insights from all their data, no matter its structure, and no matter its location. They want this power today, right now, without having to go through year-long data warehousing projects—projects that, due to the rapidly changing nature of modern data silos, are often obsolete before they are even finished.

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SlamData and Datavail Announce Strategic Partnership to Deliver the Next Generation Enterprise Analytics Solution Built for Complex, Modern Data

SlamData, Inc., the leader in NoSQL analytics, and Datavail, the leader in data management and managed database services, today announced they have partnered to deliver a next generation enterprise analytics solution for MongoDB. Through a formal partnership, the companies are able to provide clients with quick and robust access to analytics for MongoDB and soon other NoSQL platforms.

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SlamData’s Latest Release Delivers Breakthrough Embedded Analytics and Data Visualization Capabilities for MongoDB

SlamData, Inc., the company leading analytics for the post-relational age of NoSQL data, today announced its third major release in two years, SlamData 3.0. The latest release delivers critical front-end features that make it exceptionally easy to explore and visualize NoSQL data, and to securely embed interactive charts and reports into web pages and applications.

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Breakthrough Analytics for a New Generation of Data

Figuring out a way to express powerful analytics on this database required us to rethink the very foundations of relational analytics. After more than three years of napkin and blackboard development, what emerged from our quest for next-generation analytics is a groundbreaking extension to relational algebra called MRA (multi-dimensional relational algebra).

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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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