SlamData 4.0 Released, Adds Analytic Support for Apache Spark, Couchbase, and Marklogic

by | Oct 18, 2016 | Press Release, Releases

The leading solution for visual analytics on complex modern data continues to add more data sources, delivering on its vision of a single analytics solution for enterprise’s modern polyglot data problem.

Boulder, CO — SlamData Inc., the company building the industry’s first comprehensive Business Intelligence solution for complex modern data, today announced the release of SlamData 4.0, which marks the debut of new connectors for modern data sources. SlamData 4.0 now supports MongoDB, Apache Spark on Hadoop, MarkLogic and Couchbase.

SlamData’s mission has always been to completely solve the biggest problem facing enterprise BI — data chaos,” said Jeff Carr, SlamData’s CEO and cofounder.

SlamData’s mission has always been to completely solve the biggest problem facing enterprise BI — data chaos,” said Jeff Carr, SlamData’s CEO and cofounder.

“We proved that we could do analytics natively on any type of data without ETL, starting with MongoDB. Soon, we will cover all the most popular enterprise data sources to complete our mission to deliver BI across enterprise’s modern, polyglot data, including RDBMS, NoSQL Hadoop, and Streaming.”

What distinguishes SlamData from every other solution rests in its core innovation: Multi-dimensional Relational Algebra, a mathematical extension of relational algebra capable of performing advanced analytics on any data model as it exists. MRA is an industry breakthrough that provides the first fully general-purpose semantics for analytics on complex and heterogeneous data models found in Hadoop and NoSQL data stores. Combined with SlamData’s advanced analytics compiler, the technology is able to execute any type of analytics on any type of data, without data relocation or ETL.

“Enterprises are tired of BI analytics engines that can only handle small extracts of well-formed data from legacy data warehouses,” said John De Goes, CTO and cofounder of SlamData. “It just takes too long to get answers, and requires too many people and too much work. Companies want answers today, not 12 months from now, across hundreds of data silos, no matter how complex the data and no matter where it’s located. Powered by MRA, SlamData’s advanced analytics compiler pushes analytical computation on any kind of data to anywhere. It’s the biggest breakthrough in Enterprise analytics since the invention of OLAP.”

“It’s the biggest breakthrough in Enterprise analytics since the invention of OLAP.”

SlamData 4.0 is open source and is used by thousands across software development and data analysis projects in IoT, healthtech, SaaS, devops and more. The company’s proprietary licensed version, SlamData Advanced Edition, delivers out-of-the-box enterprise features that allow quick setup of complex, secure implementations including authentication, multi-tenant security, and auditing, as well as powerful developer APIs.

SlamData customers span all industries including healthcare, government, financial services and SaaS companies.  Users adopt SlamData to save considerable time and money over existing legacy solutions that require weeks to months to work with modern data models like JSON. With SlamData users reduce time to insight by 80% or more, and can be up and running in minutes, no development skills required.

The latest version 4.0 allows us to connect directly to collections in our MongoDB database without the need for special drivers or other connectivity software.

“Our lab’s project required a database to store patient clinical data and an application that would allow us to display the collected data graphically in a robust and efficient way. SlamData has proven to be the perfect solution. The latest version 4.0 allows us to connect directly to collections in our MongoDB database without the need for special drivers or other connectivity software. The robustness of the software in addition to the superb user support has provided a solution that has saved our lab considerable time and effort in achieving our project’s goals.”

Ken Aurebach, Systems/Database Engineer at Massachusetts General Hospital.

SlamData customers include Department of Homeland Security, HP Enterprise, and many others.

Contact

Jeff Carr
[email protected]
@slamdata

 

Native Analytics On MongoDb, Couchbase, MarkLogic and Hadoop.

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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
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