SlamData 3.0 and More

by | Jul 12, 2016 | Company News, MongoDB Analytics, Releases

SlamData 3.0 has shipped! This is our most significant release in the 2+ years of this project. It’s the right time for a check-in to talk about where we are and where we are headed.

What’s New? First Up Is SlamData 3.0

This is how easy it is to display live data form MongoDB. No ETL and no mapping. Read below for what’s new!

  • A completely new user interface that’s easy-to-use, beautiful and extremely powerful. You can now create any dashboard — static or interactive — you can imagine .The main highlights for this version are as follows:
  • Even more powerful developer APIs that let you simply embed charts and analytics into your MongoDB application.
  • A new extensibility framework that makes it easy for developers to write new connectors for other databases (like Couchbase, MarkLogic, or PostgreSQL, for example).
  • New docs! New docs are fresh out of the oven over here: It’s a new UI, there are new tools — go over and get yourself acquainted.
  • New charts! Tis the summer of charts at SlamData HQ and around the world with our international development team. Did you know SlamData uses the very awesome open source library from Baidu? It’s eCharts.  We’ll let everyone know when they’re added.

Download SlamData 3.0 Now >

What’s Coming? Spark, Visual Queries…

  • We have begun work on a new Spark connector, which will let you use SlamData on any data stored in Hadoop or any other data source that works with Spark. We expect to release this by fall, which will make SlamData the most powerful open source, visual analytics solution in the entire big data ecosystem!
  • Improving our user-interface, including adding more data visualizations and reducing the need to use SQL2. By the end of the year, you will be able to refine, filter, and aggregate any kind of semi-structured data in a completely visual way. Users will no longer need to know SQL2 to get maximum value from SlamData.
  • Oh yeah, number three: adding connectors for new data sources based on user priority. By the end of the year, we will support at least 4 new data sources. SlamData will become your go-to tool for all NoSQL analytics, no matter where the data is stored. We’ll even add a catch-all RDBMS connector for cross datastore joins. More on that later (actually sooner).

SlamData Cloud: Load Messy JSON, Explore, Visualize, Share

We’ll take your mess. Seriously. Load JSON, CSV, XML into the new SlamData Cloud and you can explore, search, refine and use SQL to create powerful queries. Then sculpt that insight into a powerful chart.

Then, of course, share it. No ETL or data mapping, EVER.

Learn more about SlamData Cloud >

What’s Changing? Getting to the Future Faster

As we have grown, the support requests for SlamData Community Edition has increased significantly. Our users want to do more. That’s great. But our primary focus is to add new features. To ensure our objectives are met AND users’ needs are met, we are introducing a new pricing model starting with SlamData 3.0. The Community Edition will continue to remain 100% open source (and free), but users will need to download the source files from Github and build the project themselves.

If you prefer the pre-built, visual installers for versions of the product that have been tested, users can pay a small monthly fee. The fee includes basic email support, too.

This allows us to remain opensource, build new features at an aggressive pace and offer faster support.

Our community support forum on Google Groups is always available for anyone to post questions and answers and get help from the community. SlamData Advanced for MongoDB is also available for those needing security features, such as authentication, authorization, auditing, and multi-tenant embedded reporting.

Download SlamData 3.0 Now >


Native Analytics On MongoDb, Couchbase, MarkLogic and Hadoop.

No mapping. No ETL.

Download It Now

Recent News & Blogs

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.

read more

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.

read more

Who Is Using SlamData?

Whitepaper: The Characteristics of NoSQL Analytics Systems

by John De Goes, CTO and Co-Founder of SlamData


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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