SlamData 4.1 Includes New Charts, Updated Connectors for Couchbase, MarkLogic and Spark/Hadoop

by | Jan 26, 2017 | Releases

SlamData just released its first update of 2017, SlamData 4.1.1. It delivers a number of new UI enhancements, performance improvements, new charts, as well as commercial releases for the Couchbase, MarkLogic and Spark/Hadoop connectors.

SlamData 4.1 delivers improved performance and polish over 4.0, and raises the bar on what you can do visually without using SQL,” said John De Goes, CTO and Co-Founder. “In addition, our support for MarkLogic, Couchbase, and Hadoop/Spark has been through two months of beta testing, with many performance and quality improvements.


5 New Charts for Visualizing Your Data and Creating Rich Dashboards

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Data Sources

  • Completion of Couchbase connector; now supports key joins.
  • Completion of MarkLogic connector; now supports both XML and JSON.
  • Completion of Spark/Hadoop connector.

New Charts

  • Sankey
  • Boxplot
  • Punchard
  • Candlestick
  • Parallel

User Interface

  • Now any card in a deck can now be mirrored; Previously only the last card in a deck could be mirrored.
  • Forms can now be built using cards rather than Markdown. This makes adding menus and inputs to dashboards easier.


  • Improved caching.
  • Authentication is no longer attempted if a permission token is present. This increases the loading speed of decks with permission tokens by unauthenticated consumers.
  • The Embed Deck HTML has been improved. Embedded decks now work better out the box and also provide deck URLs which allows for simpler and more powerful integration with other applications.
  • Decks are now saved in a single workspace file which reduces the number of requests.
  • The open card now includes search making selecting a file from a long listing much easier.

Chris Dima

Director of Marketing at SlamData
Chris runs marketing at SlamData. He chants "No ETL" in his sleep. He has three girls. He can still ollie.

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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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