Why SlamData?

The First Native BI Solution Built for Schema-less Data

Modern databases liberated developers from fixed schema and transformed development. But traditional analytics and BI tools didn’t keep up with innovation. Hundreds of analytics vendors claim support for flexible schema data sources like NoSQL DB’s but they all require users to maps fields, export data to a de facto temporary data store or in some way declare a fixed schema — this is the wrong approach for modern data. SlamData is the first analytics platform that can provide business intelligence on data that changes — the most complex, most dynamic data.

The Mathematical Foundation for Modern Data

All existing analytics tools are built on the foundation of relational algebra. SQL became the dominant tool to query tables and rows. And it worked well for forty years. New data formats, however, broke the system: JSON, for example, isn’t a set of rows and tables. Today, BI platforms force JSON into a flat structure in order to be able to work with it. Multidimensional Relational Algebra, or MRA, disrupts this status quo and provides the methodology and tools that allow SQL, along with 8 new operators, the ability to natively work with modern data — without moving or mapping any data.

Analytics Application Builder

SlamData is much more than just a simple charting or visualization tool. SlamData allows users to create advanced, interactive analytic workflows that can be deployed securely to thousands of users. Authors can link together as many functions as needed and build off of prior computations or results. The result — a true analytics application builder — enables analysts to build sophisticated workflows, add variables dynamically to queries, create interactive form elements and much more.

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Who Chooses SlamData?

  • Companies with large amounts of complex data with flexible or variable schema?
  • Companies that need to create interactive and flexible end-user reports and dashboards.
  • Companies that need to create a wide variety of reports quickly and easily — and in minutes not days or weeks.
  • Companies that need analytics within their own networks.
  • Companies that need to deliver reporting to hundreds, thousand or more of end users.

Agile Analytics = Faster Time-to-Insight

Traditional approaches to enterprise analytics have quickly become ineffective in the face of new database technology.
Agile analytics, via SlamData’s open source compiler technology deliver always-fresh data — and insight, too.

Key Benefits of Using SlamData

Get Insights Now, Not Later.

In a matter of minutes, you can explore data in MongoDB, Hadoop, MarkLogic and Couchbase (and more coming) without moving it.

You can create beautiful, secure, and scalable dashboards that run on live, modern, complex — messy —  data. Waiting days and weeks is over.

No More Spending 80% On ETL and Mapping

ETL is the status quo. It’s expensive to do. It takes time, lots of time. And moving data often means flattening it. Our solution lets you leave data where it is. Sending analytics to the data always made conceptual sense. Now it’s possible with SlamData.

Explore, Visualize and Report On the Same Platform

Just connect to your data source and go. Explore it, chart it, dashboard it, share it.  Lots of clients see SlamData as an analytics application builder. It’s because our solution lets you build complex workflows that you can make interactive — and you can embed them anywhere.

Leverage Your Team's SQL Skillset.

SQL has been the status quo for 40 years. Most likely you have lots of folks trained on how to use it. SlamData runs on SQL. The bonus is that if you want to try out new data sources you side-step learning new query languages.

Enterprise Features

Beautiful Interactive Dashboards

Visually build stunning interactive dashboards that run live on your MongoDB data. All without flattening, relocating, or extracting your data in any way.

NoSQL No Problem

Go crazy exploring and visualizing any NoSQL data inside MongoDB. Nothing’s a problem, not even nested data, arrays, data-in-schema, or heterogeneous schema.

Powerful Authoring

Authors can construct whatever dashboard, interactive report, or analytic workflow they want with a powerful interface based on stacking different card types in a deck.

Turbocharged SQL

Query MongoDB with SQL that’s translated into efficient and robust queries that leverage MongoDB’s find, aggregate, and map/reduce query interfaces.

Whitelabel Analytics

Embed any dashboard or any chart into web pages, web applications, and mobile applications, and customize colors and logos to project your brand identity.

Multi-Tenant Security

Partition collections of data into individually secured silos for each of your users or customers, so everyone can see exactly what they should, and nothing more.

Programmatic Embeddability

Developers can embed dashboards and charts into applications, and customize them with customer-specific permissions and other data for infinite flexibility.

One-Click Caching

Some reports and analytics take too long to run, even when MongoDB is properly indexed. Cache them with a single click to make them lightning fast.

Secure SSO

You already have SSO for your employees, and SlamData snaps into your current SSO provider so you don’t have to worry about yet another authentication provider.

Easy Sharing

Share dashboards, charts, and data sets with other users and groups of users, and give others the ability to read or write all artifacts. It’s like Google docs, but for analytics!
SlamData Connects To These Leading Data Sources. More On the Way In 2016.

Turnkey Solutions Across Leading Industries

Want To Connect Your BI Tool To A Modern Database?

Your Guide to Understanding BI Connectors for NoSQL data

The idea of a BI Connector for NoSQL (JSON) databases seems obvious.  Popular BI tools like Tableau and PowerBI are widely used by enterprises. These same enterprises are adopting modern databases like MongoDB and Couchbase that store data in very different ways from traditional relational databases (RDBMS) like Oracle and MySQL. So it is very natural for enterprise users to want continue to use the tools they know even when faced with a modern database that stores data in JSON format. Herein lies the challenge!

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NoSQL DB’s are not just simple variations on the RDBMS theme, but are in fact radically different.

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

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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Who Is Using SlamData?