Full-Blown BI On MarkLogic.

Query, Chart & Build Dashboards On Live Data.

MarkLogic Analytics, MarkLogic BI, MarkLogic Business Intelligence

Enterprise Business Intelligence for MarkLogic

Connect and Go

SlamData delivers MarkLogic analytics within a few minutes and requires no software developer. This isn’t a few nifty chart – it’s enterprise grade and 100% customizable. 

Skip ETL = Save Big

Think you have to set up relational databases and create ETL workflows to mirror data from MarkLogic? Think again. With SlamData, you can get the analytics and reporting you need without involvement of IT.

Embedded Charts & Dashboards

In fifteen minutes, you can create beautiful, secure, and scalable dashboards that run on live MarkLogic data. Embed them into your websites and applications.

Stick With SQL

Your team already knows SQL. Use SQL to query MarkLogic.

Frequently Asked Questions

How does MarkLogic handle analytics or business intelligence now?

Like many modern NoSQL databases MarkLogic has does not natively support popular BI tools such as Tableau, PowerBI or Qlik due to the complex XML/JSON data model they support. MarkLogic provides a powerful query language, X-Query, but it is incompatible with traditional SQL based BI tools. Non-native solutions such as ODBC/JDBC drivers have yielded poor results and user experience. The other approach is to ETL all your data from MarkLogic to a traditional RDBMS that is compatible with existing BI tools, but this adds considerable effort and infrastructure cost. Additionally, forcing JSON data into flat tables creates data problems as noted in our whitepaper, The Characteristics of NoSQL Analytics Systems. One example is that data and schema are often the same thing in a JSON document, so declaring an individual field as schema renders it useless for further analysis under the ETL approach. Further, it is also not an agile approach and data can often be outdated by the time it’s ready for analysis.

What types of MarkLogic users can benefit from SlamData?

SlamData requires no programming skills at all. The user interface allows users to create completely visual reports in just a few clicks. For advanced users SlamData provides complete support for SQL, so power users can create the most complex analytic workflows they need. Developers can also use SlamData and save many hours by not needing to create custom queries, connect charting libraries and building secure API’s to share or publish reports. SlamData provides all this out of the box. Additionally SlamData provides support for adding dynamic variables to queries at runtime, and the ability to create virtual data marts that are fast and flexible. SlamData makes MarkLogic BI simple and easy for analysts, business users and developers alike.

What does SlamData provide to existing MarkLogic clients out of the box?

SlamData is the only 100% native solution for MarkLogic Business Intelligence. So what does this mean for users? It means SlamData provides a BI solution that works natively on the JSON data stored in MarkLogic, and provides the ability to do reporting, advanced visualization and complex analysis.  All analytic computations are executed 100% in MarkLogic, no data ever leaves the database, just the results are returned. SlamData does not extract data into cloud instances, or relational databases, ever. This means SlamData leverages your existing MarkLogic infrastructure and scales effortlessly as you data grows. Users can install SlamData, connect to their MarkLogic DB and immediately start creating powerful reports. No mapping, ETL, or data relocation required, users can start building and publishing reports instantly.

See Out-of-the-Box Analytics for MarkLogic

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Turnkey Solutions For Every Industry. Results Day 1.

SaaS Analytics

With SlamData you can see Immediately what your customers are doing — evaluate by cohort, look for outliers, track them over time. You can even use SlamData to create a dashboard or to deliver data BACK to your end users. SlamData Advanced offers Authentication, auditing and multi-tenant security.

Healthcare Analytics

With SlamData you can find patients, review their attributes, group patients, create dynamic reports and more. SlamData Advanced provide all the security features needed to ensure regulatory compliance.

IoT Analytics

SlamData lets you see your data as it’s created. You can build dynamic and interactive charts and dashboards so you can visualize what’s going on right now with 10, 10,000 or 10,000,000 devices.

Security Analytics

SlamData gives you insight into your data immediately. And as soon as you can define what the most important “view” of that data is for your clients, hit “publish” and share it with them. In fact, use our Multi-tenant feature to build dashboards for all of your clients.

It's incredible technology. At first we used it to save time but now we're using it to create opportunties we never thougt we'd have.

Jay - Software Developer

News, Blog and Updates

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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