Frequently Asked Questions

How is it possible that SlamData does not require ETL or mapping semistructured arrays to relational data models?

Our analytics super compiler leverages advanced multidimensional relational algebra which allows standard SQL queries. Our technology also dynamically adjusts when new fields, arrays and subdocuments are noticed. This all happens live, with no administrator intervention necessary.

What are the system requirements for SlamData?

We recommend running SlamData within a virtual machine of some type where the administrator can increase the memory and CPU speed and number of cores when necessary.  Development environments typically consist of a dual or quad core system with 4GB of memory, with at least 2GB reserved for the JVM.  Larger production environments should start with quad cores and 6 – 8 GB of memory, with at least 4GB of memory reserved for the JVM.  SlamData requires approximately 500MB of installation space.  SlamData stores it’s analytical workflows in the target data source itself, so local hard drive space is not needed as the number of workflows grow.

Where can I run SlamData?

SlamData runs within a JVM so it can run on Linux, Windows and macOS.  SlamData can run in a virtual server, a physical bare-metal server and laptops.  It has been successfully deployed with Docker, VMware, Oracle VirtualBox and other container technologies.

How fast is SlamData?

SlamData pushes 100% of the computation down to the target data source so we limit both the amount of data we perform calculations on as well as the amount of data going over the network.  Every other solution available sends simple queries to a database and pulls back an unnecessarily large data set to computations against; SlamData sends an optimized, type-safe query to the target data source and forces the target data source to perform the calculations.  So in essence, SlamData runs as fast as your target data source can run the query.  SlamData is not a bottleneck in the analytics process.

Why would I choose SlamData over other solutions?

Other solutions do not provide fully embeddable workflows, SQL on NoSQL data models, live access to any and all data source targets (relational and non-relational), ability to host your own SaaS version of analytics based on per-customer non-materialized views or OAuth2 / OIDC security, auditing, all in one easy-to-use and simple installable package.

Do you offer commercial support for SlamData?

Yes, we do.  The Advanced Edition comes with Support where businesses have direct access to SlamData experts.

Do you offer consulting or other services?

Yes, we do, on a limited basis.  We offer a JumpStart program which helps get businesses off to a great start with a Proof of Concept (POC) or Minimum Viable Product (MVP) in a very short time.

Get a Jump Start!

Get Your POC Off the Ground Now.

What Our Customers Are Saying

We use SlamData to build custom reports and have found the tool is exceptionally easy to use and very powerful. We recently needed to engage the support team and we were very pleased with the turn-around time and the quality of support that we received.

Troy Thompson
Director of Software Engineering
Intermap Technologies, Inc.

When our company migrated from SQL database to MongoDB, all our query tools became obsolete. SlamData saved the day! I was able to easily write SQL2 queries. Plus the sharing, charting, and interactive reports were a game changer.

Michael Melmed
VP, Ops and Strategy
US Mobile

Slamdata helped shine the light on how our new product was being used. The support staff was awesome and we saved engineering cycles in building all the analytics in-house. I am using it to change the mindset in the teams and shift the focus from product launches to product landings

Engineering Lead
Cisco Systems

News, Analysis and Blogs

WHITEPAPER

The Characteristics of NoSQL Analytics Systems

  • 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

© 2017 SlamData, Inc.

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SlamData Provides Missing Platform for NoSQL Data Insight

This case study documents the return on investment, performance enhancements, and efficiency gains experienced by US Mobile resulting from its SlamData implementation. 
Download Case Study Now
The study was conducted by Constellation Research and published on June 25, 2017.
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