SlamData 2.0 Released- Enterprise-Certified Native Open Source Analytics and Reporting for MongoDB

Jeff Carr
Jeff Carr CEO & Co-Founder
SlamData, Inc., commercial developer of the SlamData open source project, today announced the availability of version 2.0 of their MongoDB analytics solution.

A new MongoDB Enterprise-certified version of the popular open source project features a brand new HTML5 user-interface that makes complex analytics and rich reporting accessible to non-technical users

SlamData, Inc., commercial developer of the SlamData open source project, today announced the availability of version 2.0 of their MongoDB analytics solution. The solution has been certified against MongoDB Enterprise, the commercial edition of MongoDB. The announcement was made during MongoDB World, MongoDB’s global conference taking place in New York City at the Sheraton New York Times Square. SlamData is exhibiting at booth #19.

SlamData provides a SQL-based interface to the popular MongoDB database. It is built on a formal generalization of 40-year old relational algebra called MRA (“Murray”). SlamData’s technology allows effortless manipulation and querying of the type of data stored in NoSQL databases. This data is often heavily nested, denormalized, and heterogeneous, and cannot be stored in RDBMS, let alone queried with traditional SQL.

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Previously, to perform analytics and reporting on MongoDB, developers often had to write complex code to low-level APIs, or develop expensive and brittle Extract-Transform-Load (ETL) processes to normalize, homogenize, and relocate data into relational databases such as Postgres.

Timed to coincide with MongoWorld 2015, the 2.0 release of SlamData provides a completely new, high-level interface that allows anyone to perform visual data exploration, ad hoc queries, and reporting on data stored in MongoDB. A separate developer API provides programmatic access to SlamData’s SQL-based interface, which can be leveraged from within applications built on MongoDB for embeddable reporting use cases.

According to SlamData CEO Jeff Carr, a veteran entrepreneur who has spent years working in the analytics space, “Tableau reimagined analytics for the relational world, and Splunk did it for the unstructured world. SlamData is doing the same for the semi-structured world of NoSQL data. With SlamData 2.0, companies using MongoDB now have a code-free, ETL-free solution that enables standards-based, embeddable analytics and reporting.

“Ease of use has been a key contributing factor to the growth of our database. SlamData brings the same ease of use to analytics, engineered for the modern data stored in MongoDB, says Kelly Stirman, vice president of strategy, MongoDB. We are excited to see how our users will leverage this release.

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Since its release in October of last year, the number of users of the open source project has swelled into the thousands, including many Fortune 500 companies. Companies are flocking to the project because it brings a SQL-based interface to the MongoDB database and pushes 100 percent of query computation into the database.

The company’s patent-pending technology, invented by SlamDataCTO John A. De Goes, is based on a mathematical generalization of relational algebra, which allows slicing, dicing, and aggregation of deeply nested, heterogeneous data of the type often stored in MongoDB. De Goes, a mathematician turned computer scientist and well-known expert in big data analytics, started the open source project more than a year ago.

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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.

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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.

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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

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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

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