From the about page:
SlamData was formed in early 2014 in recognition that the primary methods for analytics on NoSQL data were far too complex and resource intensive. Even simple questions required learning new technolgies, writing complex ETL processes or even coding. We created the SlamData project to address this problem.
In contrast to legacy vendors, which emphasize trying to make the data fit legacy analytics infrastructure, SlamData focuses on trying to make the analytics infrastructure fit the data.
The SlamData solution provides a common ANSI SQL compatible interface to NoSQL data. This makes modern NoSQL data accessible to anyone. SlamData retains the leading developers of the SlamData open source project and provides commercial support and training around the open source analytics technology.
I first encountered SlamData in MongoDB gets its first native analytics tool by Andrew C. Oliver, who writes in part:
In order to deal with the difference between documents and tables, SlamData extends SQL with an XPath-like notation. Rather than querying from a table name (or collection name), you might query
FROM person[*].address[*].city. This should represent a short learning curve for SQL-loving data analysts or power business users, while being inconsequential for developers.
The power of SlamData resides in its back-end SlamEngine, which implements a multidimensional relational algorithm and deals with the data without reformatting the infrastructure. The JVM (Scala) back end supplies a REST interface, which allows developers to access SlamData’s algorithm for their own uses.
My major curiosity is about the extension to SQL and the SlamEngine’s “multidimensional relational algorithm.”
I was planning on setting up MongoDB for something else so perhaps this will be the push to get that project started.