A Better Approach To Complex, Modern Data
At the heart of SlamData technology is Multi-dimensional Relational Algebra (MRA) pronounced “Murray”. MRA is a revolutionary and modern approach to data that reflects the changes in the complexity and variety of modern data.
MRA is a unique generalization of Relational Algebra that allows it to work elegantly on data in multiple dimensions. It also extends the underlying mathematics to support highly heterogeneous data without any requirement for a fixed schema or data types.
By contrast, Relational Algebra, which was originally written in 1972, requires data to be completely flat, uniform, and have a strong fixed schema throughout in order to perform analytics. This approach is fundamentally in conflict with modern data models like JSON, XML, log files or CSV which are highly heterogeneous and have varying degrees of dynamic schema and multiple levels of data (nested data).
Modern SaaS and IoT applications, as well as most web APIs, deliver JSON data payloads with a high degree of heterogeneity. This type of data can be very hard to work with for end users and non-data engineers. And this type of data will not work with any popular BI or Data Science tools without significant transformation and preparation. MRA solves this issue.
MRA is not a radical departure from tried and true Relational Algebra, but is an elegant extension that retains the best of RA and adds in new support for modern data models. The result of all this is a way to work directly with complex modern data without the need for cumbersome transformation and data integration models. MRA supports an enhanced SQL dialect with added operations for working directly with nested data, dynamic schema and heterogenous data.
SlamData’s visual interface generates SQL² queries behind the scenes as users prepare their data to make it analytics or data science ready. SQL² is an enhanced SQL dialect that falls naturally out of the MRA formalism. It is ANSI SQL compliant with regard to homogenous data, but adds a discrete number of additional operators (4) to handle heterogeneous data. Anyone fluent in SQL can learn SQL² in a few hours or less.
Surprisingly, virtually all modern data and analytics platforms still rely exclusively on Relational Algebra at their core. Even seemingly modern platforms, like Spark, ultimately expect the data to eventually reach a flat homogeneous form for consumption.
Simply put, MRA lets users work with complex data as it is versus some watered down version. It is a novel and much needed update of the core algebra that has powered database analytics for well over 40 years.