Business Intelligence


In recent years the field of Business Intelligence (BI) has taken a dramatic turn from IT-managed offerings to self-service solutions that allow end users to load their own business intelligence data and build their own dashboards. An important part of this “democratization of BI” has been a vendor trend toward allowing interactive data exploration by allowing users to dynamically create aggregates and apply filters, resulting in unique and comprehensive visualizations of business intelligence. Unfortunately this shift toward ad-hoc business analytics exploration taxes even the fastest database backends, which cannot keep up with the deluge of data coming into organizations from increasingly diverse sources.

The traditional solution to the “speed gap” was to employ cubing or indexing strategies in the hope that the majority of users’ queries could be precomputed on the BI backend. Unfortunately this strategy quickly breaks down in the face of the infinite customization that modern BI analysis systems allow - every user on the system is likely building their own dashboards with their own custom aggregates and filters, rendering pre-computation mostly ineffective.

To overcome these limitations vendors pursued a second route - throwing more servers at the problem. In recent years it has become common to see BI analysis tools propped up by backends consisting of tens or even hundreds of servers running the fastest analytics databases. However the immense costs and complexity of implementing such systems, not to mention the diminishing gains of scaling large distributed systems, has its own substantial downsides.

MapD BI tools take a very different approach. By leveraging the parallel power of GPU supercomputing to execute queries up to orders of magnitude faster than CPU solutions, MapD provides an immensely scalable backend that can enable fully interactive exploration of multi-billion record datasets by multiple simultaneous users. Since the GPUs have so much power, users don’t have to index or pre-cube their data on insert, meaning inserts and queries are predictably fast, eliminating the need for costly DBA tuning.

While the output of the MapD database can be consumed via traditional tools like Tableau or Qlikview, the system shines when paired with the MapD Immerse visual analytics app. The advantage of using the latter is that it was built from the ground up to operate hand-in-glove with the MapD database to create a lightning fast, immersive data exploration experience. Not only was MapD Immerse designed to harness the immense speed of the MapD database but it is the only platform to be able to leverage the backend’s unique capability to render large result sets directly on the server-side GPUs. So whether users need to render billions of points on a map or scatterplot, not only can Immerse use the backend to query the data in milliseconds, but it can also use it to render that data in milliseconds as well, no matter how complex or vast your business intelligence data may be.

Architecture Diagram

The speed of the backend combined with the rich, interactive visualizations produced by MapD Immerse distinguishes MapD in a crowded field and has resulted in a number of accolades and awards. Schedule a demo and see for yourself how speed can transform the data exploration process.