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Kinetica vs. PostGIS

Scale Past PostGIS With Kinetica


PostGIS is an extension that provides geospatial data types, functions, and queries for PostgreSQL, arguably the most versatile and functional open source databases available. First released in 2001, PostGIS has provided a great way for users to build custom GIS applications, without needing to pay for licenses from GIS vendors such as Esri.


PostGIS has become very popular and is an industry standard among GIS developers. Its open source license is well understood and trusted, and it has a strong community of users, so finding expertise to develop or administer it is quite easy.

PostGIS provides a very robust geospatial function library of both 2D, 3D, and raster functions. It has full support for many spatial reference systems (SRS), and plugs into popular 3rd party tools to provide missing functionality, such as for visualizations or routing algorithms. It’s SQL compliant and also allows users to bring their UDFs.

PostGIS makes it easy for organizations to build geospatial applications at a moderate scale. It also is a great choice when the application requires 3D or raster data.

As the underlying database, PostgreSQL is great for OLTP and moderate scale transactional applications. All of the major cloud providers offer PostgreSQL as-a-Service with a high degree of automation. This has made PostgreSQL even easier to use and administer, and fast to provision.



Users often run into limitations with PostGIS when working with geospatial data at a large scale. PostGIS is not set up with a memory-first, scale-out architecture, and it does not support vectorized processing that taps into GPUs and vectorized CPUs, so its performance is capped when data volumes grow very large.

As a result, PostGIS also cannot provide a level of visual interactivity with large and detailed geospatial datasets. Its queries take too long to return results, and without native support for visualization, it may face additional scale limitations from the 3rd party visualization tool it is plugged into. Many popular visualization vendors leverage client-side rendering, which limits visualizations to small and manageable datasets.

PostgreSQL is not designed for high velocity data ingestion, with real time aggregations and queries, so PostGIS will only allow users to gain insights from their geospatial data after the fact.

Kinetica for GIS

Kinetica is the choice for users who are running up against the scale limitations of PostGIS. Kinetica is a horizontally scaling distributed system with a memory first architecture, built natively for vectorization to take full advantage of GPUs and vectorized CPUs. This allows Kinetica to scale to handle far larger geospatial data sets, and provide much faster analytics on equivalently sized systems to PostGIS.

Kinetica leverages server-side rendering to enable interactivity with massive, detailed geospatial datasets. This grants users an interactive data exploration experience at a virtually unlimited scale, all natively within Kinetica.

Kinetica is also the pick for users who require support for streaming, graph, or machine learning capabilities in their use case. Kinetica provides simultaneous streaming data ingest and analysis, all in the context of historical and integrated data. Additionally, Kinetica incorporates robust graph analytics capabilities, as well as the ability to obtain machine learning inferences against geospatial data.



One of the largest telcos in the U.S. wanted to leverage its business and network data to make the best possible network improvement and site buildout decisions. It needed to blend mobile network performance data with 90B phone signal events, all overlaid on road network data, to understand its performance in key geographic areas, and determine how to prioritize infrastructure improvements.

The massive scale geospatial operation required, joining billions with billions of rows, would have required 6 years to complete on PostGIS -- a nonstarter. Kinetica was able to return the workload in 50 minutes. This is a prime example of the type of complex, large scale geospatial analysis that is a better fit for Kinetica.


PostGIS has a strong community around it and provides a great way to build GIS applications at a moderate scale. It has a very robust geospatial function library of both 2D, 3D, and raster functions. However, Kinetica is better designed for performance at large scale and interactive visualizations. Advanced analytics fit better on Kinetica with its support for streaming, graph, and machine learning capabilities.


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