In this talk presented in the Americas livestream at Citus Con: An Event for Postgres, Nirav Savani and Mahipal Ramidi share their customer experience managing a geospatial application using PostGIS, PostgreSQL, and Citus on Azure. With their geospatial application, some of the biggest global insurance firms translate vast amounts of data into spatial insights. The workload has 3000 global users, with 10-50 of them concurrently performing complex geospatial analysis, crunching millions of rows across 1000s of spatial polygons. Results are expected in real-time. The dataset is 25 TB.
In this talk, you’ll learn about their challenges scaling their spatial capabilities as their data volume & number of users (including concurrency) grew. Nirav and Mahipal share their learnings from building a large-scale geospatial workload on Postgres. Their goal was to identify a database that scales, which made them evaluate and land on the PostGIS and Citus extensions to PostgreSQL.
Come learn how PostGIS and Citus meet their application’s requirements: how the spatial indexing of PostGIS combines with the ability to scale out (shard) data across multiple database nodes with Citus, without affecting performance.
Nirav Savani works as a Senior Engineering Manager with 19 years of software Development experience. He is passionate about architecting high-performing scalable software applications to solve complex business problems. Having worked on Java applications primarily, he leads a large team involved in modernizing a distributed geospatial analytics platform to a cloud native architecture using microservices, Microsoft Azure, Postgres, Citus, and Databricks.
Mahipal Ramidi works as a data engineer with 14 years of experience in software development. His primary skills are building data-centric applications in Oracle, SQL Server, and PostgreSQL, along with business intelligence applications. In his role at Guy Carpenter, he supports real-time analytics applications using geospatial computations in the PostGIS extension to Postgres. And he is deeply involved in developing high-throughput data pipelines and ETL applications in Azure Cloud, using Azure Data Factory and Azure Databricks.
► Video bookmarks:
⏩ 00:00 Introduction
⏩ 01:52 Overview of GC Advantage Point
⏩ 03:16 How users analyze accumulations for their portfolios
⏩ 09:12 GC Advantage Point by the numbers
⏩ 12:15 Azure Database for Postgres - Hyperscale (Citus)
⏩ 17:58 Shard count & useful server parameters for real time analytics apps
⏩ 19:46 Demo of GC Advantage Point
⏩ 28:52 Q&A with Nirav, Mahipal, & Marco
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