Enterprise Spatial Analytics

Use Case Overview:
In this blog series, we explore how to combine Esri and Snowflake for real-time spatial decision-making. In Part 1, we introduced Query Layers. In Part 2, we automated wildfire perimeter detection with deep learning. Now, in Part 3, we will demonstrate how to update Snowflake cloud-native tables directly from ArcGIS using the Data Interoperability Extension.

This enables a two-way integration, allowing you to perform advanced spatial analysis in ArcGIS and write the results back into Snowflake to enrich your enterprise data stores.

The Compress API is your Hosted Feature Service’s version of “spring cleaning.” If your layer gets frequent updates or deletes, little orphaned records start piling up behind the scenes. Think of them like digital dust bunnies, you don’t see them at first, but they’ll eventually slow everything down. The compress process sweeps them away, freeing up storage space and boosting query performance.

Use Case Overview: This blog series demonstrates how Esri and Snowflake empower real-time decision-making for disaster resilience. By integrating telco data (customer locations, transactions, and cell tower positions) with wildfire risk intelligence from ArcGIS, we enable emergency responders, telecom operators, and local governments to identify at-risk infrastructure and populations. Our goal is to showcase scalable workflows powered by ArcGIS Pro Query Layers, Snowflake's cloud-native architecture, and GeoAI tools for imagery analysis.

In this first installment of our blog series on Enterprise Spatial Analytics, we dive into one of the most potent yet underused features in ArcGIS Pro: Query Layers.