Innovative, modern technology is what Snowflake is all about. This cloud data warehouse is one of the most flexible and fastest ways to manage your data in the cloud. So, it’s no surprise that Snowflake is taking cloud computing to the next level by taking the geospatial plunge. Users can now store and use geospatial data directly within Snowflake’s cloud data platform.
Why did they do this?
Well, quite frankly, it’s what people want. More and more Snowflake users are recognizing the power of geospatial data and are working to integrate geospatial datasets with regular business datasets to make informed decisions.
Between FME and Snowflake, your data integration challenges are one step closer (well, maybe more like five steps closer) to being solved. No more workarounds, no more random code snippets being added to your script — just straight forward integration.
What is Geospatial Data?
Let’s clear this up before going further. Geospatial data (also known as “spatial data”) is data that’s representative of a specific, geographic location on the Earth’s surface. Or, more simply, data with a location attribute attached to each data entry. Things like an address, a road network, or the outline of a city’s boundary are all considered geospatial data.
Why should you care? Geospatial data can help you make better decisions by leveraging the power of spatial relationships. Things like deciding where a new school is needed based on census data or determining which neighbourhoods your retail business would thrive in based on your target market.
What This Means for Snowflake Users
Snowflake users can now connect their spatial data to the cloud using the GEOGRAPHY data type. The supported spatial formats in Snowflake include:
- Well-Known Text(“WKT”)
- Well-Known Binary(“WKB”)
- IETF GeoJSON
Once your data is in Snowflake, you can perform over thirty geospatial functions on your data. This includes relationship and measurement functions like ST_DISTANCE and ST_INTERSECTS, as well as constructor functions like ST_MAKEPOINT and ST_MAKELINE.
Here are some ways geospatial data and Snowflake can now help their users:
- Analyze property risk based on weather patterns or flood zones to determine where is best to invest in property or determine insurance premiums
- Choose the best location for a new shop or restaurant based on market research and statistics about buyer demographics and behaviours
- Detect if fraudulent activity has taken place between ATMs or banks by analyzing withdrawal locations and the time it would take to travel between locations.
Where Does FME Fit Into This?
Snowflake’s GEOGRAPHY addition is one thing, but Snowflake + FME is a whole other story. As a Snowflake technology partner that provides flexible data integration and ETL options, FME is a part of the larger Snowflake ecosystem.
With FME, you can integrate spatial data from various sources without having to code. That’s right. No coding. Create custom workflows in an intuitive GUI that reads data from over 500 spatial data sources, like shapefiles, PostGIS datasets, and even point clouds, transform them, and then convert them into a Snowflake compatible format that can be added to your data warehouse.
FME expands your spatial data reach exponentially. With a greater ability to connect and transform your data, you’ll be able to solve data challenges more effectively and efficiently.
Spatial support for Snowflake is available in FME 2020.1 and up. Download FME 2020.1 (Beta) to try it yourself.
To learn more about how Snowflake and FME work together, check out Using Snowflake Spatial in FME for Geospatial Data
Amanda SchrackWhile Amanda's background education is in environmental science and GIS, she now writes content for safe.com. Looks like writing all those research reports paid off! In her spare time, Amanda likes to craft, watch true crime documentaries, and learn about bugs (the kind with six legs).