As you saw in last week's post, we have released MapInfo Pro v2023.1.
Data is growing by the minute, and Snowflake Data Cloud is leading the charge as the new data storage of choice.
To help users extract the most value from the spatial data all while reaping the benefits provided by the modern cloud, MapInfo Pro can now open DBMS Tables from Snowflake, push down queries to Snowflake, and visualize the result on a map.
In this article, we will dive into some of the capabilities you can benefit from when connecting MapInfo Pro to Snowflake.
Getting Access to MapInfo Pro v2023.1
If you want to try out or upgrade your existing installation to the new release of MapInfo Pro, you can find the installers on the MapInfo Pro v2023.1 Download page.
You can also find a link to the Release Notes on the website.
Support for Snowflake
MapInfo Pro now supports Snowflake similarly to the way we support other spatial databases.
You connect to Snowflake using the Snowflake ODBC driver and can open tables and views from the Snowflake database into MapInfo Pro.
You can open tables as linked or live like you can with other databases.
You can also edit the data and push the changes back up into Snowflake. As with other databases, MapInfo Pro requires a unique numerical key column to be able to edit the data.
In the article last week, we gave you an overview of the improvement to MapInfo Pro. This also covered the improvement to the way you access tables from remote database systems. These improvements will also be available if you connect to Snowflake.
MapInfo Pro no longer requires the MapInfo Map Catalog to be able to open and map spatial data. The Map Catalog is now optional. This also goes for Snowflake.
If you want to see the power of using MapInfo Pro in combination with Snowflake, check out this personalized demonstration: Using Snowflake from MapInfo Pro.
Building Spatial Join Queries using Snowflake
We have introduced several new ways to create queries and pass these to the database for execution.
The DBMS Select by Location dialog helps you build a query spatially joining two tables. We support several spatial relations such as Within, Partly Within, Entirely Within, Not within, and Within a Distance to name a few.
The dialog also allows you to add conditions on the two tables you are joining. In the example below, this condition could limit the business to only restaurants.
The resulting query would only return all restaurants within a 500-meter distance of the highway instead of returning all businesses within this distance.
Currently, this is only supported on Snowflake.
Building Aggregating Queries using Snowflake
Another dialog that helps you build queries to be executed server-side is the Build DBMS Aggregating Query dialog.
This is useful when your DBMS table holds many records, be that thousands or millions of records. Often you want to get an aggregated view of these data records instead of seeing the individual data records.
This dialog lets you build an aggregating query and pass it to Snowflake for execution.
We allow you to aggregate your data via 3 methods: Binning, Column, or Location.
Binning lets you aggregate the data using a grid reference system. We currently support Uber H3 (hexagons) and Geohash (rectangles).
This method also introduces a potential dynamic aggregation. We will use a level from the grid reference system that matches the zoom in your map to give you a decent number of polygons. As you zoom in and out, the level will change resulting in a similar number of polygons but at varying sizes.
Below you can see an example of how the sizes of the H3 hexagons change as you zoom out.
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Peter Horsbøll Møller
Principal Presales Consultant | Distinguished Engineer
Precisely | Trust in Data
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