Indigo Ag works every day to help farmers harness nature to sustainably feed the planet. Through their Grain and Transport Marketplaces and Carbon program, Indigo facilitates grower profitability, promotes buyer control over their supply chain, and encourages environmental sustainability. With digital technologies and natural microbiology, Indigo partners with growers to reimagine the entire agricultural system, from soil to sale.
As part of Indigo’s data-driven approach to business, their analytics team gathers, assesses, and shares spatial data with internal teams to power decisions across the organization. Using CARTO’s automation solutions, Indigo created a replicable process to aggregate, analyze, and visualize disparate datasets in user-friendly maps for key stakeholders.
The Indigo analytics team works with a wide range of internal and external data sources, all stored within Snowflake databases. From Salesforce CRM data to field measurements, the Snowflake Unified Data Platform allows Indigo’s team to access, manipulate and visualize data from across the organization.
Last year we announced the release of a Snowflake connector making CARTO the first Location Intelligence platform to support the cloud-based data platform natively. This gives Snowflake users, such as Indigo Ag, the ability to import data from the cloud, keeping large datasets fully synchronized with a single, reliable source. More recently we also featured a guide on how to create maps from Snowflake using CARTO & SQL.
As a beta tester of the Snowflake connector, Indigo made immediate use of these new capabilities when designing a mapping dashboard to plan and track soil sampling during the Fall harvest season. Map layers displaying the current sampling status of each field, drive time areas from employee locations, and meteorological variables provided managers with information to assign and optimize sampling deployment. Local precipitation and field sampling status were updated daily using CARTO’s Snowflake connector, ensuring the dashboard displayed a current view of the sampling process.
Other layers, like days until first freeze and drive times, required additional processing in Python outside of Snowflake. Using a combination of CARTOframes, our Python package, and Cron, the Indigo analytics team automated complex data processing workflows and refreshed the data tables in CARTO through scheduled Python scripts.
CARTO’s Snowflake connector and Python package provide ultimate flexibility in creating customized, automated data processing and management workflows. Whether through the connector’s data syncing options or through scheduled CronJobs of scripts utilizing CARTOframes, the tools elevate dashboards from static snapshots in time to dynamic maps with up-to-date data reflecting the current state of the business.
The CARTOframes Python package and Snowflake connector have simplified the process of pulling in, refreshing, and keeping track of source data. Once the data are in CARTO, it is easy to draft different geospatial queries and quickly visualize the results.
Natalie Weiss, Associate Data Scientist at Indigo Ag.
Soil sampling cannot take place if the ground is too saturated with moisture. Indigo Ag uses a county choropleth layer of accumulated precipitation over the past week and in the last day, so FOS can reschedule field samples based on recent precipitation. These layers are updated daily using the Snowflake connector.
Want to learn more how Indigo Ag are rapidly creating prototypes?Read the full customer story
If you are a Snowflake user and want to easily connect your data to CARTO, sign up for a trial account to access the Snowflake connector.
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If you are an enterprise user of CARTO, your Customer Success Manager will be happy to guide you through this connection process, which can also be found here.
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