Automating your spatial workflows in Databricks with CARTO
In today’s cloud environments like Databricks, automating spatial workflows is essential for organizations working with geospatial data. As spatial datasets grow in complexity, businesses need efficient ways to process, analyze, and integrate them into their decision-making processes. However, traditional approaches to geospatial data processing can be cumbersome, requiring significant time and expertise.
CARTO bridges this gap by enabling users to design and automate geospatial workflows natively within Databricks. By leveraging Databricks' built-in capabilities, CARTO simplifies spatial data processing, making it easier to extract insights and integrate them into broader data pipelines.
In this post, we’ll explore how users can schedule and trigger their spatial data processes and analyses in Databricks Workflows using CARTO. We’ll cover how this integration allows for scheduling and seamless incorporation of workflows into existing pipelines in Databricks, helping organizations automate spatial analysis with minimal friction.
CARTO ensures that your data processes and analytical procedures take advantage of the distributed computing and Lakehouse architecture of your Databricks account. Since CARTO operates entirely within your lakehouse, data never leaves that platform, ensuring alignment with your security policies and reducing risks associated with data movement, such as duplication and synchronization issues.
Learn more about how you can use CARTO and Databricks to level-up your spatial analysis here.
Automating geospatial workflows is a key requirement for organizations that rely on spatial data analysis in order to make processes standardized, interoperable and cost-effective.
With CARTO Workflows, users can schedule workflows to run natively in Databricks. Processes can be scheduled to run automatically at predefined intervals, using daily, weekly, monthly, or custom-defined periods with cron syntax. This allows for integration with wider organizational processes and enables organizations to leverage real-time analytics.
When a schedule is created, CARTO Workflows automatically provisions a native Databricks workflow, leveraging Databricks' built-in scheduling capabilities. This ensures seamless integration into the Databricks ecosystem without the need for external schedulers or manual intervention.
By relying on Databricks’ native workflow engine, scheduled workflows benefit from:
- Transparent execution within Databricks, eliminating the need to move data or configure additional infrastructure.
- Consistent scheduling and monitoring, using Databricks’ workflow interface and logging mechanisms.
- Better integration with existing pipelines, allowing spatial workflows to fit naturally into broader enterprise data processes.
With this new capability, organizations can ensure their spatial analysis workflows run on a reliable and automated schedule, improving efficiency and consistency across their Databricks environment.
Geospatial workflows rarely operate in isolation—they are often part of broader data processing pipelines that combine multiple data sources and analytics processes. By integrating natively with Databricks Workflows, CARTO ensures that spatial analysis fits naturally into existing enterprise pipelines.
Since workflows which are scheduled in CARTO are then executed as native Databricks workflows, they can be linked to other Databricks tasks, such as data ingestion, transformation, machine learning models, or BI dashboards. This allows organizations to:
- Chain spatial workflows with other Databricks tasks to create end-to-end automated pipelines.
- Ensure consistency and scalability by running spatial analysis within Databricks, avoiding external dependencies.
- Leverage Databricks' orchestration capabilities, monitoring tools, and logging mechanisms for a unified pipeline management experience.
By eliminating the need for external scheduling or custom integrations, this approach allows teams to extend and optimize their spatial workflows without disrupting existing processes. Whether integrating geospatial insights into large-scale ETL pipelines or feeding results into visualization tools, CARTO Workflows provides a frictionless way to bring geospatial analysis into the Databricks ecosystem.
When working with geospatial data in the cloud, efficiency and scalability are critical. Running spatial analysis natively within Databricks ensures that workflows are optimized for performance, security, and smooth integration with existing data architectures. By leveraging Databricks’ built-in capabilities, CARTO Workflows provides a cloud-native approach to geospatial automation without requiring external tools or complex configurations.
If you're already using Databricks, get started today by integrating CARTO Workflows into your automated pipelines. Visit our documentation to learn more, or reach out to our team to see how CARTO can help you unlock the full potential of spatial analytics in the cloud.
