How to Pull Airtable Data into Snowflake and Automate Jira & Slack Alerts

Bringing together data from multiple platforms to create actionable insights for internal and external stakeholders is critical for data teams seeking to drive value for their organization. In a time where data teams are being asked to do “more with less,” being able to create data pipelines efficiently and at scale has become essential to staying competitive and making informed decisions quickly.

Rivery streamlines data workflows by uniting data ingestion, orchestration, and transformation in a single, user-friendly interface, enabling data engineers, architects, and analysts to quickly build complex ETL pipelines with flexible no-code and low-code options, as well as SQL and Python for transformations.

In this tutorial, we’ll guide you through setting up a custom data pipeline that extracts data from Airtable, loads it into Snowflake, and then sends alerts to Slack and create Jira  tickets (via a Reverse ETL process) —all within a single Rivery workflow.

If you prefer a video walkthrough, watch it below.

Step 1: Extracting Data from Airtable

The first step involves creating a data pipeline in Rivery to pull data from Airtable and load it into Snowflake. In Rivery, this process begins by creating a “Source to Target” River, which allows you to set up and test the connection between Airtable and your Snowflake database.

Connecting to Airtable: Start by selecting Airtable as your source and providing the necessary credentials. You can choose specific tables from Airtable, like “Tasks” and “Issues,” to be loaded into Snowflake.

Setting the Destination: Snowflake serves as our destination, where data from Airtable is loaded. Select your Snowflake database, schema, and target table. Rivery’s auto-mapping feature simplifies the process by mapping Airtable fields to Snowflake columns automatically.

Once you’ve set up the extraction process, you can schedule this pipeline to run at intervals or simply save it for now and incorporate it into a larger workflow.

Step 2: Sending Data to Slack and Jira with Action Rivers

With data now in Snowflake, the next step is to send alerts to Slack and create tasks in Jira. Rivery’s “Action River” functionality allows you to trigger alerts and send data to various applications, enabling real-time insights and updates for your team.

Setting up a Slack Alert: Select “REST Action” to create a Slack alert and define the connection. You can configure the request body with variables to customize the Slack message with relevant data from Snowflake.

Creating Jira Tasks: Set up another Action River to post data to Jira. Similar to Slack, define variables for the API request, ensuring that specific data fields like “Task Name” or “Due Date” are passed directly from Snowflake into Jira tasks.

These Action Rivers help automate notifications and task creation, keeping your team informed and workflows up-to-date.

Step 3: Bringing It All Together with a Logic River

To unify the data flows, Rivery offers the Logic River—a workflow orchestration tool that sequences all Rivers into a single, streamlined process. Here’s how to set up a Logic River:

Orchestrate the Workflow: Begin by adding your Airtable-to-Snowflake River to the Logic River. Then, follow up with any transformations required in Snowflake, such as setting variables or modifying data fields.

Set Up Action Loops: Use looping functions to iterate over data fields and execute Slack and Jira alerts as data flows in. This ensures real-time notifications and task assignments as new data is loaded.

Final Execution and Results

Once your Logic River is complete, simply hit “Run” to execute the entire pipeline. 

Rivery loads the data from Airtable to Snowflake, triggers transformations, and sends updates to Slack and Jira. You can verify the output by checking Slack for alert messages and Jira for new tasks. 

Snowflake’s final table will contain all your data in one organized place, ready for analysis.

Rivery streamlines the process of integrating data across platforms, from extraction to transformation and notification. With flexible no-code and low-code options, as well as support for SQL and Python, Rivery enables data teams to rapidly build and customize pipelines that fit their unique needs. Whether you’re managing marketing data or operational metrics, Rivery’s unified approach to data workflows makes it easier to leverage your data for impactful insights.

Have questions or want to learn more? Contact our team to see how Rivery can help elevate your data strategy.