Stitch is a powerful ETL (Extract, Transform, Load) platform that enables seamless data integration and consolidation from various sources. By leveraging the Stitch integration, Statsig users can effortlessly push events and data to their Stitch accounts, unlocking a wide range of possibilities for data analysis and insights.

The Stitch integration empowers Statsig users to forward their valuable data to any connectors available within the Stitch ecosystem. This means that you can easily route your Statsig events to popular destinations such as data warehouses, analytics platforms, and more. With just a few simple steps, you can establish a robust data pipeline that ensures your Statsig data flows seamlessly into the tools and systems you rely on for decision-making.

The importance of the Stitch integration lies in its ability to streamline data consolidation and analysis. By centralizing your Statsig data alongside information from other sources, you gain a holistic view of your application's performance and user behavior. This comprehensive perspective allows you to uncover valuable insights, identify trends, and make data-driven decisions to optimize your product and drive growth.

Moreover, the Stitch integration eliminates the need for manual data export and import processes, saving you time and effort. With automated data synchronization, you can trust that your Statsig data is always up to date and readily available for analysis. This efficiency enables you to focus on extracting meaningful insights rather than worrying about data management tasks.

Configuring outbound events for Stitch

To set up outbound events for Stitch, first create a new webhook URL by following the steps in the Stitch Webhook Setup Guide. Once you have the URL, go to the Statsig Integrations page and enable the Stitch integration by pasting the URL and clicking Confirm.

Statsig sends events to Stitch in batches using a JSON format. The structure includes fields like event, user, userId, stableId, timestamp, value, and metadata. Custom event formatting varies slightly for different types of events, such as logEvent, checkGate, getConfig, and getExperiment.

Here are examples of the JSON structures for each event type:

• logEvent: Includes the custom event name, user details, a custom value, and metadata.

• checkGate: Contains information about the feature gate, its value, rule ID, reason, and timestamp.

• getConfig: Provides details about the dynamic config, rule ID, reason, and timestamp.

• getExperiment: Includes experiment details, rule ID, reason, timestamp, and the assigned experiment group.

By configuring outbound events for Stitch, you can seamlessly forward your Statsig data to any connectors available through Stitch. This integration allows you to leverage Stitch's powerful data pipeline to analyze and visualize your Statsig events alongside other data sources.

Event filtering and best practices

Statsig allows you to select specific categories of events for export to Stitch. This helps optimize data transfer and ensures you're only sending the most relevant information.

The Event Filtering button in the Stitch integration settings provides a simple way to customize your data flow. By checking or unchecking boxes, you can include or exclude certain event types from being sent to Stitch.

To ensure efficient data transfer and analysis, consider the following best practices:

• Only export events that are necessary for your specific use case or analysis

• Regularly review and update your event filtering settings as your needs evolve

• Leverage Stitch's data transformation capabilities to further refine and optimize your data

For more information on integrating Statsig with Stitch, refer to the following resources:

• Statsig Documentation: Stitch Integration

• Stitch Documentation: Webhook Integration

By carefully selecting the events you export and following best practices, you can ensure a smooth and effective integration between Statsig and Stitch. This will help you unlock valuable insights from your data while maintaining efficiency in your data pipeline.