A Webhook Love Story

🌈 Abstract

The article discusses the author's journey from initially disliking webhooks as a data engineer to developing a deep appreciation for them in the world of marketing. It covers the basics of webhooks, the challenges with implementing them, and how to scale them effectively. The article also highlights the author's experience using Pipedream, a workflow automation platform, to manage their lead flow process at Prefect.

🙋 Q&A

[01] A Webhook Love Story

1. What was the author's initial response to webhooks as a data engineer? The author initially hated webhooks and found them extremely hard to manage during their time as a data engineer. They were concerned about the lack of ability to run data quality checks, understand errors, retry failures, or encode dependencies.

2. How did the author's perception of webhooks change over time? The author's initial repulsive response to webhooks grew into a deep appreciation for event-driven automation in the world of marketing. The author's relationship with webhooks evolved from an initial forced relationship to one they would never want to live without.

3. What are the key components of a webhook? The key components of a webhook are: a programmatic event (usually in the form of a REST API call), an API endpoint, and an API spec that the endpoint agrees to accept.

4. When are webhooks most useful? Webhooks are most useful when you need an automated process to run based on something in the outside world, and the event from the outside world is unpredictably timed, but you need to respond to it ASAP. Examples include B2C fraud detection workflows, B2B marketing enrichment processes, and D2C social network notifications.

[02] The Challenge with Webhooks

1. What are the main challenges with implementing webhooks? The main challenges with implementing webhooks are:

• Alert fatigue due to lack of information in the alerts
• Triage being a mess because requests aren't centralized, and errors fall into a triaging black hole
• Broken workflows when errors occur and aren't resolved

2. How do batch data pipelines differ from event-driven workflows in terms of observability and debugging? Batch data pipelines are orchestrated centrally, making debugging easier as you can start in one dashboard to see which workflow failed and why. Event-driven workflows are more complex, as an API spec change may not be reflected in the events sent to the endpoint, making it difficult to rerun a failed response.

[03] Why We Need Webhooks and How to Scale Them

1. What are the key parameters for choosing a webhook implementation? The key parameters are:

• Quick response requirements (minutes or seconds)
• Large historical data that can be processed incrementally for individual events
• Events that travel across teams and systems, requiring integrations with third-party APIs

2. How does the author recommend scaling webhooks effectively? The author recommends:

• Implementing observability through alerts on failure response codes, latency issues, and webhook infrastructure overload
• Maintaining in-depth logs and documentation for debugging
• Finding a centralized hub to deploy webhooks consistently, consolidating alerts and logs

3. Why does the author prefer Pipedream over Zapier for managing webhooks? The author prefers Pipedream because it provides features like fast workflow building, a single view for failed runs with accessible logs, control over setup parameters, the ability to ping custom API endpoints, and versatile use cases. The author found Zapier to be lacking in areas like retries, live debugging, and customization.

4. How does the author use Pipedream to manage Prefect's lead flow process? The author uses Pipedream to handle lead enrichment, Salesforce integration, and routing leads to automated Outreach campaigns. Pipedream's features, such as out-of-the-box integrations, quick development time, custom task handling, and easy error handling, make it a better fit for Prefect's needs compared to other automation tools.