Tagged: Data Pipelines

Web-Powered Workflows: Fetching and Running Digdag Workflows with Callbacks

Web-Powered Workflows: Fetching and Running Digdag Workflows with Callbacks

In Digdag, workflows are typically defined in YAML files with a “.dig” extension. Developers usually write these workflows, which consist of tasks to be executed. However, tasks can also be added dynamically using the Digdag Python API or by downloading a “.dig” file from a remote HTTP server and incorporating it as a subtask. This approach is useful when a web service or app generates customized workflow files based on web app conditions, allowing the workflow logic to be managed externally. You can add webhooks to make it reactive.

My Boring Yet Modern Data Stack

We have a data stack that we have been using for years now. We have used it with medium to large customers, and they have worked very well. The goal has always been simple, stable, composable tools that can be used on the developer’s machine and scaled to work with massive data on production. You can self-host them, host them on the cloud, or get managed services based on your need.

Very similar to my web stack. It’s called “Boring” not because it’s dull but because there are minimal unwanted surprises. So my current stack for data looks like this. This stack is both “Modern” and “Boring.”