This page provides you with instructions on how to extract data from Webhooks and analyze it in Metabase. (If the mechanics of extracting data from Webhooks seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
What are webhooks?
A webhook is a way for one application to provide other applications with real-time information. Webhooks send data through user-defined HTTP POST callbacks, which means an application that uses webhooks can POST data when an event occurs to a specified endpoint (web address).
What is Metabase?
Metabase provides a visual query builder that lets users generate simple charts and dashboards, and supports SQL for gathering data for more complex business intelligence visualizations. It runs as a JAR file, and its developers make it available in a Docker container and on Heroku and AWS. Metabase is free of cost and open source, licensed under the AGPL.
Getting data out of webhooks
Different applications have different ways to set up webhooks. Often, you can use a management console to define the webhook and specify the endpoint to which you want data delivered. You must make sure that the specified endpoint exists on your server.
What does webhook data look like?
Webhooks post data to your specified endpoints in JSON format. It's up to you to parse the JSON objects and decide how to load them into your data warehouse.
Loading data into Metabase
Metabase works with data in databases; you can't use it as a front end for a SaaS application without replicating the data to a data warehouse first. Out of the box Metabase supports 15 database sources, and you can download 10 additional third-party database drivers, or write your own. Once you specify the source, you must specify a host name and port, database name, and username and password to get access to the data.
Using data in Metabase
Metabase supports three kinds of queries: simple, custom, and SQL. Users create simple queries entirely through a visual drag-and-drop interface. Custom queries use a notebook-style editor that lets users select, filter, summarize, and otherwise customize the presentation of the data. The SQL editor lets users type or paste in SQL queries.
Keeping data from webhooks up to date
Once you've set up the webhooks you want and have begun collecting data, you can relax – as long as everything continues to work correctly. You have to keep an eye on any changes your applications make to the data they deliver. You should also watch out for cases where your script doesn't recognize a new data type. And since you'll be responsible for maintaining your script, every time your users want slightly different information, you'll have to modify the script.
From Webhooks to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Webhooks data in Metabase is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Webhooks to Redshift, Webhooks to BigQuery, Webhooks to Azure SQL Data Warehouse, Webhooks to PostgreSQL, Webhooks to Panoply, and Webhooks to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Webhooks with Metabase. With just a few clicks, Stitch starts extracting your Webhooks data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Metabase.