Webhooks to Snowflake

This page provides you with instructions on how to extract data from Webhooks and load it into Snowflake. (If this manual process sounds onerous, 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 Snowflake?

Snowflake is a cloud-based data warehouse that runs on Amazon Web Services EC2 and S3 instances. Snowflake can natively load and optimize both structured and semi-structured data and make it available via SQL. As a managed service, it's easy to work with, and its columnar database engine, running on the scalable AWS platform, makes it fast.

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.

Preparing data for Snowflake

Depending on your data structures, you may need to prepare your data before loading. Check the supported data types for Snowflake and make sure that your data maps neatly to them.

Note that you won't need to define a schema in advance when loading JSON or XML data into Snowflake.

Loading data into Snowflake

Snowflake's documentation outlines a Data Loading Overview that can help you with the task of loading your data. If you're not loading a lot of data, look into the data loading wizard in the Snowflake web UI, but for many organizations, the limitations on that tool will make it a non-starter as a reliable ETL solution. Instead:

  • Use the PUT command to stage files.
  • Use the COPY INTO table command to load prepared data into an awaiting table.

You can copy from your local drive or from Amazon S3. Snowflake lets you make a virtual warehouse that can power the insertion process.

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.

Other data warehouse options

Snowflake is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, or PostgreSQL, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, and To Panoply.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Webhooks data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Snowflake data warehouse.