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 implemented as a managed service. It runs on the Amazon Web Services architecture using EC2 and S3 instances. Snowflake is designed to be fast, flexible, and easy to work with. For instance, for query processing, Snowflake creates virtual warehouses that run on separate compute clusters, so querying one virtual warehouse doesn't slow down the others.
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 the structure of your data, you may need to prepare it for loading. Look at the supported data types for Snowflake and make sure that the data you've got will map neatly to them.
Note that you don't need to define a schema in advance when loading JSON data into Snowflake.
Loading data into Snowflake
The Snowflake documentation's Data Loading Overview section can help you with the task of loading your data. If you're not loading a lot of data, you might be able to use the data loading wizard in the Snowflake web UI, but chances are the limitations on that tool will make it a non-starter as a reliable ETL solution. Alternatively, there are two main steps for getting data into Snowflake:
- Use the PUT command to stage files.
- Use the COPY INTO table command to load prepared data into an awaiting table.
You’ll have the option of copying from your local drive or from Amazon S3. One of Snowflake's slick features 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.