This page provides you with instructions on how to extract data from Webhooks and load it into PostgreSQL. (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 PostgreSQL?
PostgreSQL, a.k.a. Postgres, touts itself as "the world's most advanced open source database." The popular open source object-relational database management system (ORDBMS) offers enterprise-grade features with a strong emphasis on extensibility and standards compliance.
PostgreSQL runs on all major operating systems, including Linux, Unix, and Windows. It's fully ACID-compliant and supports a roster of features that includes foreign keys, joins, views, triggers, and stored procedures in multiple languages. PostgreSQL serves as the back-end database for many web systems and software tools, and is available in cloud-based deployments from most major cloud vendors. PostgreSQL's syntax forms the basis for querying Amazon Redshift, which makes migration between the two systems relatively painless, and makes Postgres a good stepping-stone for developers who may later use Redshift's data warehouse platform.
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 Postgres
Once you have identified all of the columns you will want to insert, you can use the
CREATE TABLE statement in Postgres to create a table that can receive all of this data. Then, Postgres offers a number of methods for loading in data, and the best method varies depending on the quantity of data you have and the regularity with which you plan to load it.
For simple, day-to-day data insertion, running
INSERT queries against the database directly are the standard SQL method for getting data added. Documentation on INSERT queries and their bretheren can be found in the Postgres documentation here.
For bulk insertions of data, which you will likely want to conduct if you have a high volume of data to load, other tools exist as well. This is where the
COPY command becomes quite useful, as it allows you to load large sets of data into Postgres without needing to run a series of INSERT statements. Documentation can be found here.
The Postgres documentation also provides a helpful overall guide for conducting fast data inserts, populating your database, and avoiding common pitfalls in the process. You can find it here.
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
PostgreSQL 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, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.
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 move data from Webhooks to PostgreSQL automatically. 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 your PostgreSQL data warehouse.