This page provides you with instructions on how to extract data from Zapier and load it into Google BigQuery. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
What is Zapier?
Zapier lets non-programmers integrate multiple applications and services to automate repetitive tasks. It uses a graphical web interface – no coding involved.
What is Google BigQuery?
Google BigQuery is a data warehouse that delivers super-fast results from SQL queries, which it accomplishes using a powerful engine dubbed Dremel. With BigQuery, there's no spinning up (and down) clusters of machines as you work with your data. With that said, it's clear why some claim that BigQuery prioritizes querying over administration. It's super fast, and that's the reason why most folks use it.
Getting data out of Zapier
Zapier exposes data through webhooks. You can use Zapier webhooks to push data to a defined HTTP endpoint as events happen. Zapier supports form-encoded, XML, and JSON webhooks.
It's up to you to parse the objects you catch via your webhooks and decide how to load them into your data warehouse.
Loading data into Google BigQuery
Google Cloud Platform offers a helpful guide you can follow to begin loading data into BigQuery. Use the
bq command-line tool, and in particular the
bq load command, to upload files to your datasets. The syntax is documented in the Quickstart guide for bq. You can supply the table or partition schema, or, for supported data formats, you can use schema auto-detection. Iterate through this process as many times as it takes to load all of your tables into BigQuery.
Keeping Zapier data 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'll have to keep an eye out for any changes to Zapier’s webhooks implementation.
Other data warehouse options
BigQuery 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, PostgreSQL, or Snowflake, 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 Postgres, To Snowflake, 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 Zapier data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Google BigQuery data warehouse.