Particle to BigQuery

This page provides you with instructions on how to extract data from Particle 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.)

About Particle

Particle allows its users to bring their Internet of Things (IoT) products to market faster. They provide a secure, easy-to-use, full-stack IoT cloud platform and low-cost connected hardware.

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 all of 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 Particle

The first step for getting your Particle data into Redshift is collecting that data from Particle’s servers. Particle exposes events through webhooks. To use webhooks, log into your Particle console and click on the Integrations tab, then click New Integration > Webhook. Set the event name to the item you want to track; it’s good practice to specify the name of the field where you want the data to live in Redshift. Set the URL to the key or token that Redshift will use to accept the data. Leave the request type as POST. In the device field, select the device you want to trigger the webhook. Finally, click Create Webhook.

Sample Particle Data

Now Particle will send data via the webhook through a POST request whenever an event triggers it to do so. Data will be enclosed in the body of the request in JSON format. The fields and endpoints will match the data collected by your form. For instance:

{ "event": [event-name], "data": [event-data], "published_at": [timestamp], "coreid": [device-id] }

Loading data into Google BigQuery

Google Cloud Platform offers a helpful guide for loading data into BigQuery. You can use the bq command-line tool to upload the files to your awaiting datasets, adding the correct schema and data type information along the way. The bq load command is your friend here. You can find the syntax in the bq command-line tool quickstart guide. Iterate through this process as many times as it takes to load all of your tables into BigQuery.

Other data warehouse options

BigQuery is really great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Postgres or Redshift, which are two RDBMS that use similar SQL syntax. If you're interested in seeing the relevant steps for loading this data into Postgres or Redshift, check out To Redshift and To Postgres.

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 Particle data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Google BigQuery data warehouse.