Particle to Delta Lake

This page provides you with instructions on how to extract data from Particle and load it into Delta Lake. (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 Particle?

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

What is Delta Lake?

Delta Lake is an open source storage layer that sits on top of existing data lake file storage, such AWS S3, Azure Data Lake Storage, or HDFS. It uses versioned Apache Parquet files to store data, and a transaction log to keep track of commits, to provide capabilities like ACID transactions, data versioning, and audit history.

Getting data out of Particle

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 your data warehouse. Set the URL to the key or token that you'll 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

Particle sends data in JSON format via webhook through a POST request whenever an event triggers it to do so. The JSON 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 Delta Lake on Databricks

To create a Delta table, you can use existing Apache Spark SQL code and change the format from parquet, csv, or json to delta. Once you have a Delta table, you can write data into it using Apache Spark's Structured Streaming API. The Delta Lake transaction log guarantees exactly-once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table. Databricks provides quickstart documentation that explains the whole process.

Keeping Particle data up to date

Once you've coded up a script or written a program to get the data you want and move it into your data warehouse, you're going to have to maintain it. If Particle modifies its API, or sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

Other data warehouse options

Delta Lake on Databricks 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, PostgreSQL, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3. 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, To Snowflake, To Panoply, and To S3.

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 Particle to Delta Lake automatically. With just a few clicks, Stitch starts extracting your Particle data, structuring it in a way that's optimized for analysis, and inserting that data into your Delta Lake data warehouse.