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.

Panoply provides end-to-end data management-as-a-service. It uses machine learning and natural language processing (NLP) to learn, model, and automate standard data management activities from source to analysis. It can import data with no schema, no modeling, and no configuration. Users can quickly spin up an Amazon Redshift instance and run analysis, SQL, and visualization tools just as they would on a Redshift data warehouse they created on their own.

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.

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]
}

Once you've identified all the columns you want to insert, you can use Redshift's CREATE TABLE statement to create a table to receive all of the data.

Once you have a table built, you might think that the easiest way to migrate your data (especially if there isn't much of it) would be to build INSERT statements to add data to your Redshift table row by row. Don't do it! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, we suggest loading the data into Amazon S3 and then using the COPY command to load it into Redshift.

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.

Panoply 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, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax. 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 Postgres, To Snowflake, To Azure SQL Data Warehouse, To S3, and To Delta Lake.