Onfleet to Google Data Studio

This page provides you with instructions on how to extract data from Onfleet and analyze it in Google Data Studio. (If the mechanics of extracting data from Onfleet seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Onfleet?

Onfleet is logistics management software and route optimization for businesses offering last-mile delivery. Onfleet helps businesses assign and track local deliveries and communicate with drivers and customers. It includes smartphone apps for drivers to receive tasks, a web dashboard for dispatchers to manage and analyze operations, and automatic notifications and tracking for recipients.

Getting data out of Onfleet

Onfleet provides a RESTful API that lets developers retrieve data stored in the platform about workers, destinations, recipients, and more. For example, to get information a list of all teams, you would call GET https://onfleet.com/api/v2/teams.

Sample Onfleet data

Here's an example of the kind of response you might see with a query like the one above.

    "id": "yKpCnWprM1Rvp3NGGlVa5TMa",
    "timeCreated": 1455048584000,
    "timeLastModified": 1455049756016,
    "name": "FiDi",
    "workers": [
    "managers": [
    "hub": null,
    "tasks": [
  // ...
    "id": "R4P7jhuzaIZ4cHHZE1ghmTtB",
    "timeCreated": 1455048567000,
    "timeLastModified": 1455073711646,
    "name": "Tenderloin",
    "workers": [
    "managers": [
    "hub": "E4s6bwGpOZp6pSU3Hz*2ngFA",
    "tasks": [

Preparing Onfleet data

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Onfleet's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Keeping Onfleet data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Onfleet's API results include fields like timeCreated and timeLastModified that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've take new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

From Onfleet to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Onfleet data in Google Data Studio is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Onfleet to Redshift, Onfleet to BigQuery, Onfleet to Azure SQL Data Warehouse, Onfleet to PostgreSQL, Onfleet to Panoply, and Onfleet to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Onfleet with Google Data Studio. With just a few clicks, Stitch starts extracting your Onfleet data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Google Data Studio.