Density to Metabase

This page provides you with instructions on how to extract data from Density and analyze it in Metabase. (If the mechanics of extracting data from Density 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 Density?

Density provides intelligent sensors and software for anonymously counting people as part of an occupancy and space utilization analytics platform. Its sensors measure how busy a location is in real time to help organizations make better use of their space.

What is Metabase?

Metabase provides a visual query builder that lets users generate simple charts and dashboards, and supports SQL for gathering data for more complex business intelligence visualizations. It runs as a JAR file, and its developers make it available in a Docker container and on Heroku and AWS. Metabase is free of cost and open source, licensed under the AGPL.

Getting data out of Density

Density provides a REST API that lets developers retrieve data stored in the platform about spaces, counts, sensor events, and more. For example, to retrieve information about a doorway, you would call GET /doorways/:doorway_id/.

Sample Density data

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

  "id": "drw_437303389773103967",
  "sensor_serial_number": "Z458467949888405826",
  "name": "Board Room Door",
  "description": "The doorway to the Board Room",
  "spaces": [
      "id": "spc_439805443284402880",
      "name": "Conference Room",
      "sensor_placement": 1
  "tags": [
  "created_at": "2019-05-01T11:45:13.356Z",
  "updated_at": "2019-05-01T11:45:13.356Z"

Preparing Density 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. Density'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.

Loading data into Metabase

Metabase works with data in databases; you can't use it as a front end for a SaaS application without replicating the data to a data warehouse first. Out of the box Metabase supports 15 database sources, and you can download 10 additional third-party database drivers, or write your own. Once you specify the source, you must specify a host name and port, database name, and username and password to get access to the data.

Using data in Metabase

Metabase supports three kinds of queries: simple, custom, and SQL. Users create simple queries entirely through a visual drag-and-drop interface. Custom queries use a notebook-style editor that lets users select, filter, summarize, and otherwise customize the presentation of the data. The SQL editor lets users type or paste in SQL queries.

Keeping Density 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, Density's API results include fields like created_at 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 Density to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Density data in Metabase 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 Density to Redshift, Density to BigQuery, Density to Azure Synapse Analytics, Density to PostgreSQL, Density to Panoply, and Density 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 Density with Metabase. With just a few clicks, Stitch starts extracting your Density 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 Metabase.