Contentful to Google Data Studio

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

Contentful's content infrastructure system lets organizations create, manage, and distribute content to any platform. It's API-centric, and therefore more developer-friendly than most CMSes.

What is Google Data Studio?

Google Data Studio is a simple dashboard and reporting tool. It's free and easy to use, but it lacks the sophisticated features of higher-end reporting software. Many of the connectors it supports are for Google products, but third parties have written partner connectors to a wide variety of data sources. Its drag-and-drop report editor lets users create about 15 types of charts.

Getting data out of Contentful

You can extract information about several kinds of operations, including create, publish, and archive, through webhooks, which you can set up through the web app.

Contentful also offers four REST APIs for accessing and manipulating content.

Sample Contentful data

Contentful returns data in JSON format. Here’s an example of the data returned for a content type snapshot:

{
  "snapshot": {
    "name": "Blog Post",
    "fields": [
      {
        "id": "title",
        "name": "Title",
        "required": true,
        "localized": true,
        "type": "Text"
      },
      {
        "id": "body",
        "name": "Body",
        "required": true,
        "localized": true,
        "type": "Text"
      }
    ],
    "sys": {
      "firstPublishedAt": "2017-11-15T13:38:11.311Z",
      "publishedCounter": 2,
      "publishedAt": "2017-11-15T13:38:11.311Z",
      "publishedBy": {
        "sys": {
          "type": "Link",
          "linkType": "User",
          "id": "4FLrUHftHW3v2BLi9fzfjU"
        }
      },
      "publishedVersion": 9
    }
  },
  "sys": {
    "space": {
      "sys": {
        "type": "Link",
        "linkType": "Space",
        "id": "yadj1kx9rmg0"
      }
    },
    "type": "Snapshot",
    "id": "cat",
    "createdBy": {
      "sys": {
        "type": "Link",
        "linkType": "User",
        "id": "4FLrUHftHW3v2BLi9fzfjU"
      }
    },
    "createdAt": "2017-11-18T11:29:46.809Z",
    "snapshotType": "publish",
    "snapshotEntityType": "ContentType"
  }
}

Preparing Contentful 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. Contentful'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. This means you’ll likely have to create additional tables to capture the unpredictable cardinality in each record.

Loading data into Google Data Studio

Google Data Studio uses what it calls "connectors" to gain access to data. Data Studio comes bundled with 17 connectors, mostly to pull in data from other Google products. It also supports connectors to MySQL and PostgreSQL databases, and offers 200 connectors to other data sources built and supported by partners.

Using data in Google Data Studio

Google Data Studio provides a graphical canvas onto which users drag and drop datasets. Users can set dimensions and metrics, specify sorting and filtering, and tailor the way reports and charts are displayed.

Keeping Contentful 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.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Contentful.

And remember, as with any code, once you write it, you have to maintain it. If Contentful modifies its API, or the API 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.

From Contentful to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Contentful 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 Contentful to Redshift, Contentful to BigQuery, Contentful to Azure Synapse Analytics, Contentful to PostgreSQL, Contentful to Panoply, and Contentful 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 Contentful with Google Data Studio. With just a few clicks, Stitch starts extracting your Contentful 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.