3.3. Data Connectors

3.3. Looker Studio Data Connectors

Data connectors act as a pipeline for a report. They extract data from a dataset through an API. They can also be used to transform and aggregate data.

The Role of Data Connectors

Data connectors play a crucial role in Looker Studio, as they bridge the gap between datasets (outside Looker Studio) and data sources (Inside Looker Studio).

They extract data from the APIs of tools, databases, and datasets available over the internet.

Some data connectors even transform and aggregate data before providing it to the data source, allowing for data modeling and transformation even before it reaches Looker Studio.

Types of Data Connectors

There are two primary types of data connectors: Google connectors and Partner connectors.

Google Connectors

Google connectors are free and allow you to connect to various Google products like Google Analytics, Google Sheets, YouTube, Google BigQuery, and Google Search Console. They can also connect to databases and data warehouses such as MySQL, PostgreSQL, Amazon Redshift, and Microsoft SQL Server. Google connectors are live connectors (more on this soon).

Partner connectors

Partner connectors are usually paid since developers need to spend time creating and maintaining them. These connectors are for non-Google tools and datasets that are accessible through APIs. Examples include Facebook Ads, Microsoft Ads, CRMs, Shopify, and MailChimp. Partner connectors can be either live or warehoused.

When a partner connector is free, it's typically built and maintained by the tool itself, such as CallRail, a call tracking software. They offer their connector as an added value service for their product.

Community Connectors

Community connectors are like partner connectors but not available to the public yet. If there isn't a Google or partner connector available for a particular tool, dataset, or marketing service, you can use Google App Script to create your own connector. This custom connector will allow you to bring data into Looker Studio from ANY publicly accessible API to model and visualize.

Creating a custom connector is relatively straightforward if you have access to development resources. You can even publish your connector as a partner connector, going through verification by Google and potentially monetizing it.

Live vs. Warehoused Connectors

Now that you know there are live and warehouse connections, let's briefly explain the difference:

Live connectors retrieve data directly from the dataset whenever the report loads, providing up-to-date information.
Warehoused connectors store a copy of the data in a separate location, such as a data warehouse, and connect to that storage for reporting purposes. This can be more efficient for larger datasets.

If you want to create a connector for large amounts of data that doesn't reside in Google Sheets, you can use Google Cloud Functions or Compute Engine to create connectors between the dataset and a data warehouse like BigQuery. Then, use the free connector between Looker Studio and BigQuery provided by Google to connect to that dataset.