Google Analytics 4 (GA4)

Knowi enables data discovery, visualization, data manipulation, warehousing, and reporting automation from Google Analytics V4, along with the ability to merge that data with other data stores. 

Overview

1. Connect, extract, and transform data from your Google Analytics V4, using one of the following options:

• Through our UI to connect directly.
• Using our Cloud9Agent. This can securely pull data inside your network. See agent configuration for more details.  

2. Visualize and Automate your Reporting instantly.

UI-Based Approach

Connecting

1. Log in to Knowi and select Queries from the left sidebar.
2. Click on New Datasource + button and select Google Analytics 4 from the list of datasources.
3. Authorize the Gmail account connected to Google Analytics.
4. Enter the following details: 
   - Datasource name: Enter a name for your datasource (Identifier)
   - Google Analytics Profile ID: Select the Property ID associated with your Google account. Please refer to the Property ID documentation to find your Property ID.
   - Refresh Token: Refresh Authentication Token returned by Google. This is used to connect and pull your GA reports. You can revoke access anytime at https://www.google.com/settings/u/1/security.
    
5. Click on the Save button and start Querying.

Query 

After connecting to the Google Analytics V4 datasource, Knowi will pull out a list of metrics along with field samples. Using these metrics, you can automatically generate queries through our visual builder in a no-code environment by either dragging and dropping fields or making your selections through the drop-down. 

• Metrics: Select a list of metrics you want to track from the drop-down (or type in). See API Schema for a list of all metrics.
• Dimensions: Dimensions enable the grouping of data for the metrics selected. Each dimension can be set with filters; it is name/value pairs. E.g for dimension "browser", you can filter as "Chrome". For more info, please see Dimension Filters.
• Filter Dimensions: Filter conditions applied to the data before aggregation. Use this to filter the dataset by a dimension without affecting how results are grouped - these dimensions are passed to the GA4 API via dimensionFilters only and do not appear as grouping dimensions in the response. For example, you can filter results to a specific country without including country as a grouping column in your output.
• Start Date: Specify a start date with a Date format: yyyy-MM-dd, or relative date (e.g., today, yesterday, or NdaysAgo where N is a positive integer. Note: Use Start and End Dates, or, alternatively, use the date range field to specify the last n time units.
• End Date: Specify an end date with a Date format: yyyy-MM-dd, or relative date (e.g., today, yesterday, or NdaysAgo where N is a positive integer. Note: Use Start and End Dates, or, alternatively, use the date range field to specify the last n time units
• Date Range: Specify a date range to pull data from. Leave this empty if you have already specified explicit start/end dates. Use a number followed by y for years, m for Months, w for weeks, d for days, h for hours, min for minutes. For example, 3m implies 3 months.

- Max Results: Maximum number of records to pull. Note: Google Analytics allows up to 10k rows per data pull.

- Sort By Field: Field sorting for the data returned. Example: Ascending: ga:visits Descending: -ga:visits

Note: You can also perform Cloud9QL transformations. 

Define data execution strategy by using any of the following two options:
 

• Direct Execution: Directly execute the Query on the original Datasource, without any storage in between. In this case, when a widget is displayed, it will fetch the data in real-time from the underlying Datasource.
   
• Non-Direct Execution: For non-direct queries, results will be stored in Knowi’s Elastic Store. Benefits include- long-running queries, reduced load on your database, and more. 

Non-direct execution can be put into action if you choose to run the Query once or at scheduled intervals. For more information, feel free to check out this documentation- Defining Data Execution Strategy

Click on the Preview button to analyze the results of your Query and fine-tune the desired output, if required.  

The result of your Query is called Dataset. After reviewing the results, name your dataset and then hit the Create & Run button. 

Cloud9Agent

Use Cloud9Agent as an alternative to the UI-based connectivity outlined above. The agent runs inside your network to extract data from Google Analytics 4 and sends the extracted/manipulated data into your Knowi warehouse. Check out Cloud9Agent to download your agent. 

For sample Google Analytics configuration, see the datasource_example_googleanalytics.json and query_example_googleanalytics.json examples folder under the Cloud9Agent install directory. 

Highlights:

• Connects to Google Analytics using OAuth tokens
• Pulls data using GA API, with optional manipulations using Cloud9QL
  Obtain the Refresh token using the Connect step in the UI section above.

Datasource Configuration: 

Query Configuration: 

Datasource Example:

[
  {
   "name":"demoGA",
   "datasource":"ga",
   "authToken":"1/JL-_Wkuan6c_Hs5FdEkgGMJ4wCgHm4STz2J7EgjN4OI",
   "gaProfileID":"ga:66465881"
  }
]

Query Example:

[
  {
    "entityName":"Website Visits",
    "dsName":"demoGA",
    "gaMetrics":"active7DayUsers",
    "dimensions" : [ {
        "name" : "date",
        "value" : [ "*" ],
        "operator" : "="
      } ],
    "dimensionFilter" : [ {
        "name" : "browser",
        "value" : [ "Chrome" ],
        "operator" : "="
      } ],
    "gaDateRange":"10d",
    "gaMaxResults":"1000",
    "gaSort":"active7DayUsers",
    "overrideVals":{
      "replaceAll":true
    },
    "frequencyType":"hour",
    "frequency":2
  }
]