Connect YouTube Analytics to BigQuery: 2 Easy Methods

Easily move your data from YouTube Analytics To BigQuery to enhance your analytics capabilities. With Hevo’s intuitive pipeline setup, data flows in real-time—check out our 1-minute demo below to see the seamless integration in action!

In today’s digital landscape, YouTube Analytics provides much-needed data to content creators and businesses at a time when they might have to optimize strategies. Whenever big datasets are involved, performing analysis straight within YouTube is a hassle. However, BigQuery enables you to store, query, and analyze your YouTube data efficiently by connecting your YouTube Analytics to BigQuery. 
Let’s take a step-by-step approach toward making this integration easy for you to unlock deeper insights and make more informed decisions.

What is YouTube Analytics?

YouTube Analytics provides a detailed performance view of your channel, including views and watch time of your videos, audience demographics, and engagement. It assists content creators and marketers in understanding their audiences, optimizing content, and measuring strategy success.

What is BigQuery?

BigQuery is a fully managed serverless data warehouse offered by Google Cloud to support fast SQL queries over large datasets. It allows you to analyze huge volumes of data efficiently and fast enough, thus empowering data-driven decisions with strong analytics capabilities and integration with other sources.

Method 1: Replicate Data from YouTube Analytics to BigQuery Using Hevo

Step 1: Configure YouTube Analytics as a Source

Step 2: Configure BigQuery as a Destination

Your ETL Pipeline is set up! Once your Youtube Analytics to BigQuery ETL Pipeline is configured, Hevo will collect new and updated data from Youtube Analytics every five minutes (the default pipeline frequency) and duplicate it into BigQuery. NOTE: You can adjust the pipeline frequency from 5 minutes to an hour, depending on your needs.

Method 2: Replicate Data from YouTube Analytics to BigQuery Using YouTube Analytics APIs

In the example below, the Reports Query API is used. You can hit API endpoints by specifying metrics parameters like Geography, Demographics, Traffic Source, etc.  

Step 1: Generating YouTube Analytics API 

• YouTube provides the Youtube Analytics API to retrieve YouTube data. The following command is generated by YouTube Analytics for you after you provide your authentication details.
  GET https://youtubeanalytics.googleapis.com/v2/reports

Below is a sample response you will get on hitting the API endpoint:

{
  "kind": "youtubeAnalytics#resultTable",
  "columnHeaders": [
    {
      "name": string,
      "dataType": string,
      "columnType": string
    },
    ... more headers ...
  ],
  "rows": [
    [
      {value}, {value}, ...
    ]
  ]
}

Step 2: JSON files into BigQuery

NOTE: Before uploading the data to BigQuery, you first need to navigate to Google BigQuery homepage and select a resource where you need to build the dataset.

• Step 2.1: Create a dataset by providing a dataset ID, source location, and default table expiration period.
  NOTE: If you select “Never” for table expiration, the physical storage location will not be chosen. You’ll need to specify how long you wish to keep temporary tables stored.
• Step 2.2: Now, create a table within the dataset and choose JSON as the file format to upload a JSON file from your computer, Google Cloud Storage, or Google Drive Disk.

Limitations of Using YouTube APIs

1. Rate Limits: The use of the API is subjected to quotas and rate limits and could restrict the amount of data you can transfer.
2. Complexity: Involves dealing with API authentication and data extraction/ transformation processes.
3. Data Latency: Data being updated periodically does not reflect real-time updates and may have potential delays in being analysed.
4. Limited data availability: Some metrics and dimensions in YouTube Analytics may be unavailable through the API.

Benefits of Migrating Your Data from YouTube Analytics to BigQuery

1. Advanced Data Analysis: Perform complex query operations and detailed analysis on large datasets.
2. Thorough Reporting: Combine YouTube data with other sources to get the fullest picture.
3. Improved Decision Making: Drive content and marketing strategies with detailed, in-depth analytics.
4. Better Data Integration: Pool the data from YouTube Analytics with other Google tools seamlessly.
5. Custom Dashboards: You can construct custom dashboards to monitor and visualise the key metrics relevant to your business.

Summing It Up

Youtube Analytics APIs is the right path for you when your team needs data from Youtube Analytics once in a while. However, an ETL solution becomes necessary if there are rapid changes in the source and frequent data replication needs to be done to meet the data demands of your product or marketing channel. You can free your engineering bandwidth from these repetitive & resource-intensive tasks by selecting Hevo’s 150+ plug-and-play integrations.

Saving countless hours of manual data cleaning & standardizing, Hevo’s pre-load data transformations get it done in minutes via a simple drag-and-drop interface or your custom Python scripts.

Frequently Asked Questions