Youtube Analytics to BigQuery: 2 Easy Ways

As a data engineer, you hold all the cards to make data easily accessible to your business teams. Your team just requested a Youtube Analytics to BigQuery connection on priority. We know you don’t wanna keep your data scientists and business analysts waiting to get critical business insights. As the most direct approach, you can go straight for the Youtube Analytics APIs. Or, hunt for a no-code tool that fully automates & manages data integration for you while you focus on your core objectives.

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Well, look no further. With this article, get a step-by-step guide to connecting Youtube Analytics to BigQuery effectively and quickly, delivering data to your marketing team.

Replicate Data from Youtube Analytics to BigQuery Using Youtube Analytics APIs

To start replicating data from Youtube Analytics to BigQuery, you need to use one of the Youtube Analytics APIs. In this example, Reports Query API is used. You can hit API endpoints be specifying metrics parameters like Geography, Demographic, Traffic Source etc.

Step 1: Youtube provides the Youtube Analytics API to retrieve Youtube data. The following command is generated by Youtube Analytics for you after providing your authentication details. Youtube Analytics API uses Athuroziation 2.0, the detailed guide of Authorization can be found here. You can hut the endpoint using an API testing tool.

GET https://youtubeanalytics.googleapis.com/v2/reports

The 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: You can read the JSON files directly into BigQuery. 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.

Youtube Analytics to BigQuery: BigQuery Editor — Image Source

In the Create dataset window, you need to provide your dataset an ID, pick a source location, and provide the default table expiration period. Please remember that if you select “Never” for table expiration, the physical storage location won’t be chosen. You’ll need to specify how long you wish to keep temporary tables stored.

Now, create a table within the dataset. Finally choose JSON as the file format. Then you’ll be able to upload a JSON file from your computer, Google Cloud Storage, or Google Drive Disk.

This process using the Youtube Analytics APIs is a great way to replicate data from Youtube Analytics to BigQuery effectively and is optimal for the following scenarios:

If you need to program customized scripts that can be deployed with detailed instructions on completing each workflow stage.
If your JSON data doesn’t need any transformation and is present in analysis-ready form.

In the following scenarios, using the Youtube Analytics APIs might be cumbersome and not a wise choice:

Updating the existing API calls and managing workflows requires immense engineering bandwidth and hence can be a pain point for many users. Maintaining APIs is costly in terms of development, support, and updating.
If you have to transform a large amount of data, then manually transforming JSON data is a lengthy and tedious task.

When the frequency of replicating data from Youtube Analytics increases, this process becomes highly monotonous. It adds to your misery when you have to transform the raw data every single time. With the increase in data sources, you would have to spend a significant portion of your engineering bandwidth creating new data connectors. Just imagine — building custom connectors for each source, transforming & processing the data, tracking the data flow individually, and fixing issues. Doesn’t it sound exhausting?

How about you focus on more productive tasks than repeatedly writing custom ETL scripts? This sounds good, right?

In these cases, you can..

Replicate data from Youtube Analytics to BigQuery Using an Automated ETL Tool

Here, are the benefits of leveraging a no-code tool:

Automated pipelines allow you to focus on core engineering objectives while your business teams can directly work on reporting without any delays or data dependency on you.
Automated pipelines provide a beginner-friendly UI. Tasks like configuring and establishing connection with source and destination, providing credentials and authorization details, performing schema mapping etc. are a lot simpler with this UI. It saves the engineering teams’ bandwidth from tedious preparation tasks.

For instance, here’s how Hevo, a cloud-based ETL tool, makes Youtube Analytics to BigQuery data replication ridiculously easy:

Step 1: Configure Youtube Analytics as a Source

Authenticate and Configure your Youtube Analytics Source.

Youtube Analytics to BigQuery: Configure your Youtube Analytics source — Image Source

Step 2: Configure BigQuery as a Destination

In the next step, we will configure BigQuery as the destination.

Youtube Analytics to BigQuery: Configure your BigQuery destination — Image Source

Step 3: All Done to Setup Your ETL Pipeline

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. Depending on your needs, you can adjust the pipeline frequency from 5 minutes to an hour.

Data Replication Frequency

Default Pipeline Frequency	Minimum Pipeline Frequency	Maximum Pipeline Frequency	Custom Frequency Range (Hrs)
1 Hr	15 Mins	24 Hrs	1-24

In a matter of minutes, you can complete this No-Code & automated approach of connecting Youtube Analytics to BigQuery using Hevo and start analyzing your data.

Hevo offers 150+ plug-and-play connectors(Including 40+ free sources). It efficiently replicates your data from Youtube Analytics, databases, data warehouses, or a destination of your choice like Google BigQuery in a hassle-free & automated manner. Hevo’s fault-tolerant architecture ensures that the data is handled securely and consistently with zero data loss. It also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.

Hevo’s reliable data pipeline platform enables you to set up zero-code and zero-maintenance data pipelines that just work. Here’s what allows Hevo to stand out in the marketplace:

Fully Managed: You don’t need to dedicate time to building your pipelines. With Hevo’s dashboard, you can monitor all the processes in your pipeline, thus giving you complete control over it.
Data Transformation: Hevo provides a simple interface to cleanse, modify, and transform your data through drag-and-drop features and Python scripts. It can accommodate multiple use cases with its pre-load and post-load transformation capabilities.
Faster Insight Generation: Hevo offers near real-time data replication, so you have access to real-time insight generation and faster decision-making.
Schema Management: With Hevo’s auto schema mapping feature, all your mappings will be automatically detected and managed to the destination schema.
Scalable Infrastructure: With the increase in the number of sources and volume of data, Hevo can automatically scale horizontally, handling millions of records per minute with minimal latency.
Transparent pricing: You can select your pricing plan based on your requirements. Different plans are clearly put together on its website, along with all the features it supports. You can adjust your credit limits and spend notifications for any increased data flow.
Live Support: The support team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

Take our 14-day free trial to experience a better way to manage data pipelines.

Get started for Free with Hevo!

What Can You Achieve by Migrating Your Data from Youtube Analytics to BigQuery?

Here’s a little something for the data analyst on your team. We’ve mentioned a few core insights you could get by replicating data from Youtube Analytics to BigQuery. Does your use case make the list?

What percentage of customers from a region have the most engagement on Youtube?
Which YouTube videos are most popular in a country?
How to check YouTube stats?
How to pull data from Youtube?

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.

Visit our Website to Explore Hevo

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. No need to go to your data warehouse for post-load transformations. You can simply run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form.

Want to take Hevo for a ride? Sign Up for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.

Share your experience of replicating data from Youtube Analytics to BigQuery! Let us know in the comments section below!