Connect Google Analytics to BigQuery

Connect Google Analytics to BigQuery in 2023

It is very simple to connect Google Analytics to BigQuery, it can be done in a fast and easy manner with Windsor.ai.

First Step:

You need to select Google Analytics as a Data Source and Grant Access to Windsor.ai.

Second Step:

You will need to go to Google BigQuery as a Destination.

Third Step:

Make sure you follow the instructions on the screen. Once you complete the setup your data will start streaming in the interval you specify. You can always come back and change the settings at a later stage.

 (Don’t forget to grant access to bq-upload@windsor-ai-bigquery.iam.gserviceaccount.com)

Fourth Step:

As a connector URL, you can use any URL providing a JSON. Either from the connectors or for example a URL with cached and transformed data.

Why do you need to load data from Google Analytics to BigQuery?

Loading data from Google Analytics to BigQuery offers several advantages for in-depth data analysis and reporting:

1. Flexible data analysis: Google Analytics provides valuable insights into website and app performance, user behavior, and marketing effectiveness. By loading Google Analytics data into BigQuery, you can perform more advanced and customizable data analysis using SQL queries, machine learning, and statistical models. This flexibility allows you to uncover deeper insights and answer specific business questions.
2. Large-scale data processing: BigQuery is designed to handle massive amounts of data efficiently. By loading Google Analytics data into BigQuery, you can process and analyze large volumes of data quickly, even as your data grows over time. This scalability is particularly useful for businesses with high website traffic or complex data analysis requirements.
3. Custom reporting and visualization: BigQuery integrates with various business intelligence and data visualization tools, such as Google Data Studio or Tableau. By loading Google Analytics data into BigQuery, you can leverage these tools to create customized reports, interactive dashboards, and visualizations tailored to your specific business needs. This enables you to communicate data-driven insights effectively to stakeholders.
4. Cross-platform data integration: BigQuery allows you to combine Google Analytics data with data from other sources, such as CRM systems, advertising platforms, or social media. This integration enables you to gain a holistic view of your customer journey, track conversions across different channels, and perform cross-channel analysis. By loading Google Analytics data into BigQuery, you can enrich your analysis by combining it with other relevant data sources.
5. Long-term data retention: Google Analytics has data retention limits based on your account type. By loading data into BigQuery, you can extend the retention period significantly, allowing you to analyze historical data beyond the default limits of Google Analytics. This is especially beneficial for long-term trend analysis, seasonality studies, or retrospective analysis of marketing campaigns.
6. Advanced segmentation and audience analysis: BigQuery’s powerful querying capabilities enable you to segment your Google Analytics data in more granular ways, beyond the standard segmentation options available in the Google Analytics interface. You can create complex audience segments based on user attributes, behavior, or custom dimensions, and analyze their interactions with your website or app in detail.

Conclusion

Integrating Google Analytics with BigQuery can help you to consolidate your data in real-time and analyze it to obtain data-driven decisions.

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