Google Analytics vs Google BigQuery

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Google Analytics vs Google BigQuery: What are the differences?

Key Differences between Google Analytics and Google BigQuery

  1. Data Analysis vs. Data Warehousing: Google Analytics is primarily used for data analysis and tracking website user behavior, providing insights into user demographics, acquisition channels, and website performance. On the other hand, Google BigQuery is a data warehousing solution that enables businesses to store, query, and analyze large volumes of structured and semi-structured data in real-time.

  2. Real-Time vs. Batch Processing: With Google Analytics, data is processed and displayed in near real-time, allowing users to track website activity and metrics as they happen. In contrast, Google BigQuery is optimized for batch processing and analyzing large datasets over extended periods, making it a powerful tool for complex queries and deep analysis.

  3. Data Collection Method: Google Analytics collects data through website tracking codes, where JavaScript is embedded on web pages to capture user interactions. It relies on cookies and client-side tracking mechanisms. In contrast, Google BigQuery receives data from various sources, including Google Analytics, but it can also ingest data from other external systems, cloud storage, streaming data, or data warehouses.

  4. Data Accessibility and Scalability: Google Analytics provides a user-friendly interface and pre-built dashboards for easy access to data analysis and reporting. It offers a limited set of dimensions and metrics, suitable for general web analytics needs. In contrast, Google BigQuery provides more flexibility and scalability, allowing users to run complex SQL queries on vast amounts of data, with the ability to integrate with other data sources and conduct advanced data analysis.

  5. Pricing Model: Google Analytics offers both free and premium versions, with the premium version providing additional features and support. It is mainly aimed at small and medium-sized businesses. On the other hand, Google BigQuery operates on a pay-per-query basis, with separate pricing for storage and data processing. It aligns its pricing with the volume of data stored and the amount of data processed for analysis.

  6. Data Ownership and Integration: When using Google Analytics, the data collected is owned by the website owner, but Google has certain rights to use and analyze the data for its own purposes. Google Analytics data can be integrated with other Google products, such as Google Ads, to provide a holistic view of advertising and website performance. Google BigQuery, being a data warehousing solution, allows integration with various data sources, both internal and external, providing a unified view of large amounts of data.

In Summary, Google Analytics is a powerful tool for real-time web analytics and user behavior analysis, while Google BigQuery is a scalable data warehousing solution for advanced data analysis, querying massive datasets, and integration with multiple data sources.

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Pros of Google Analytics
Pros of Google BigQuery
  • 1.5K
  • 926
    Easy setup
  • 890
    Data visualization
  • 698
    Real-time stats
  • 405
    Comprehensive feature set
  • 181
    Goals tracking
  • 154
    Powerful funnel conversion reporting
  • 138
    Customizable reports
  • 83
    Custom events try
  • 53
    Elastic api
  • 14
    Updated regulary
  • 8
    Interactive Documentation
  • 3
    Google play
  • 2
    Industry Standard
  • 2
    Advanced ecommerce
  • 2
    Walkman music video playlist
  • 1
    Medium / Channel data split
  • 1
  • 1
    Financial Management Challenges -2015h
  • 1
  • 1
    Easy to integrate
  • 28
    High Performance
  • 25
    Easy to use
  • 22
    Fully managed service
  • 19
    Cheap Pricing
  • 16
    Process hundreds of GB in seconds
  • 12
    Big Data
  • 11
    Full table scans in seconds, no indexes needed
  • 8
    Always on, no per-hour costs
  • 6
    Good combination with fluentd
  • 4
    Machine learning
  • 1
    Easy to manage
  • 0
    Easy to learn

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Cons of Google Analytics
Cons of Google BigQuery
  • 11
    Confusing UX/UI
  • 8
    Super complex
  • 6
    Very hard to build out funnels
  • 4
    Poor web performance metrics
  • 3
    Very easy to confuse the user of the analytics
  • 2
    Time spent on page isn't accurate out of the box
  • 1
    You can't unit test changes in BQ data

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What is Google Analytics?

Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications.

What is Google BigQuery?

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

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