Why Use BigQuery For UA & GA4 Google Analytics Tracking?

In a world driven by data, organizations are constantly seeking more efficient ways to manage and analyze vast amounts of Google Analytics data. One such solution in the world of data management is Google BigQuery. This cloud-based data warehousing and analytics platform has altered the way businesses handle their analytics data.

In this article, we will dive into the arena that is BigQuery, exploring what it is, its advantages and disadvantages, the types of data it can store, its pricing (with a focus on the free tier), and its compatibility with popular reporting software such as Looker Studio.

Home » Blog » Why Use BigQuery For UA & GA4 Google Analytics Tracking?

Analytics Tracking

by Robin Hammond

1 year ago 4,560 12829

In this article, we will dive into the arena that is BigQuery, exploring what it is, its advantages and disadvantages, the types of data it can store, its pricing (with a focus on the free tier), and its compatibility with popular reporting software such as Looker Studio.

What is BigQuery?

Google BigQuery, often simply referred to as BigQuery, is a fully managed, serverless, and highly scalable data warehouse offered by Google Cloud Platform (GCP). It is designed to help organizations process and gain insights from massive datasets in real-time. Unlike traditional on-premises data warehouses, BigQuery eliminates the need for infrastructure and high cost servers and allows users to focus more on their data.

Key Advantages of BigQuery

Scalability: BigQuery can handle massive datasets, making it suitable for businesses of all sizes, from startups to large enterprises.
Speed: Query execution in BigQuery is incredibly fast, even when dealing with massive datasets. This is achieved through the use of Google’s distributed computing power.
Serverless: BigQuery is a serverless platform, meaning users don’t have to worry about hardware provisioning, maintenance. Google takes care of all infrastructure-related tasks.
Integration: One of the great features of Google BigQuery is its integration with the wider Google Cloud ecosystem. BigQuery easily integrates with various Google Cloud services, such as Google Cloud Storage, Dataflow, and Dataprep, and GAt4 and be scheduled to back up to BigQuery automatically, it also allows data to be exported to report making software for easy viewing.
Ease Of Use: With its support for standard SQL, BigQuery is accessible to a wide range of users, including SQL-savvy analysts and data scientists. Its user-friendly interface makes it easy to get started with data analysis.

More Than Just Website Data

If you wish, BigQuery can keep all your data in one place allowing for easy access capabilities across multiple in-house departments, allowing for improved transferability and communication between divisions.

Sales Data: BigQuery can store detailed sales information, including product sales, revenue, and sales trends.
Financial Records: It’s a secure repository for financial data, such as earnings, expenses, and financial statements.
Website Analytics: BigQuery is well-suited for storing data related to website traffic, user interactions, and web performance metrics.
Customer Data: It securely manages customer information, including profiles, purchase histories, and preferences.
Inventory Management: BigQuery is used to track inventory levels, monitor stock movements, and manage supply chain data.
Employee Records: It serves as an organized database for employee information, including personal details, job roles, and performance evaluations.
Supply Chain Data: BigQuery helps businesses keep track of their supply chain processes, including data related to the origin and movement of products, ensuring efficient inventory management and logistics.
Customer Feedback: It’s a valuable repository for customer feedback data collected from various sources, allowing businesses to assess customer satisfaction, sentiment, and opinions, leading to improvements in products or services.

Disadvantages of BigQuery

While BigQuery offers numerous advantages, it’s essential to be aware of its limitations and potential drawbacks:

Cost Management: Although BigQuery’s pay-as-you-go pricing model is cost-effective for many organizations, it can become expensive if not properly managed. Running complex and resource-intensive queries without optimization can lead to unexpected costs.
Learning Curve: Despite its user-friendly interface, BigQuery may still have a learning curve for users unfamiliar with SQL or cloud-based data warehouses.
Limited Free Tier: BigQuery does offer a free tier, allowing users to store up to 10 GB of data per month at no cost and 1TiB of query processing. However, this limit can be reached, especially for businesses with larger datasets or higher query volumes, costs starting at $0.02 per GB stored per month and $6.25 per Tb of processing.
Data Location: Data stored in BigQuery is location-specific, meaning it is associated with a particular region or data centre. This can affect data latency and access times.
Data Lock-In: Once an organization starts using BigQuery, it may become tightly integrated with Google Cloud Platform. This can create a degree of vendor lock-in, making it challenging to migrate data and workloads to other cloud providers or on-premises environments.

BigQuery Pricing and the Free Tier

BigQuery offers a pricing model that charges users based on the amount of data processed during queries, data storage, and streaming inserts. It’s important to note that while BigQuery does have a free tier, it has usage limitations.

Free Tier:

BigQuery’s free tier allows users to store up to 10 GB of data per month at no cost and run queries using 1Tb of processing power per month. It’s a great way for small and medium businesses to store data without incurring expenses. However, that 10 GB limit can be quickly exhausted in a professional setting, especially for larger datasets or frequent querying.

Pricing:

Beyond the free tier, BigQuery pricing is based on several factors:

Data Storage: You are charged for the data you store in BigQuery. The pricing depends on the amount of data stored and its location. For active storage (information accessed within the last 90 days) is $0.02 per month and this drops to half the prices afterwards.

Querying: You are billed for the amount of data processed during query execution. The cost per terabyte (TB) processed varies depending on the region but it can be expected to be $6.25 per TiB after the first free Tib is used. It’s important to monitor your usage and optimize queries to control costs effectively. Google Cloud provides a pricing calculator to estimate costs based on your specific usage patterns.

Integration with Looker Studio

Integration: Looker Studio offers integration with BigQuery, simplifying the process of connecting your data to Looker’s platform. This integration allows users to create and share interactive dashboards, reports within Looker, using the power of BigQuery’s data processing capabilities.

Enhanced Data Exploration: Looker Studio’s intuitive interface enables users to explore and analyze data stored in BigQuery easily. Data can be created and customized to make reports, pivot tables, and charts without extensive SQL knowledge, making data analysis accessible to a broader audience.

Real-time Data Insights: By combining BigQuery’s real-time data streaming capabilities with Looker Studio, organizations can gain immediate insights from their streaming data.

Who Else Does Analytics Data

Amazon Redshift:

Fully managed, petabyte-scale data warehouse.
Compatible with popular business intelligence tools.
Offers both on-demand and provisioned pricing.
Costs: On-demand usage can range from a few hundred dollars per month for small workloads to thousands of dollars for larger ones. Provisioned pricing starts at around $1,000 per month for a small cluster.

Snowflake:

Unique architecture separates storage and compute resources.
Supports semi-structured data and offers ease of use.
Pay-as-you-go pricing based on actual usage.
Costs: Prices typically start at a few dollars per credit consumed, with the actual cost depending on the volume of data processed and stored.

**Azure Synapse Analytics (formerly SQL Data Warehouse):**

Fully managed and scalable data warehousing solution.
Integrates with Azure services and supports on-demand and provisioned resources.
Costs: On-demand pricing starts at approximately $2 per query per TB scanned. Provisioned pricing begins at around $1,000 per month for a small data warehouse.

IBM Db2 on Cloud:

Cloud-native database service with data warehousing capabilities.
Features like automated backups, workload management.
Pricing based on service plan and data usage.
Costs: Prices can vary based on the chosen service plan, starting at around $0.25 per IBM Cloud SQL Query Unit (CSQU) and data storage costs of approximately $2 per GB per month.

While these competitors offer alternatives to Google BigQuery, cost considerations are essential. Each has its pricing structure, and organizations should carefully assess their specific needs and budget to make an informed decision.

Conclusion

Google BigQuery is a formidable player in the world of data warehousing and analytics. Its speed, ease of use, and seamless integration with tools like Looker Studio makes it an attractive choice for businesses seeking to harness the power of their data. However, organizations must also be mindful of potential cost management challenges and data location considerations. By understanding the advantages and disadvantages of BigQuery and exploring its compatibility with reporting software like Looker Studio, businesses can make informed decisions about their data strategy and unlock valuable insights to drive growth and innovation.