As a digital marketing analyst, having a thorough understanding of the latest tools and technologies helps you manage data inflow.
With the introduction of Google Analytics 4, an improved data collection and reporting tool, the future of digital marketing is now AI and privacy-focused.
Along with new capabilities, GA4 enables users to access BigQuery for free.
Initially only available to Google Analytics 360 (paid) users, BigQuery allows you to store massive datasets.
In this blog post, we look at how BigQuery integration with GA4 helps you simplify complex data and derive actionable insights for marketing campaigns.
Let’s dive in!
1. The beta version of Universal Analytics was launched in 2012 for premium customers.
2. It supported new tracking codes for websites and provided detailed information about user behavior.
3. UA has an event-based model, less robust cross-device and cross-platform tracking, and provides bounce rate and beginner-level exploration reports.
Google Analytics 4
1. Google Analytics 4 is a session-based model available for websites and mobile applications.
2. It introduced the concept of engagement rate and helps you calculate user count using ‘User ID’.
3. GA4 uses machine learning to understand user behavior.
4. Its privacy-first tracking approach enables you to work without cookies or identifying data.
Universal Analytics to Google Analytics 4: All You Need to Know
Google Analytics 360
Google Analytics 4 has two versions – GA4 Standard and GA 360. While GA4 is a free/standard version, GA 360 is the premium version.
1. GA 360 is a part of Google’s Marketing Platform that provides in-depth statistical and analytical information about your website.
2. It offers seamless integration with Google Ads, Google Optimize, Google Display and Video 360.
3. With GA 360, businesses can access all standard Analytics features as well as advanced analytics tools such as data-driven attribution, unsampled reports, and BigQuery export.
What is Google BigQuery?
Google BigQuery is a serverless data warehouse that can store highly scalable data.
It comes with a built-in query engine that can run SQL queries on terabytes of data within seconds.
You don’t need to manage infrastructure or rebuild indexes to derive quick, real-time results with massive amounts of data.
The working of BigQuery is divided into three main parts:
1. Ingestion: To work using BigQuery, you need to create a table to run different operations on it. These tables can be in the form of Native Tables, External Tables, and Views. Once you’ve created a table, load/ingest the data into it. You can either do this automatically using Data Transfer Service or manually by:
a. Loading data in batches
b. Streaming individual data records
c. Using queries to generate new data
d. Using a third-party application/service
2. Storage & Preparation: Data is stored in BigQuery and prepared for analysis. Raw data is refined and sent for further analysis.
3. Analysis/Exporting Data: Once the table is ready with data, it can be exported from BigQuery in different ways. You can also either manually export data or automate the export process using the Dataflow service. Once data is exported, you can use BigQuery ML and Google Data Studio to analyze data.
Features of Google BigQuery
1. Multi Cloud Functionality (BQ Omni)
BigQuery enables you to analyze data in multiple clouds. It can compute data from wherever it is located.
BigQuery Omni runs on Anthos clusters that Google Cloud manages. This enables secure query execution even on foreign cloud platforms.
Overall, BQ Omni enables you to:
a. Break down data silos and gather actionable insights
b. Get consistent data experiences across clouds
c. Extend flexibility using Anthos clusters
2. Built-in Integration (BQ ML)
You can create and execute machine learning models in BigQuery by using simple SQL queries.
BQ ML has eliminated the need for ML-specific programming skills and enabled SQL practitioners to develop ML models with their current knowledge and skills.
Using BigQuery ML with a cloud-based data warehouse has three main benefits:
a. Since there’s no need to export data, the speed of model development is quick.
b. There’s no need to program ML solutions using Python or Java.
c. ML models can be built and run using existing BI tools.
3. Foundation for BI (BQ BI Engine)
BigQuery BI Engine is an in-memory analysis solution that helps you analyze data stored in BigQuery with high concurrency and rapid response times.
The SQL interface of BigQuery allows you to interact with BI tools such as Power BI, Looker, and Tableau.
You can integrate BQ BI Engine with custom applications. BigQuery BI Engine offers data analysis over streaming data which results in fast response and load time.
The real-time analysis that BQ provides eliminates the need for ETL pipelines. Smart tuning design ensures fewer configuration settings from the user’s end.
4. Geospatial Analysis (BQ GIS)
BigQuery Geographic Information Systems showcase data around geolocation mapping.
BigQuery GIS converts latitude and longitude columns into different geographic points.
The geospatial data analysis is done by using any one of the following:
a. BigQuery Geo Viz
b. Google Earth Engine
c. Jupyter Notebooks (Using Extension)
5. Automated Data Transfer (BQ Data Transfer Service)
BigQuery Data Transfer Service automates regular data movement into BigQuery. The analytics team doesn’t need to do coding, instead one can add data to fill in gaps and outages during ingestion.
The BQ Data Transfer Service can be done using:
a. Cloud Console
b. BQ Command-Line-Tool
c. BigQuery Data Transfer Service API
6. Free Access (BQ Sandbox)
If you want to try BigQuery features, Google BigQuery Sandbox is the answer.
You won’t need a billing account or project to experience BigQuery and the Cloud Console.
The applications will run in an environment emulated by the BigQuery Google Cloud Platform.
Once the user is satisfied with the features, they can upgrade to the full BigQuery experience.
Advantages of GA4 BigQuery Integration
1. Access BigQuery for Free
BigQuery was earlier only available to Google Analytics 360 users but with GA4, it can be accessed for free.
It enables you to store huge amounts of data and run super-fast SQL queries using the processing power of Google’s infrastructure.
2. Manipulate Advanced Data
Certain dimensions and combinations of metrics cannot be queried together while using the GA4 interface or API. BigQuery resolves all such limitations.
It allows you to manipulate GA4 data and make advanced data segmentation and analysis feasible.
3. Integrate & Analyze Data
BigQuery is a data warehouse that helps you combine data from different data sources such as Google Analytics, Facebook Ads, and Google Ads. This fixes data integration issues, cross-platform data analysis, and reporting.
4. Filter Out Incorrect GA4 Data
If you use the GA4 interface to query data, you won’t be able to correct the data or filter it out.
However, BigQuery enables you to filter out incorrect data available in the GA4 interface. You can also modify this data from your analysis and reports.
5. Access Unsampled & Raw Event Data
The GA4 user interface doesn’t allow you to access raw events from your GA4 property. It also does not allow you to access unsampled data. Even if you do not have a GA 360 account, you can access the raw event and unsampled data via BigQuery.
Google Analytics 4 Cheat Sheet
How GA4 BigQuery Integration Helps You Save Costs
GA4 and BigQuery integration opens doors for businesses to access improved features and get started for free.
Let’s take a look at the table below to learn how this integration is a game-changer in the way web analytics is done.
GA3/ Universal Analytics
GA4 + BigQuery Integration
$150K per year, based on quotas & usage
Free + BigQuery cost
10 million hits per property
Unlimited data collection
Allows sampling over 500K sessions
Starts sampling at 100 million sessions per view
Standard view – No sampling
Advanced view – 10 million events
BigQuery Integration & Advanced Analysis UI
Search Console, Optimize, Google Ads
All Integrations from GA3 + Google 360 Products
Integrating Google Analytics 4 with BigQuery can help you save costs and get access to features that are otherwise not available in the standard GA4 version.
Businesses with datasets spanning billions of rows might look at this integration as the ideal option to import, analyze, and export whatever data is required.
Although GA 360 will be a powerful tool, GA4 and BigQuery integration is likely to reshape the competitive business landscape.
Learn More About GA4 + BigQuery Integration. Contact Us Today.
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