Setting up Firebase Analytics BigQuery Integration: 2 Easy Methods

Today, one of the main problems faced by business organizations is handling huge amounts of data and delivering it to their clients without any complexity. While there are large datasets, and processing requirements with a variety of complex queries to be written, firing those queries can be very time-consuming in DB applications like PostgreSQL, or PLSQL. Google BigQuery is one proposed solution to process complex SQL-like queries instantly. This is why many companies are leaning towards using Google Firebase Analytics in tandem with Google BigQuery to combine the featural benefits of both. You might also read our article about setting up Firebase BigQuery integration.

This article will help you explore the beneficial features of Google BigQuery and Firebase Analytics, along with the two methods you can leverage to establish Firebase Analytics BigQuery Integration for optimized use. You will also read about the best practices for exporting data in Firebase Analytics BigQuery Integration and the quick steps to carry out the export. 

What is Google BigQuery?

Google BigQuery is a cloud-based REST service platform that executes complex analytical queries for large datasets with access to read-only permission with ease. Google BigQuery makes the task easier and simple to perform. You need to just upload large datasets into Google BigQuery, and run the same complex queries as the Postgre queries. 

Running such complex queries tends to slow down the system and in turn, makes it difficult for companies to analyze data and make constructive decisions for their business benefits. This not only affects business performance, but also the consistency and efficiency of Predictive Analysis. 

Google BigQuery simplifies this scenario by running the query instantly. It reduces the throughput and lowers the CPU performance. It has a range of useful features that can help out business organizations to make a decision using Big Data Analytics.

Key Features of Google BigQuery

There are some specific features that make Google BigQuery efficient to use for larger datasets and data warehouses. These are highlighted as follows:

• Free Access: The Google BigQuery sandbox provides an amazing interface with all-time free access if you are new to using it. Google BigQuery enables the user to use the full power of Google BigQuery to set certain limits. It grants permission to use Google BigQuery without using a credit card or billing the project.
• Strict Monitoring And Auditing: Well, cloud storage is a bit risky, as one can either steal data or hacking is a concern. The Google BigQuery platform has no such issues, as it serves all the applications with cloud auditing services and logging. For any mishap activities, it generates an alert. 
• Interactive Programming: Business users find it difficult to code the platform with various programming languages or sometimes it might be difficult to switch from one programming language to another. Google BigQuery grants extreme ease with the REST API, which helps the clients access in-built libraries available in different languages like Java, Python, Node.js, C#, Go, Ruby, and PHP.
• High Performance: Google BigQuery improves performance to get quick results for petabytes of datasets. It makes it seamlessly easy to store and analyze even zettabytes and petabytes of datasets.
• High Availability: Google BigQuery has automated higher availability in which it automatically duplicates the data and also stores it in multiple locations for better availability and faster reach.
• Real-Time Analytics: Google BigQuery has proven evidence for real-time streaming data. It has APIs available that rapidly speed up high streaming and real-time analytics which enables business users to observe their results in seconds.

Google Firebase, in tandem with Google BigQuery, can capture raw, unstructured data available in the dataset. With the help of Google Firebase Analytics, users can access a statistical report that mentions the user’s activities on their web app or mobile app to decide on improvements that are to be made for profitable revenue. This makes Firebase Analytics BigQuery Integration an advantageous action.

What is Google Firebase Analytics?

Google Firebase Analytics helps to analyze the user interaction and its usability with their web application or mobile application to generate a statistical report on the user’s usage. This helps the business user and the IT team to get more clarity in decision-making for improving the performance of the app and improving the business revenue. 

Key Features of Google Firebase Analytics

• Limitless Reporting: With Google Firebase Analytics, it is easy to generate a statistical report. Thus, it provides unlimited report generation for more than 500 distinct raw data or event logs.
• Easy Audience Segmentation: Based on the user data, custom events can also be added to the firebase console so that it invites a new audience to explore the new features and notifications.
• Easy Integration: Google Firebase Analytics has made the task of integrating easier along with other interfaces like Google BigQuery.

A multitude of features that can integrate other interfaces and fetch data for the performing analytics can be experienced with Google Firebase Analytics. As Google BigQuery uses multiple datasets, it often becomes difficult for a business user to get feedback from a user perspective about using their applications. Setting up Firebase Analytics BigQuery Integration reduces the load of predicting results and performing analytics on raw data. In the next section, you will explore the steps to establish Firebase Analytics BigQuery Integration.

Why integrate Firebase Analytics with Google BigQuery?

You can integrate Firebase Analytics into Google BigQuery for custom analysis. This customized analysis comes in handy while aggregating data from the Android and iOS versions of the app. Firebase Analytics BigQuery integration also allows you to access custom parameters that you pass within your Firebase Analytics events.

Working with Complex Queries

If you wish to run a query across both platforms of your app across a specific date range, you can leverage Firebase Analytics BigQuery integration for the same. Firebase Analytics needs to split the data into tables for each day, you can use BigQuery’s TABLE_DATE_RANGE function for the same. This query will then return a count of the cities users that are coming from over a one-week period:

SELECT
  user_dim.geo_info.city,
  COUNT(user_dim.geo_info.city) as city_count 
FROM
TABLE_DATE_RANGE([firebase-analytics-sample-data:android_dataset.app_events_], DATE_ADD('2016-06-07', -7, 'DAY'), CURRENT_TIMESTAMP()),
TABLE_DATE_RANGE([firebase-analytics-sample-data:ios_dataset.app_events_], DATE_ADD('2016-06-07', -7, 'DAY'), CURRENT_TIMESTAMP())
GROUP BY
  user_dim.geo_info.city
ORDER BY
  city_count DESC

You can even write a query to compare mobile vs tablet target usage across platforms for a one-week period:

SELECT
  user_dim.app_info.app_platform as appPlatform,
  user_dim.device_info.device_category as deviceType,
  COUNT(user_dim.device_info.device_category) AS device_type_count FROM
TABLE_DATE_RANGE([firebase-analytics-sample-data:android_dataset.app_events_], DATE_ADD('2016-06-07', -7, 'DAY'), CURRENT_TIMESTAMP()),
TABLE_DATE_RANGE([firebase-analytics-sample-data:ios_dataset.app_events_], DATE_ADD('2016-06-07', -7, 'DAY'), CURRENT_TIMESTAMP())
GROUP BY
  1,2
ORDER BY
  device_type_count DESC

If you have stored data in Google Analytics for the same app, you can even export your Google Analytics data to BigQuery and perform a JOIN with your Firebase Analytics BigQuery tables.

Visualizing Analytics Data

After you’ve gathered insights from your data by leveraging raw BigQuery support, you can visualize the data by using Google Data Studio. It allows you to read data directly from the BigQuery tables, and you can even pass it to a custom query like the ones mentioned above. For instance, you can create a bar chart to compare the device types from which users are accessing your app on every platform. You can paste the mobile vs. tablet query mentioned above directly into Data Studio to develop the following chart:

How to Load data to BigQuery?

The best option to load data for Firebase Analytics BigQuery integration is to set up a connection between BigQuery and your data source for automated synchronization. You can use Hevo Data for the same.

What data is available for export from Firebase to Google BigQuery?

For Firebase Analytics BigQuery integration, you can export the following project data:

• Cloud Messaging
• Analytics
• A/B Testing
• Crashlytics
• Predictions
• Performance Monitoring

How to Establish Firebase Analytics BigQuery Integration?

This article has given you a roundabout of the benefits of Firebase Analytics BigQuery Integration and the salient features, along with the benefits of these two tools that make them so good together. Establishing Google Firebase Analytics BigQuery Integration can help you save time and strategically allocate resources to dig deeper into customer data for new actionable insights that help you achieve your business goals. In the next section, you will look at the different methods available at your disposal:

• Method 1: Using Google Platform for Firebase Analytics BigQuery Integration
• Method 2: Using Hevo Data for Firebase Analytics BigQuery Integration

Method 1: Using Google Platform for Firebase Analytics BigQuery Integration

In this method, you will look at how to establish Firebase Analytics BigQuery Integration using the Google Platform. Follow these simple steps to initiate Firebase Analytics BigQuery Integration:

• Step 1: Open your favorite browser, and enter the ‘Firebase Console URL’.
• Step 2: For Opening the Google Firebase Console page, ‘Sign-in with your Google Account.
• Step 3: After Sign-in, the first job is to Select the Google Firebase App that is created and Link it with Google BigQuery.
• Step 4: Once the project is opened, ‘Click on the Project Settings icon’ A page will appear, slide on and Select the option ‘Account Linking’.
• Step 5: The ‘Account Linking’ page opens. You need to select the ‘Upgrade Project and Link Button’ that is present at the right bottom titled ‘Analyze the Raw Event Data With BigQuery’.
• Step 6: After Clicking the Button, a pop-up will appear, asking to confirm the purchase. If you haven’t purchased it, then you can proceed with your preferred payment options to pay.
• Step 7: On Successful payment, a dialogue box appears titled Linking Google BigQuery with a ‘continue’ button. Click on the “Continue” button.
• Step 8: After hitting the Continue button, the project gets added to Google BigQuery. To check it, open a new tab, and navigate to ‘bigquery.cloud.google.com’.
• Step 9: If you have followed the steps clearly, then on the page opened, you will get to see your ‘Firebase Project’ with all the raw data and event logs present on the left side.

With these, you can successfully integrate Google Firebase Analytics with Google BigQuery. You can also apply filters by IDs to perform analytics.

Method 2: Using Hevo Data for Firebase Analytics BigQuery Integration

Hevo Data, a No-code Data Pipeline, helps you transfer data from Google Firebase Analytics (among 100+ sources) to Google BigQuery & lets you visualize it in a BI tool. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

It provides a consistent & reliable solution to manage data in real-time and always has analysis-ready data in your desired destination. It allows you to focus on key business needs and perform insightful analysis using various BI tools such as Power BI, Tableau, etc. 

Steps to use Hevo Data for Firebase Analytics BigQuery Integration:

Hevo Data focuses on two simple steps to get you started:

• Configure Source: Connect Hevo Data with a data source of your choice such as Google Firebase Analytics.
• Integrate Data: Load data from Google Firebase Analytics to Google BigQuery by providing your Google BigQuery database credentials such as your authorized Google BigQuery account, along with a name for your database, destination, and project Id.

Using manual scripts and custom code to move data into the warehouse is cumbersome. Changing API endpoints and limits, ad-hoc data preparation and inconsistent schema makes maintaining such a system a nightmare. Hevo’s reliable no-code data pipeline platform enables you to set up zero-maintenance data pipelines that just work.

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How to export Firebase active users to BigQuery?

Firebase Analytics raw data can easily be exported in intraday BigQuery tables. To extract the required metrics, such as active users for a specified period, you will have to query those tables. Firebase considers the active users to be the users who have engaged with an app and logged in to a user_engagement event. This allows you to obtain this metric from the intraday data that moves from Firebase to BigQuery through the following query:

SELECT
  COUNT(*) AS active_users
FROM (
  SELECT
COALESCE(user_dim.user_id, user_dim.app_info.app_instance_id) AS user_id
  FROM TABLE_DATE_RANGE([XXXXX.app_events_], TIMESTAMP('2021-01-24'), TIMESTAMP('2021-01-29'))
  WHERE
    DATE(event_dim.timestamp_micros) = '2021-01-25'
    AND event_dim.name ='user_engagement'
  GROUP BY
    user_id )

Here, you’ll have to replace the dates in the code snippet with your values.

Best Practices for Exporting Data in Firebase Analytics BigQuery Integration

Google Firebase offers users efficient tools that help in analyzing the app usage and understanding how your app performs. There are some best practices that you can keep in mind for exporting data from Google Firebase Analytics to Google BigQuery. For starters, you can always make a copy of existing data along with setting up daily syncs to be on the safer side. Here are a few more best practices you can keep in mind for Firebase Analytics BigQuery integration:

• Don’t compare events like user_engagement or session_start that get triggered quite often.
• You can use the table for a specific day as opposed to using a date comparison on the event timestamp within the WHERE clause. The event timestamp is based on the client device time, which can often be skewed.
• Always obtain a report past 3 days so that the data exported from GA4F to Google BigQuery gets normalized. Data is only exported to Google BigQuery once a day. Therefore, the queries which include the three most recent days will depict different results between Firebase Analytics and Google BigQuery.
• If a query runs out of allocated resources, make sure that the ORDER BY clause has been removed since it is a pretty expensive operation and cannot be processed simultaneously.

Other than general best practices, there are multiple Google Firebase products available that can be used for best practice exports and configuration of data from Google Firebase Analytics to BigQuery. You can use the following Google Firebase products for best practices and the optimum design:

• Analytics
• Cloud Messaging
• Crashlytics
• Performance Monitoring
• Predictions
• A/B Testing

Conclusion

Thus, the robust use of Firebase Analytics with Google BigQuery can present several interesting features for your Data Analytics machinery. This article talks about the two methods you can use to establish Firebase Analytics BigQuery Integration in great detail after exploring the basics of the two. The article wraps up with the best practices and steps you can implement to export data in Firebase Analytics BigQuery Integration.

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Tell us about your experience of setting up Firebase Analytics BigQuery Integration. Share your thoughts in the comments section below!