Understanding how customers interact with a website or application helps businesses to improve the user experience and eliminate bottlenecks. Poor user experience on a website or app leads to decreased customer retention rate and revenue. Pendo is a User Management and Product Analytics platform that helps businesses to understand their customers, take feedback, and analyze their actions. 

Companies will improve two core business functions: User Onboarding and Behavioral Analytics, by adding Pendo to their tech stack. Although Pendo has analytics capabilities, connecting it to a Data Warehouse can help businesses to combine data from multiple sources and perform in-depth analyses. Google BigQuery is one such fully managed data warehouse that helps businesses to manage and analyze their data. Users can connect Pendo to BigQuery to offer a better product experience and gain new insights into their business. 

In this blog, you will learn about the features of Pendo and BigQuery. You will also learn how to connect Pendo to BigQuery.


  • Basic understanding of Google Cloud Console

What is Pendo?

Pendo to BigQuery: Pendo logo
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Pendo is a Product-Analytics App that helps software companies deliver excellent digital experiences through features such as User Onboarding, in-app support, Product Engagement, Feedback Collection, and Revenue Growth. Companies can embed these tools in their products/apps and track user engagement metrics to reduce churn, improve navigation, eliminate bottlenecks, etc. Teams across organizations like marketing, sales, customer success, UX, etc., can use Pendo Product Cloud to improve their services and products. It also helps to collect digital experiences on mobile devices, where you can track user activity in the Pendo app and get insights into how customers navigate through your app.

Pendo has a free plan that allows up to 1000 free monthly active users for one mobile and web app. There is no specific pricing, but there are three paid plans: Team, Pro, and Enterprise. The team plan comes with Analytics, In-app Guides, and API access. Pro users will also get Feedback, Roadmap, and one integration. For companies that want to leverage Pendo across their organization, the Enterprise plan offers up to three integration, security audit, installation on a dev environment, and  24/5 Live chat support. Companies will have to get in touch with Pendo’s Sales team to get pricing. 

Key Features of Pendo

  • Survey and polls: Teams can bring together quantitative app data and qualitative user feedback to understand the intent behind user actions. This feature helps businesses to capture feedback in-app to understand the context of user action. There can be targeted surveys, so businesses can segment their customers and take feedback from specific user groups. Other forms are app ratings and polls directly in their apps. 
  • Feature adoption analytics: Feature adoption is a dashboard widget in Pendo that offers insights into how many users use the application. It also details which area of your product or application is the most usable by the users. 
  • Incredible mobile experience: Pendo for mobile is a quick, codeless solution for Android, iOS, Xamarin, and React applications. Businesses can get mobile-specific insights and make better, data-informed decisions about how often customers upgrade applications or use the old version.
  • Personalized in-app guides: Pendo has in-app guides that help product teams to drive desired behavior, highlight new features, and provide support across devices. Users get help whenever they need it, simplifying the user experience and improving the overall product experience for product teams. You can design guides through a WYSIWYG editor, templates, or custom HTML/CSS. You can also have target messages based on customer usage. 

What is Google BigQuery?

Pendo to BigQuery: BigQuery logo
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Google BigQuery is a fully managed Data Warehouse that helps companies manage and analyze their data. It has various built-in features such as Machine Learning, Business Intelligence, Geospatial Analysis, etc. It runs on a serverless architecture that lets you use SQL queries to query terabytes in seconds and petabytes in minutes. Google BigQuery separates compute engine and storage options, providing flexibility to organizations with different compute and storage requirements. Google Cloud Console and the Google BigQuery command-line tool are part of the BigQuery interface, where developers can use client libraries to transform and manage data. 

There are different pricing for analysis, storage, and data ingestion. There is an on-demand and flat-rate pricing for analysis that has a free 1 TB tier per month.

Key Features of Google BigQuery

  • Multi-cloud Functionality: BigQuery is an analytics solution that offers data analytics solutions across multiple cloud platforms. The USP of BigQuery is that it provides a novel way of analyzing data in multiple clouds without costing an arm and a leg. This contrasts with other solutions, which have high egress costs to migrate data from other sources. BigQuery isn’t expensive because it separates compute and storage components. Users can only opt for compute resources and run queries where data is stored. 
  • Built-in ML Integration: BigQuery ML is used for designing and executing ML models in BigQuery with simple SQL queries. Before BigQuery ML was introduced, developers needed ML-specific knowledge and programming skills to build models. However, BigQuery eliminated the need for ML expertise for building ML models. Some models supported in BigQuery ML include Linear regression, Binary, Multiclass Logistic regression, and Deep Neural Network models. You can access BigQuery ML in the following four manners:
    • Google Cloud Console
    • BigQuery command-line-tool
    • BigQuery REST API
    • An external tool (for example, Jupyter)
  • Automated Data Transfer: You can automate the movement of data to BigQuery regularly. Analytics teams can easily schedule data movement without any code. One can also include data backfills to remove any outages or gaps during ingestion. 
  • Free access: Google offers a BigQuery sandbox where you can experience the cloud console and BigQuery without any commitment. You don’t have to create a billing account or even provide credit card details. Every application runs in a separate environment, and users can quickly upgrade to a full BigQuery experience. 
Explore These Methods to connect Pendo to BigQuery

Pendo is a product-analytics app that helps software firms provide outstanding digital experiences with features such as user onboarding, in-app assistance, product engagement, feedback gathering, and revenue growth. Google BigQuery is a fully managed data warehouse that aids in the management and analysis of data for businesses. It contains many built-in functions, including geospatial analysis, business intelligence, and machine learning. When you need to transfer data from Pendo to BigQuery, you can use the following methods to achieve this:

Method 1: Connect Pendo to BigQuery using Hevo

Hevo Data, an Automated Data Pipeline, provides you with a hassle-free solution to transfer the data from Pendo to BigQuery within minutes with an easy-to-use no-code interface. Hevo is fully managed and completely automates the process and also enriching the data and transforming it into an analysis-ready form without having to write a single line of code.

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Method 2: Manually Connect Pendo to BigQuery using API

This method would be time-consuming and somewhat tedious to implement. You will have to first manually export data from Pendo and then again import that data into BigQuery using Google Cloud Platform.

Methods to Connect Pendo to BigQuery

Method 1: Connect Pendo to BigQuery using Hevo

Hevo helps you directly transfer data from various sources such as Pendo, Business Intelligence tools, Data Warehouses, or a destination of your choice such as Google BigQuery in a completely hassle-free & automated manner. 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.

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The following steps can be implemented to connect Pendo to BigQuery using Hevo:

  • Configure Source: Connect Hevo Data with Pendo providing a unique name for your Pipeline, along with details such as  Region, Integration Key and Historical Sync Duration. 
Pendo to BigQuery: Pendo Source
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  • Configure Destination: Establish a connection to Google BigQuery by providing information about its credentials such as Destination Name, Authorized Account, Project ID and Dataset ID. 
Pendo to BigQuery: BigQuery Destination
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Here are more reasons to try Hevo:

  • Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
  • Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
  • Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.
  • Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
  • Scalable Infrastructure: Hevo has in-built integrations for 100+ sources that can help you scale your data infrastructure as required.
  • Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within Data Pipelines.
  • Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

Method 2: Manually Connect Pendo to BigQuery using API

Data collected from Pendo API or Pendo agent can be pushed into BigQuery. Data from Pendo is available in a rolling 90-day window, and a detailed schema is only provided on request. Pendo only offers read-only direct access to BigQuery but will need a third-party tool for analysis access. 

Pendo provides the following options for integrations:

  • The Pendo Agent API: This allows programmers to interact with Pendo’s Agent to collect user interactions. 
  • The Pendo v1 API: A RESTful API to update, reset, and query Pendo data by Guide resources or running Aggregation queries. 
  • The Pendo Resource Center Integration Kit: It’s an integration that allows third-party developers to design applications that Pendo customers can sell to their customers. 

With Pendo, companies can capture critical event data at a granular level and make it available for segmentation, analytics, and guide targeting.

Step 1: Export Data from Pendo

  • Step A: Create a report
Pendo to BigQuery: Data Explorer
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Go to the Data Explorer section and create a fresh report. You will have to add an object and data source. By default, Data Explorer looks for only the last 30 days, but you can change the time period. 

  • Step B: Build Your Query
Pendo to BigQuery: Build Query
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You can choose up to two data sources and select various measurements for each data source. There are various measurements available such as average, median, total, number of visitors/accounts, percent of visitors/accounts, and total. 

You can choose Select App for each data source and Date Range as well. Users also have an option to compare date ranges, select segment(s), and group sections by metadata. 

  • Step C: Select Date Range
Pendo to BigQuery: Data Range
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  • Step D: Save and Download the report
Pendo to BigQuery: New Report
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You can save the new report and download the CSV file. 

Step 2: Upload Data to BigQuery

You can update data to Google BigQuery, and the file will be loaded to a new table. You’ll need IAM permissions to load data to BigQuery. You can upload data through a cloud console or cloud storage. Furthermore, you can upload data in Google BigQuery via Google Cloud Platform by following the below steps:

  • Step A: Navigate to the BigQuery page in the Google Cloud Platform.
Pendo to BigQuery: GCP BigQuery
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  • Step B: Select Dataset in the Explorer pane.
  • Step C: Click on the Create table.
  • Step D: Select CSV for the file format in the Source section, and your Pendo data will be uploaded to BigQuery. 
Pendo to BigQuery: Create Table
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Limitations of Manually Connecting Pendo to BigQuery

Pendo provides insights into how your customers are using your products/services, how they feel about it, etc. However, there are various limitations when it comes to integrating Pendo with BigQuery. Pendo only provides read-only direct access with a 90-day holding period. This means you can’t get real-time data. But, companies can use a no/low code ETL tool like Hevo Data to connect Pendo to BigQuery without any hassle. They don’t need to create pipelines or write scripts with Hevo Data. 


In this article, you got a glimpse of how to connect Pendo to BigQuery after a brief introduction to the salient features, and use cases. The methods talked about in this article are using API and an automated method. The process can be a bit difficult for beginners. Moreover, you will have to update the data each and every time it is updated and this is where Hevo saves the day!

Visit our Website to Explore Hevo

Hevo Data provides its users with a simpler platform for integrating data from 100+ sources for Analysis. It is a No-code Data Pipeline that can help you combine data from multiple sources like Pendo. You can use it to transfer data from multiple data sources into your Data Warehouses, Database, or a destination of your choice such as BigQuery. It provides you with a consistent and reliable solution to managing data in real-time, ensuring that you always have Analysis-ready data in your desired destination.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.

Share your experience of learning about Pendo to BigQuery! Let us know in the comments section below!

Osheen Jain
Freelance Technical Content Writer, Hevo Data

Osheen has extensive experience in freelance writing within the data industry and is passionate about simplifying the complexities of data integration and data analysis through informative content for those delving deeper into these subjects.

No-code Data Pipeline For Google BigQuery