Every organization relies on data they collect from different sources to make informed decisions. However, properly analyzing first-party data can help you gain better insights about your costumes than information from other sources. This is where organizations, especially SaaS-based companies, are using Pendo to understand the customer journey and make better decisions for business growth.

Pendo not only helps organizations collect but also analyzes data to simplify decision-making. Although it has all the necessary analytics capabilities a SaaS company would need, you can connect Pendo to Snowflake for centralizing data, generating quick reports, and leveraging machine learning algorithms for in-depth analysis.

In this article, you will be discussing Pendo and Snowflake and how to establish a connection between Pendo to Snowflake.


Understand analytics and knowledge in cloud data warehouses.

What is Pendo?

Pendo to Snowflake: Pendo Logo | Hevo Data
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Pendo is a product-analytics application designed to assist software firms in creating solutions that are appealing to customers. Similar to Google Analytics, Pendo provides a javascript code that you can include in your mobile applications and websites for tracking user engagement. But, that is where the similarity ends; Pendo is built from the ground up to cater to SaaS companies to understand their user engagement.

With Pendo, you can understand the user journey, find shortcomings in your products, and more. Apart from analytics, Pendo offers an in-app guide to help introduce new features, onboard new users, and more. This in-app guide can also help you analyze whether users liked your new feature based on the time they take to follow along with the steps of the in-app guide while introducing new updates.

With Pendo, customers can also implement feedback polls and surveys to improve the user experience, allowing organizations to combine quantitative and qualitative data. When users fill out feedback forms on the app, Pendo generates Net Promoter Scores (NPS) to determine the cause of their dissatisfaction.

Key Features of Pendo

  • Usage: Pendo Analytics is ideal for companies who need usage information for their product and rely on consumer behavior for product creation and development. It allows for easy tagging of pages and features, and the API enables customized integrations. With such multidimensional features, Pendo can provide you with advanced and extensive data on all of your KPIs.
  • Feedback: Pendo’s premium plans include a feedback function that allows you to collect user product enhancement requests via various channels. Users can fill in the form details like the problems they witness while using the product and possible solutions to address the challenges. This not only simplifies feedback collection but also improves the visibility of the challenges your teams have to mitigate to ensure you avoid churn.  
  • In-depth Analytics and Reporting: Pendo focuses its efforts more on the interaction between the user and the software to provide you with enough data on which you can do in-depth analytics to understand your customer’s behavior. You can evaluate your product by observing your product performance and interaction with users using Pendo Analytics and prebuilt reports.

What is Snowflake? 

Pendo to Snowflake: Snowflake Logo | Hevo Data
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Developed in 2012, Snowflake is a fully managed Software as a Service that offers a single platform for data lakes, data engineering, data warehouse, and more. Snowflake uses massively parallel processing to distribute the computation to help analyze a plethora of data quickly. Apart from offering superior computation, it has more flexibility in terms of the host for your data. With Snowflake, you can store data on any cloud service like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform. 

Snowflake is a cloud-native data warehouse that addresses many of the issues that plague older hardware-based data warehouses, including limited scalability, data transformation issues, and delays or failures caused by high query volumes.

Snowflake’s architecture and data-sharing capabilities, however, set it apart. Customers can use and pay for storage and computation separately thanks to the Snowflake architecture, which allows storage and computation to scale independently. Furthermore, the sharing functionality enables organizations to share governed and secure data in real time.

Snowflake is a fully managed service that is easy to use and can run an almost infinite number of concurrent workloads. Data warehousing, data lakes, data engineering, data science, data application development, and securely sharing and consuming shared data are all possible with Snowflake

As Snowflake is a fully managed platform, businesses do not need to select, control or configure hardware and software. Therefore, Snowflake is a good choice for businesses that do not want to dedicate resources for maintenance, configuration, setup, and more. Snowflake provides a flexible pricing model that allows businesses to pay independently for storage and computation. Businesses can also select the on-demand storage where they can pay a monthly upfront payment.

Key Features of Snowflake

  • Scalability: The Snowflakes multi-cluster shared data architecture separates the computation and storage resources. This method allows customers to scale up resources when significant quantities of data need to be loaded quickly, then scale back down when the operation is complete, without disrupting service. Customers can start with a very modest virtual warehouse and grow as required. Snowflake’s auto-scaling and auto-suspend capabilities ensure that you need minimum administration. When clusters are idle for a specified amount of time, auto-suspend pauses the virtual warehouse. These two principles provide flexibility, performance optimization, and cost control.
  • Concurrency and Workload Separation: Users and processes compete for resources in a typical data warehouse system, resulting in concurrency concerns. However, concurrency is no longer a concern because of Snowflake’s multi-cluster virtual warehouse design. Inquiries from one virtual warehouse will never impact other virtual warehouse queries. The ability to conduct ETL/ELT processing, data analysis activities and reports without competing for resources is enabled by having separate virtual warehouses for users and applications.
  • Integration: Although you can use the web-based user interface to carry out necessary tasks, Snowflake also offers other methods like SnowSQL, ODBC, and JDBC connection, native connectors for Python and Spark, and more to interact and manage information in Snowflake.
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If yours is anything like the 1000+ data-driven companies that use Hevo, more than 70% of the business apps you use are SaaS applications. Integrating the data from these sources in a timely way is crucial to fuel analytics and the decisions that are taken from it. But given how fast API endpoints etc., can change, creating and managing these pipelines can be a soul-sucking exercise.

Hevo Data’s no-code data pipeline platform lets you connect over 150+ sources in a matter of minutes to deliver data in near real-time to your warehouse. What’s more, the in-built transformation capabilities and the intuitive UI means even non-engineers can set up pipelines and achieve analytics-ready data in minutes. 

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How to Replicate Data from Pendo to Snowflake?

Method 1: Using Custom Code to Move Data from Pendo to Snowflake

To manage product analytics with your other essential data sources, push your Pendo product data to your Snowflake warehouse. With Pendo and Snowflake connection, your company will get a complete picture of all your customers and find opportunities to enhance your products.

Steps to Export Data from Pendo

All Pendo users other than the Read-Only can create and save reports under the Data Explorer section. The data will have details on the status, tags, scores, description, etc., in the form of a CSV file. Follow these instructions to export your data correctly from the Pendo application: 

  • Create a report: Navigate to the behavior section under the Data Explore pane.
Pendo to Snowflake: create a report | Hevo Data
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To create a new report for simple analysis in Pendo, you will need to provide a data source and an object. However, for advanced analysis, you will have to give a minimum of two data sources and ten objects. 

  • Construct a command to execute: To create the desirable dataset for your analysis, you will have to construct a query. You can select up to two data sources and measurements for both.
Pendo to Snowflake: construct a command to execute | Hevo Data
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  • Choose the date range from the calendar:
Pendo to Snowflake: choose a date range from the calendar | Hevo Data
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  • Execute the query you created:
Pendo to Snowflake: execute query | Hevo Data
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  • Save and download the report: Under the Visualization pane, navigate to the breakdown table. Click the download button to export your Pendo data as a CSV file.
Pendo to Snowflake: save and download the report | Hevo Data
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Steps to Import Data to Snowflake

After exporting data from Pendo, you must import it to the Snowflake account. Follow the below steps to import data into Snowflake.

  • Click on Data and click on Databases on the Snowflake Web homepage.
  • Select the table to which you want to upload the data or click the load button after selecting the table name.
  • Choose your preferred data warehouse.
  • Click on the Load files from your computer and then click on the Select Files button.
  • Click the Open button and select the data file you downloaded from Invoiced.
  • Click on the Next button, and then the Load Data wizard will occupy the table with the data.

What are the Limitations of Manually Connecting Pendo to Snowflake?

Connecting Pendo with Snowflake manually might seem easy, but it can be challenging if you work with big data. Since Pendo can fetch numerous data points about your customers, you will most probably have a colossal amount of data to handle.

In such cases, you cannot use the manual process because it can lead to data quality problems like data inconsistency, data integrity, and more. On the other hand, you can embrace API to connect Pendo to Snowflake. But, you might require a dedicated technical team to write custom scripts and monitor the data pipelines. To mitigate such issues, you can use no-code/low-code ETL solution providers like Hevo Data. 

Method 2: Using Hevo Data to Set Up Pendo to Snowflake

Pendo to Snowflake_Hevo Data
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Hevo Data provides an Automated No-code Data Pipeline that helps you move your Pendo swiftly to Snowflake. Hevo is fully-managed and completely automates the process of not only loading data from your 150+ Sources(including 40+ free sources)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.

Using Hevo Data, you can connect Pendo to Snowflake in the following 2 steps:

  • Step 1: Configure Pendo as the Source in your Pipeline by following the steps below:
    • Step 1.1: In the Asset Palette, select PIPELINES.
    • Step 1.2: In the Pipelines List View, click + CREATE.
    • Step 1.3: Select Pendo on the Select Source Type page.
    • Step 1.4: Set the following in the Configure your Pendo Source page:
      • Pipeline Name: A name for the Pipeline that is unique and does not exceed 255 characters. 
      • Region: Your Pendo account’s subscription area. Region is set to the US by default.
      • Integration Key: A private key with v1 API read/write access to your Pendo data. This key is unique to your account’s subscription region.
      • Historical Sync Duration: The time it takes for historical data to be synced with the Destination. 3 Months is the default value.
Pendo to Snowflake: Configure Pendo as source | Hevo Data
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  • Step 1.5: TEST & CONTINUE is the button to click.
  • Step 1.6: Set up the Destination and configure the data ingestion.
  • Step 2: To set up Snowflake as a destination in Hevo, follow these steps:
    • Step 2.1: In the Asset Palette, select DESTINATIONS.
    • Step 2.2: In the Destinations List View, click + CREATE.
    • Step 2.3: Select Snowflake from the Add Destination page.
    • Step 2.4: Set the following parameters on the Configure your Snowflake Destination page:
      • Destination Name: A unique name for your Destination.
      • Snowflake Account URL: This is the account URL that you retrieved.
      • Database User: The Hevo user that you created in the database. In the Snowflake database, this user has a non-administrative role.
      • Database Password: The password of the user.
      • Database Name: The name of the Destination database where data will be loaded.
      • Database Schema: The name of the Destination database schema. Default value: public.
      • Warehouse: SQL queries and DML operations are performed in the Snowflake warehouse associated with your database.
Pendo to Snowflake: configure Snowflake as destination | Hevo Data
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  • Step 2.5: Click Test Connection to test connectivity with the Snowflake warehouse.
  • Step 2.6: Once the test is successful, click SAVE DESTINATION.

Here are more reasons to try Hevo Data:

  • Smooth Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to your schema in the desired Data Warehouse.
  • Exceptional Data Transformations: Best-in-class & Native Support for Complex Data Transformation at fingertips. Code & No-code Flexibility is designed for everyone.
  • Quick Setup: Hevo with its automated features, can be set up in minimal time. Moreover, with its simple and interactive UI, it is extremely easy for new customers to work on and perform operations.
  • Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
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What Can You Achieve by Replicating Data from Pendo to Snowflake?

Here’s a little something for the data analyst on your team. We’ve mentioned a few core insights you could get by replicating data from Trello to Redshift. Does your use case make the list?

  • Which features of the product are most popular in a country?
  • Your power users are majorly from which location?
  • How to make your users happier and win them over?
  • What are the custom retention trends over a period of time?
  • What is the trend of a particular feature adoption with time?


Pendo is a great analytics tool to analyze customer behavior on your SaaS solutions and helps make crucial decisions with its visualization feature. However, the analytics and reporting features of Pendo can limit you to only specific insights. While such features are suitable for obtaining an overview of your products, obtaining in-depth insights into user engagement would require you to perform machine learning algorithms. As a result, you should migrate the data from Pendo to Snowflake and democratize data to generate unique insights which can help in the growth of your company.

Visit our Website to Explore Hevo

Hevo Data offers a No-code Data Pipeline that can automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Marketing, Customer Management, etc.

This platform allows you to transfer data from 150+ sources (including 40+ Free Sources) such as Pendo and Cloud-based Data Warehouses like Snowflake, Google BigQuery, etc. It will provide you with a hassle-free experience and make your work life much easier.

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.

Freelance Technical Content Writer, Hevo Data

Pranay is curious about topics related to data science at heart with a passion for data, software architecture, and writing technical content. He is passionate about solving business problems through content tailored to data teams.

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