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Begin by exporting the data from Pendo. Log into your Pendo account and navigate to the analytics section. Use Pendo's export functionality to download the data you need. Pendo offers CSV export options, which you can use to download your data in a structured format. Make sure to format the data in a way that is compatible with BigQuery's requirements.
Once the data is exported, the next step is to prepare it for import into BigQuery. Ensure the CSV files are cleaned and properly formatted. Check for any inconsistencies or errors in the data that might cause issues during the upload process. Correct any malformed data entries and ensure that the data types in your CSV match the schema you plan to use in BigQuery.
If you haven�t already, set up a Google Cloud Platform (GCP) project. Go to the Google Cloud Console, create a new project, and enable billing if necessary. This project will host your BigQuery dataset and tables. Ensure that you have the necessary permissions to create datasets and tables within BigQuery.
In your GCP project, navigate to the BigQuery section. Create a new dataset where you will store your data. Within the dataset, create tables that match the structure of your CSV files. Define the schema for each table, specifying the correct data types for each column based on the data you exported from Pendo.
Before uploading your data to BigQuery, you need to store your CSV files in Google Cloud Storage (GCS). Open the GCS console and create a new bucket if necessary. Upload your CSV files to this bucket. GCS serves as an intermediary storage location that allows BigQuery to access your data for importing.
With your CSV files in GCS, you can now load them into BigQuery. In the BigQuery console, use the "Create Table" option and select "Google Cloud Storage" as the source. Provide the URI of your GCS bucket and files. Ensure you select the correct file format (CSV) and specify the schema if not already done. Execute the load job to import your data into BigQuery.
After loading the data, verify its integrity in BigQuery. Run queries to check that the data has been imported correctly and that there are no discrepancies. Compare a sample of the data in BigQuery with your original Pendo export to ensure accuracy. Fix any issues that arise, such as data type mismatches or missing data, by adjusting your import process and reloading the data if necessary.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Pendo is a product experience platform that enables marketers to deliver personalized in-app experiences and gather valuable customer insights. With Pendo, marketers can create targeted campaigns, walkthroughs, and product tours directly within their applications. This allows for contextual, relevant messaging that enhances user onboarding and adoption. Pendo also provides robust analytics and feedback tools, giving marketers visibility into feature usage, user journeys, and sentiment. By understanding how customers interact with their products, marketers can optimize experiences, drive engagement, and ultimately improve conversions and retention. Pendo's integrations with popular marketing automation and CRM systems streamline data sharing and enable coordinated cross-channel campaigns.
Pendo's API provides access to a wide range of data related to user behavior and product usage. The following are the categories of data that can be accessed through Pendo's API:
1. User data: This includes information about individual users such as their name, email address, and user ID.
2. Product data: This includes information about the product being used, such as the product name, version, and features.
3. Usage data: This includes information about how users are interacting with the product, such as which features they are using, how often they are using them, and how long they are spending on each feature.
4. Engagement data: This includes information about how engaged users are with the product, such as how frequently they are logging in, how often they are completing certain actions, and how long they are spending in the product.
5. Feedback data: This includes information about user feedback, such as ratings, reviews, and comments.
6. Conversion data: This includes information about how users are converting, such as how many users are signing up, how many are upgrading to paid plans, and how many are churning.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
