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Begin by exporting your data from Pendo. Pendo allows you to export data in CSV format through its user interface. Navigate to the Pendo dashboard, select the data you wish to export (such as user data, event data, etc.), and download it as a CSV file. Ensure that you have the necessary permissions to access and export the data from Pendo.
Log into your AWS Management Console and create an S3 bucket where you will store the exported Pendo data. Go to the S3 service, click on "Create bucket," and follow the prompts. Choose a unique name for your bucket and select appropriate options for region and access permissions. This bucket will serve as the storage location for your data lake.
Upload the exported CSV files to your newly created S3 bucket. You can do this manually through the AWS S3 console by clicking "Upload" within your bucket and selecting the files from your local machine. Alternatively, use the AWS CLI for a more automated approach:
```bash
aws s3 cp /path/to/local/file.csv s3://your-bucket-name/
```
AWS Glue is a service that prepares your data for analysis. Set up an AWS Glue Data Catalog to catalog your S3 data. In the AWS Glue console, create a new database. Then, define a Crawler to scan your S3 bucket, identify the CSV files, and populate the Glue Data Catalog with table definitions. This step is crucial for organizing your data for querying.
With your data cataloged, create an AWS Glue ETL (Extract, Transform, Load) job to transform the CSV data into a format suitable for analysis, such as Parquet or ORC. In the Glue console, configure a new job with the source as your S3 bucket and the target as another S3 bucket or the same bucket in a different folder. Define transformations as needed to clean and prepare the data.
Execute the AWS Glue job to process and load the transformed data back into your S3 bucket. This processed data now forms your AWS Data Lake, organized in a manner that facilitates efficient querying and analysis. Ensure that the output location in S3 is structured to support your query engine, like AWS Athena.
Finally, use AWS Athena to query your data lake. Athena allows you to run SQL queries directly on your S3 data. In the Athena console, point to your AWS Glue Data Catalog, select the database and tables you created, and start querying. This step enables you to gain insights from your Pendo data efficiently.
By following these steps, you can successfully move and transform your data from Pendo into an AWS Data Lake without relying on third-party connectors or integrations.
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:
