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First, determine how to extract data from FullStory. FullStory provides an API that allows you to export session data. Familiarize yourself with their API documentation to understand how to authenticate and retrieve the data you need. Use a script or tool like Python with HTTP requests to programmatically pull data from FullStory.
Once data is extracted, it often comes in JSON format. Parse this data to ensure it's structured correctly for your needs. Use a programming language like Python to load the JSON and transform it into a structured format like CSV or Parquet, which is suitable for loading into AWS.
In your AWS Management Console, create an S3 bucket where the data will be stored. Choose a unique bucket name and configure permissions so only authorized users can access and modify the data. Make note of the bucket name and region, as you'll need this information for subsequent steps.
Use AWS CLI or SDKs to upload your structured data to the S3 bucket. With AWS CLI, you can use the `aws s3 cp` command to copy files from your local machine to the S3 bucket. Ensure you have the necessary permissions set up in IAM to perform this operation.
AWS Glue is used to catalog your data. Create a Glue Crawler in the AWS Management Console that points to your S3 bucket. Configure the crawler to detect the data format (e.g., CSV, Parquet) and extract metadata, making sure it updates the Glue Data Catalog with table definitions.
If your data needs further transformation before analysis, create an AWS Glue job. This job can be written in Python or Scala and will allow you to perform ETL (Extract, Transform, Load) operations to clean, enrich, or reformat your data as needed.
With your data cataloged in Glue, use Amazon Athena to query your data directly from S3. Athena allows you to perform SQL queries on your data without needing to move it to a database, providing a serverless way to analyze data stored in your data lake.
This guide outlines a straightforward process for moving and preparing data from FullStory to AWS Data Lake, leveraging AWS services for data storage, cataloging, and analysis.
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.
Fullstory is a digital experience analytics platform that helps businesses understand how users interact with their websites and applications. It captures every user interaction, including clicks, scrolls, and keystrokes, and provides insights into user behavior, preferences, and pain points. Fullstory's features include session replay, which allows businesses to watch recordings of user sessions to identify issues and opportunities for improvement, as well as heatmaps, funnels, and conversion analytics. The platform also integrates with other tools such as Google Analytics and Salesforce to provide a comprehensive view of user behavior across the entire customer journey. Overall, Fullstory helps businesses optimize their digital experiences to improve customer satisfaction and drive business growth.
Fullstory's API provides access to a wide range of data related to user behavior on a website or application. The following are the categories of data that can be accessed through Fullstory's API:
1. Session data: This includes information about user sessions, such as session ID, start and end time, and duration.
2. Page data: This includes data related to the pages that users visit, such as page URL, title, and referrer.
3. Event data: This includes data related to user interactions with the website or application, such as clicks, form submissions, and page scrolls.
4. User data: This includes data related to user attributes, such as user ID, email address, and location.
5. Device data: This includes data related to the devices that users are accessing the website or application from, such as device type, operating system, and browser.
6. Error data: This includes data related to errors that occur on the website or application, such as error messages and stack traces.
Overall, Fullstory's API provides a comprehensive set of data that can be used to gain insights into user behavior and improve the user experience.
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:
