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First, log into your FullStory account and navigate to the data export section. FullStory allows you to export session data into CSV or JSON formats. Choose the format that suits your needs and export the data to your local machine. Make sure to select the appropriate date range and data fields required for your analysis.
Once you have the exported data, review it to ensure all necessary fields are present and correctly formatted. If you exported a CSV, you can use a spreadsheet tool like Excel or Google Sheets to inspect and clean the data. If it's JSON, check for any structural inconsistencies and ensure all records are complete and valid.
Download and install Typesense on your local machine. Typesense provides detailed documentation for installation on different platforms. Once installed, start the Typesense server. This will allow you to interact with Typesense using its API locally.
Define a schema for your data in Typesense. This involves creating a collection that matches the structure of your FullStory data. You can do this by sending a POST request to the Typesense API to create a new collection. Specify the fields and their data types, ensuring it aligns with the data you're importing.
Develop a script using a programming language like Python to read the exported FullStory data and convert it into a format suitable for Typesense. This script should parse through the CSV or JSON file, map the data fields to the Typesense schema, and prepare the data for batch import.
Use the Typesense API to import your prepared data into the newly created collection. You can do this by batching the data into manageable chunks and sending POST requests to the `/documents/import` endpoint of the Typesense API. Ensure the API key and other necessary authentication details are correctly configured in your script.
After the import process, verify that all data has been successfully transferred and indexed in Typesense. You can do this by querying the Typesense collection and checking for record counts and sample data correctness. This step ensures the data integrity and that the import process was successful.
By following these steps, you will be able to move data from FullStory to Typesense 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.
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:
