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Begin by logging into your Qualaroo account. Navigate to the dashboard and locate the option to export data. Typically, this can be found under survey results or data management. Choose the CSV or Excel format to export your data, as these are easily manageable file types for manual data handling.
Open the exported file in a spreadsheet application like Microsoft Excel or Google Sheets. Review the data to ensure it is clean and organized. Check for any inconsistencies or errors and correct them. Ensure that all necessary fields match the data structure required by Convex.
Before transferring the data, understand the data structure required by Convex. This may involve examining Convex documentation or your existing data setup in Convex. Identify the fields and data types that your data needs to conform to for successful import.
Create a mapping document or spreadsheet that aligns the Qualaroo data fields with the Convex data structure. This will help ensure that each piece of data is placed correctly during the import process. Make sure to account for any necessary transformations, such as data type conversions or renaming fields.
Using your mapping document, adjust the data in your spreadsheet to match the Convex data structure. This may involve renaming columns, reordering fields, or converting data types. Ensure that the data is thoroughly reviewed and validated against the Convex requirements to prevent import errors.
Log into your Convex account and navigate to the data import section. Follow the platform's instructions for manual data import, which usually involves uploading your prepared CSV or Excel file. Carefully monitor the import process for any errors or warnings that may arise and address them accordingly.
Once the import is complete, verify that the data has been transferred correctly. Check various entries to ensure that the data fields are accurate and that no data has been lost or corrupted during the transfer. Conduct any necessary reconciliation to confirm that the data in Convex matches your original Qualaroo dataset.
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.
Qualaroo is a SaaS product that helps companies gather customer insights to grow their business. Koala's mission is to help companies understand the reasons behind their customers' and prospects' decisions. Understanding why leads to better business results like increasing sales, improving web conversion rates and experience, increasing product engagement, reducing churn, and more. Qualaroo makes it possible to intelligently target interactions by time on page, pages visited, number of site visits, source citations, or any internal data.
Qualaroo's API provides access to various types of data related to user feedback and behavior. The categories of data that can be accessed through Qualaroo's API are:
1. Survey data: This includes data related to the surveys created using Qualaroo, such as survey responses, completion rates, and survey questions.
2. User behavior data: This includes data related to user behavior on a website or application, such as page views, clicks, and time spent on a page.
3. User feedback data: This includes data related to user feedback, such as comments, ratings, and suggestions.
4. Demographic data: This includes data related to user demographics, such as age, gender, location, and occupation.
5. Conversion data: This includes data related to user conversions, such as conversion rates, conversion funnels, and revenue generated.
6. A/B testing data: This includes data related to A/B testing, such as test results, variations, and statistical significance.
Overall, Qualaroo's API provides access to a wide range of data that can help businesses better understand their users and improve their products and services.
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?
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