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Start by logging into your SurveySparrow account. Navigate to the survey whose data you want to export. Use the export option to download the survey responses. Choose a suitable format, such as CSV or JSON, which can be easily processed and transformed for Typesense.
Open the exported file using a spreadsheet application or a text editor, depending on the format. Review the data structure and make note of the fields you want to index in Typesense. Ensure consistency and clean any unnecessary data or formatting issues.
If your exported data is not already in JSON format, you will need to transform it. You can use a scripting language like Python to read your CSV or existing JSON data and convert it into a JSON format that Typesense can understand. Ensure that each survey response is represented as a JSON object.
Install and configure Typesense on your server if it is not already set up. Follow the official documentation to set up a Typesense server, ensuring it's accessible and properly configured to accept data.
Before importing data, define a collection schema in Typesense that matches the structure of your survey data. Use the Typesense Dashboard or API to create a new collection. Specify the fields, their data types, and the fields you want to be indexed for search.
Use the Typesense API to import your transformed JSON data. Write a script or use a command-line tool to send HTTP POST requests to the Typesense server. Make sure to batch your requests if you're importing a large dataset to avoid overwhelming the server.
Once the data import is complete, verify the data in Typesense by querying the collection. Use the Typesense Dashboard or API to perform search queries and ensure that the data is correctly indexed and searchable. Check for any errors and correct them by re-transforming and re-importing data if necessary.
By following these steps, you can successfully move data from SurveySparrow 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.
SurveySparrow is an online survey tool which permits users to create and distribute customer surveys through multiple channels, along with evaluate responses and it is also an experience management platform on a mission to assists businesses refine experiences end to end Conversational Experience Management Platform that helps you get a 40% better response rate. SurveySparrow supports you measure employee motivation by using surveys specially made for them. One can easily measure how engaged they are and their job satisfaction.
SurveySparrow's API provides access to a wide range of data related to surveys and responses. The following are the categories of data that can be accessed through SurveySparrow's API:
1. Survey data: This includes information about the surveys created on the platform, such as survey title, description, and status.
2. Response data: This includes information about the responses received for each survey, such as response ID, respondent email, and response timestamp.
3. Question data: This includes information about the questions asked in each survey, such as question type, question text, and answer options.
4. User data: This includes information about the users who have access to the surveys, such as user ID, email, and role.
5. Analytics data: This includes information about the survey performance, such as response rate, completion rate, and average time taken to complete the survey.
6. Integration data: This includes information about the integrations used with SurveySparrow, such as the API key and endpoint URL.
Overall, SurveySparrow's API provides comprehensive access to all the data related to surveys and responses, enabling users to analyze and utilize the data for various purposes.
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.
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