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First, log in to your Retently account and navigate to the data or reports section where your desired data is stored. Utilize the built-in export feature to download your data in a common file format such as CSV or JSON. Ensure you have all the necessary fields required for your Typesense database.
Once you have exported your data, open the file using a spreadsheet application or a text editor. Review the data structure and clean it up if necessary, ensuring that there are no formatting issues and that each record is complete. It’s important to format your data so it is compatible with Typesense, typically in JSON format.
Install and run a Typesense server on your machine or a cloud instance. You can follow the Typesense documentation for installation instructions specific to your operating system. Ensure the server is running by accessing the Typesense server URL in your browser or using a command-line tool.
Define a schema for your data in Typesense. This involves specifying the index name, fields, and their data types, and which fields should be faceted or searchable. You can use Typesense's API to create an index with this schema by sending a POST request to the Typesense server with your schema details in JSON format.
If your data is not already in JSON format, convert it using a script or tool. For CSV files, you can write a simple script in Python or another language to read the CSV and output JSON objects, ensuring each object aligns with the schema you defined in Typesense.
Use the Typesense API to import the JSON data. This typically involves making a POST request to the `/collections/:collection/documents/import` endpoint of your Typesense server. You can use cURL, Postman, or a custom script to send the JSON data. Ensure that you handle any errors or confirmations returned by the API.
After importing, verify that the data has been successfully added to your Typesense index. You can do this by querying the Typesense server using the search API to check for a few sample records. Additionally, verify that the data types and values are correctly indexed and searchable as intended.
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.
Retently is a tool for measuring and increasing customer satisfaction and loyalty through Net Promoter Score surveys and collecting feedback and The tool is packed with various robust features to help you segment your audience, create custom polls, and collect multichannel polls. With Retently, businesses can collect customer feedback and analyze the results with advanced analytics and reports for corrective action. Retently's cloud-based platform is designed to help businesses track their Net Promoter Score, collect valuable customer reviews, and build customer loyalty by converting detractors into repeat customers.
Retently's API provides access to various types of data related to customer feedback and satisfaction. The categories of data that can be accessed through Retently's API include:
1. Customer feedback data: This includes data related to customer feedback, such as NPS scores, customer comments, and ratings.
2. Customer satisfaction data: This includes data related to customer satisfaction, such as customer satisfaction scores, customer loyalty, and customer retention rates.
3. Customer behavior data: This includes data related to customer behavior, such as customer purchase history, customer demographics, and customer preferences.
4. Campaign data: This includes data related to Retently's campaigns, such as campaign performance metrics, campaign engagement rates, and campaign conversion rates.
5. User data: This includes data related to Retently's users, such as user activity, user preferences, and user engagement.
Overall, Retently's API provides access to a wide range of data related to customer feedback and satisfaction, which can be used to improve customer experience and drive business growth.
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:
