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Begin by logging into your Delighted account. Navigate to the data export section, usually found under settings or reports. Choose the format you want (CSV is commonly used) and export the data. This will serve as your raw dataset to be imported into Typesense.
Once exported, open the dataset in a spreadsheet tool like Microsoft Excel or Google Sheets. Review the data for any inconsistencies or errors. Make sure the data is clean and structured properly with relevant fields that match your intended schema in Typesense.
If you haven't already, install Typesense on your local machine or server. You can do this by following the installation instructions on the [Typesense documentation](https://typesense.org/docs/0.24.0/guide/install-typesense.html). Ensure that you have all the necessary dependencies installed.
Define the schema for the dataset you intend to import. This involves setting up a collection in Typesense with fields that correspond to the columns in your Delighted data export. Use the Typesense REST API to create this schema, specifying attributes like name, type, and whether each field should be indexed or faceted.
Convert your cleaned and structured CSV data into JSON format. This can be done using a script in a programming language like Python or using an online CSV to JSON converter. Ensure the JSON structure aligns with the schema defined in Typesense, with keys matching the field names.
Use the Typesense API to import your JSON data. This involves sending a POST request to the Typesense server with your JSON data payload. You can use tools like `curl` or a script written in Python using `requests` library to facilitate this process. Ensure you handle any errors or issues that arise during the import process.
Once the data is imported, perform checks to verify its integrity. Use Typesense's search capabilities to test queries and ensure the data is searchable and returns expected results. Check for any discrepancies and resolve any issues by adjusting the schema or re-importing data if necessary.
By following these steps, you can successfully move data from Delighted to Typesense without using 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.
Delighted assists businesses connect with their customers learning, improving, and delighting.It is well known for delivering some of the most innovative functionality for customer experience management. Delighted is completely the self-serve experience management platform of choice for the worldwide top brands. It helps to collect and analyze survey feedback through Delighted. Get set up in minutes, no technical knowledge needed. Delight helps to build long-lasting relationships and deliver great service experience.
Delighted's API provides access to various types of data related to customer feedback and satisfaction. The categories of data that can be accessed through Delighted's API are:
1. Survey Responses: This includes all the responses received from customers through Delighted's surveys. It includes both quantitative and qualitative data.
2. Metrics: This includes various metrics related to customer satisfaction, such as Net Promoter Score (NPS), Customer Satisfaction Score (CSAT), and Customer Effort Score (CES).
3. Trends: This includes trends related to customer feedback and satisfaction over time. It helps businesses to identify patterns and make data-driven decisions.
4. Segmentation: This includes data related to customer segments, such as demographics, location, and behavior. It helps businesses to understand their customers better and tailor their offerings accordingly.
5. Integrations: Delighted's API also provides access to data from various integrations, such as Salesforce, HubSpot, and Slack. It helps businesses to streamline their workflows and improve their customer experience. Overall, Delighted's API provides a comprehensive set of data that businesses can use to measure and improve their customer satisfaction.
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:
