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Begin by accessing the Delighted API to extract the necessary data. You'll need to authenticate using an API key. Use a script (e.g., in Python) to send a GET request to the appropriate Delighted API endpoint to retrieve your data in JSON format.
Once you have the JSON data from Delighted, parse it within your script. This involves converting the JSON response into a structured format like a list of dictionaries, where each dictionary represents a record that you want to store in ClickHouse.
Analyze the structure of your data and transform it to match the schema of your ClickHouse table. This might involve renaming fields, changing data types, or organizing nested JSON data into a flattened structure. Ensure that your data types match those defined in your ClickHouse schema.
Before importing data, ensure that your ClickHouse database and table are set up correctly. Use the ClickHouse command-line client or a SQL interface to create a table with the appropriate schema to match your transformed data.
Use the ClickHouse HTTP interface to insert data directly from your script. Construct an HTTP POST request to the ClickHouse server with your data in a format that ClickHouse expects, such as CSV or TabSeparated format. Ensure each record is formatted correctly and matches the ClickHouse table schema.
After inserting data into ClickHouse, perform a series of validation checks to ensure data integrity. Query the ClickHouse table to verify that the data count and sample entries match those from Delighted. Check for data type mismatches or any anomalies.
Once you've successfully transferred and validated the data, automate the entire process using a scheduling tool like cron (on Unix systems) or Task Scheduler (on Windows). This will allow for regular data transfers without manual intervention, ensuring that your ClickHouse database remains up-to-date with Delighted data.
By following these steps, you can efficiently move data from Delighted to ClickHouse while maintaining control over the process without relying on third-party connectors.
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:
