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Begin by familiarizing yourself with the Delighted API documentation. Understand the endpoints available for fetching the survey responses or other data you need. Make sure you have the necessary API key for authentication.
Set up a local development environment where you can write and test your code. Install necessary tools and libraries, such as Python or Node.js, which will allow you to make HTTP requests to the Delighted API.
Write a script using a language of your choice to make HTTP GET requests to the Delighted API. This script should authenticate using your API key and fetch the data you need. Ensure that you handle pagination if the data is large, and parse the JSON response to extract relevant information.
Install Apache Kafka on your local machine or server. Configure the necessary settings, such as `zookeeper` and `server` properties. Ensure that Kafka is running by starting the Zookeeper and Kafka server.
Use the Kafka command-line tools to create a new topic where the Delighted data will be published. For instance, run a command like `kafka-topics.sh --create --topic delighted-data --bootstrap-server localhost:9092 --partitions 1 --replication-factor 1`.
Extend your existing script to include Kafka producer functionality. Use a Kafka client library suitable for your chosen programming language to publish the data fetched from Delighted to the Kafka topic. Ensure that data is serialized properly before sending it to Kafka.
Once your script is working correctly, automate its execution using a scheduling tool like cron (for Unix-based systems) or Task Scheduler (for Windows). This ensures that data is continuously moved from Delighted to Kafka at regular intervals, keeping your Kafka topic updated with fresh data.
By following these steps, you can effectively move data from Delighted to Kafka 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.
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?
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