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Start by reviewing Freshcaller's API documentation to understand the available endpoints and the data schema. Familiarize yourself with how to authenticate, what data can be extracted, and any rate limits that apply. This step is crucial as it sets the foundation for data extraction.
Log in to your Freshcaller account and navigate to the API settings section. Create an API key or token that will allow you to authenticate your requests. Ensure you have the necessary permissions to access the data you need to export.
Write a script or use a command-line tool like `curl` to make GET requests to the Freshcaller API endpoints you identified in Step 1. Capture the data you need, such as call records, in a structured format like JSON. Ensure you handle pagination if Freshcaller returns large datasets in multiple pages.
Once you have the data in JSON format, transform it to match the Elasticsearch indexing format. This might involve renaming keys, restructuring JSON objects, or data type conversions. The goal is to make the data ready for direct ingestion into Elasticsearch.
Install and configure an Elasticsearch instance where the data will reside. This involves setting up your Elasticsearch cluster, defining the index where Freshcaller data will be stored, and configuring any necessary mappings. Ensure Elasticsearch is running and accessible.
Use a script or tool to send HTTP POST or PUT requests to your Elasticsearch instance to index the transformed data. This can be done using Elasticsearch’s REST API. Ensure you handle batch indexing to efficiently load large volumes of data and monitor for any errors during the process.
Once the data is loaded, perform queries in Elasticsearch to ensure data integrity and availability. Check that all expected records are present and that they are correctly indexed. Use Elasticsearch's powerful query capabilities to validate that the data can be retrieved and analyzed as needed.
By following these steps, you can effectively move data from Freshcaller to Elasticsearch 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.
Setup a connection to your Freshcaller site in minutes, and select the Freshcaller collections you want to replicate.
Freshcaller's API provides access to a wide range of data related to call center operations. The following are the categories of data that can be accessed through Freshcaller's API:
1. Call data: This includes information about incoming and outgoing calls, call duration, call recordings, and call transcripts.
2. Agent data: This includes information about agents, such as their availability, status, and performance metrics.
3. Queue data: This includes information about call queues, such as the number of calls waiting, the average wait time, and the number of agents available.
4. IVR data: This includes information about Interactive Voice Response (IVR) systems, such as the number of calls handled by the IVR, the number of calls transferred to agents, and the success rate of the IVR.
5. Ticket data: This includes information about tickets created from calls, such as the status of the ticket, the agent assigned to the ticket, and the resolution time.
6. Analytics data: This includes information about call center performance metrics, such as call volume, call abandonment rate, and average handle time.
Overall, Freshcaller's API provides a comprehensive set of data that can be used to monitor and optimize call center operations.
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:
