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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.
Setup a connection to your Freshcaller site in minutes, and select the Freshcaller collections you want to replicate.
A cloud data platform, Snowflake Data Cloud provides a warehouse-as-a-service built specifically for the cloud. The Snowflake platform is designed to empower many types of data workloads, and offers secure, immediate, governed access to a comprehensive network of data. Snowflake’s innovative technology goes above the capabilities of the ordinary database, supplying users all the functionality of database storage, query processing, and cloud services in one package.
1. Open the Airbyte dashboard and click on "Sources" from the left-hand menu.
2. Click on the "Create a new source" button and select "Freshcaller" from the list of available connectors.
3. Enter a name for your Freshcaller source and click on "Next".
4. Enter your Freshcaller API credentials, including the API key and domain name.
5. Click on "Test connection" to ensure that the credentials are correct and the connection is successful.
6. Select the data you want to replicate from Freshcaller, including calls, agents, and call recordings.
7. Choose the replication frequency and the destination where you want to store the data.
8. Click on "Create source" to save your Freshcaller source connector on Airbyte.
9. You can now monitor the replication status and view the data in your destination of choice.
Note: It is important to ensure that your Freshcaller API credentials are correct and have the necessary permissions to access the data you want to replicate. Refer to the Freshcaller API documentation for more information on how to generate API keys and manage permissions.
1. First, navigate to the Airbyte website and log in to your account.
2. Once you are logged in, click on the "Destinations" tab on the left-hand side of the screen.
3. Scroll down until you find the Snowflake Data Cloud destination connector and click on it.
4. You will be prompted to enter your Snowflake account information, including your account name, username, and password.
5. After entering your account information, click on the "Test" button to ensure that the connection is successful.
6. If the test is successful, click on the "Save" button to save your Snowflake Data Cloud destination connector settings.
7. You can now use the Snowflake Data Cloud destination connector to transfer data from your Airbyte sources to your Snowflake account.
8. To set up a data transfer, navigate to the "Sources" tab on the left-hand side of the screen and select the source you want to transfer data from.
9. Click on the "Create New Connection" button and select the Snowflake Data Cloud destination connector as your destination.
10. Follow the prompts to set up your data transfer, including selecting the tables or data sources you want to transfer and setting up any necessary transformations or mappings.
11. Once you have set up your data transfer, click on the "Run" button to start the transfer process.
With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.
We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Ready to get started?
Frequently Asked Questions
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 should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: