Freshcaller is a cloud-based call center solution offering call routing, IVR, and analytics. It helps businesses manage customer support efficiently and improve communication.
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This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
Set up a source connector to extract data from in Airbyte
Choose from one of 400 sources where you want to import data from. This can be any API tool, cloud data warehouse, database, data lake, files, among other source types. You can even build your own source connector in minutes with our no-code no-code connector builder.
Configure the connection in Airbyte
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Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Move large volumes, fast.
Change Data Capture.
Security from source to destination.
We support the CDC methods your company needs
Log-based CDC
Timestamp-based CDC
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Including for your Airbyte Open Source instance with our premium support.
Airbyte supports a growing list of destinations, including cloud data warehouses, lakes, and databases.
Airbyte supports a growing list of destinations, including cloud data warehouses, lakes, and databases.
Airbyte supports a growing list of sources, including API tools, cloud data warehouses, lakes, databases, and files, or even custom sources you can build.
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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.
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 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.
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.
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.
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