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First, log in to your Freshcaller account. Navigate to the Reports section where you can find call logs and other relevant data. Use the export option to download the data as a CSV file. Ensure you have the necessary permissions to access and export the data. Save the CSV file to your local machine.
Log in to your Google Cloud Platform account. If you haven't already, create a new project where your BigQuery dataset will reside. Enable the BigQuery API for this project by navigating to the APIs & Services dashboard and searching for BigQuery.
Access the BigQuery console from your GCP dashboard. Create a new dataset by clicking on "Create dataset." Assign a unique name to your dataset and configure any necessary settings, such as data location and expiration preferences.
Analyze the structure of your exported CSV file from Freshcaller. Use this structure to define a schema for your BigQuery table. This schema should include the field names and data types (e.g., STRING, INTEGER, TIMESTAMP) that correspond to the columns in your CSV file.
Navigate to Google Cloud Storage in your GCP console. Create a new bucket to store your CSV file. Upload the CSV file to this bucket using the "Upload files" option. Make sure the bucket is in the same region as your BigQuery dataset for optimal performance.
In the BigQuery console, create a new table by selecting your dataset and clicking "Create table." Choose "Create table from Google Cloud Storage." Provide the URI of your uploaded CSV file (e.g., gs://your-bucket-name/your-file.csv). Select "CSV" as the file format and configure any additional options, such as field delimiters and header row presence. Use the schema defined in Step 4 to map the CSV columns to BigQuery fields.
After loading the data, verify the import by running a few queries in the BigQuery console. Check the number of records and sample data to ensure everything was imported correctly. Use SQL queries to explore and analyze your data as needed.
This guide assumes you have the basic knowledge and permissions to access and manage resources in both Freshcaller and GCP, as well as the technical familiarity with CSV files and SQL queries.
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.
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