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Begin by familiarizing yourself with the data structures used in Freshcaller. Identify the specific data you need to move, such as call logs, customer details, or agent performance metrics. Explore Freshcaller's native export options, typically available in formats such as CSV or Excel, to determine the best way to extract this data.
Use Freshcaller's built-in export functionality to download the necessary data files. Navigate to the appropriate section in Freshcaller (e.g., reports or analytics), and use the export feature to download the data in a CSV format. Ensure that the export includes all relevant fields you need for analysis in Starburst Galaxy.
Once you have the exported data files, review them to ensure they contain the necessary information and are free of errors. Clean the data by removing duplicates, correcting any inconsistencies, and ensuring all fields are properly formatted. This step is crucial for ensuring smooth integration into Starburst Galaxy.
Before loading the data into Starburst Galaxy, identify any transformations needed to align with its schema requirements. This could involve restructuring the data, adjusting data types, or renaming columns to match the expected format in Starburst Galaxy. Document these transformation requirements clearly.
Use a scripting language like Python or SQL to apply the necessary data transformations. Write scripts to automate the restructuring, formatting, and renaming of fields as identified in the previous step. This process ensures that the data is compatible with Starburst Galaxy"s ingestion requirements.
With the data properly transformed, prepare to load it into Starburst Galaxy. Utilize Starburst Galaxy"s native capabilities to ingest data from local or cloud storage. Follow the specific steps provided by Starburst for data ingestion, ensuring that you map the transformed data accurately to the corresponding tables or views in Starburst Galaxy.
After loading the data, perform a thorough validation process to ensure data integrity. Compare the loaded data in Starburst Galaxy against the original data from Freshcaller to confirm that all records are present and accurate. Run queries to check for consistency and completeness, and resolve any discrepancies that arise during the verification process.
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:
