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First, you need to access and extract the necessary data from Gong. This can usually be done by utilizing Gong's API. You will need to create an API key in Gong and use it to authenticate your requests. Write a script in a language like Python to send HTTP GET requests to Gong's API endpoints to fetch the required data. Ensure you handle pagination and any rate limits imposed by the API.
Once you have extracted the data from Gong, transform it into a CSV format, as this is a straightforward format that many systems, including Apache Iceberg, can work with. Use your script to parse the JSON data from Gong and write it into a CSV file. Be sure to include headers and properly handle data types to maintain data integrity.
Before you can load the data, you need to set up an Apache Iceberg environment. This typically involves configuring a compatible compute engine like Apache Spark or Flink. Install the necessary Iceberg libraries and ensure your environment is properly configured to access your file system or cloud storage where Iceberg tables will be managed.
Define the schema of your Iceberg table based on the CSV data structure. This involves specifying the column names and data types that match those in your CSV file. Use SQL or a Spark DataFrame API to create the table in your environment, depending on how Iceberg is integrated with your compute engine.
Load the CSV data into the Iceberg table. This can be done using a Spark job (or similar tool) to read the CSV file and write the data into the Iceberg table. Use Spark's DataFrame API to read the CSV, and then use the DataFrame `write` operation to insert the data into your Iceberg table. Ensure the data types and schema match exactly to avoid any loading errors.
After loading the data, verify that it has been accurately and completely transferred. Perform queries on your Iceberg table to check the row count and sample data against the original data from Gong. This step is crucial to ensure that the data transformation and loading process was successful without any data loss or corruption.
Once you have successfully moved the data manually, consider automating this process for future data transfers. Write scripts to automate the extraction, transformation, and loading (ETL) process. Use a scheduler like cron jobs (for Unix-based systems) or a task scheduler for periodic execution to keep your Iceberg tables updated with the latest data from Gong.
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.
Gong is a sales enablement platform that uses artificial intelligence to analyze sales calls and meetings, providing insights and recommendations to help sales teams improve their performance. The platform records and transcribes conversations, analyzes them for key topics and sentiment, and provides real-time coaching and feedback to sales reps. Gong also offers analytics and reporting tools to help sales managers track team performance and identify areas for improvement. The platform is designed to help sales teams close more deals, improve customer relationships, and increase revenue.
Gong's API provides access to a wide range of data related to sales conversations. The following are the categories of data that Gong's API gives access to:
1. Conversation data: This includes information about the participants, duration, and content of the conversation.
2. Call recordings: Gong's API allows users to access call recordings, which can be used for training and coaching purposes.
3. Transcripts: Gong's API provides access to transcripts of sales conversations, which can be used for analysis and insights.
4. Sales performance data: Gong's API provides data on sales performance, including metrics such as win rates, deal size, and sales cycle length.
5. Customer insights: Gong's API provides insights into customer behavior and preferences, which can be used to improve sales strategies and customer engagement.
6. Sales team performance data: Gong's API provides data on sales team performance, including metrics such as call volume, talk time, and response time.
7. Sales pipeline data: Gong's API provides data on the sales pipeline, including metrics such as pipeline velocity and conversion rates.
Overall, Gong's API provides a comprehensive set of data that can be used to improve sales performance and customer engagement.
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:
