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Begin by exporting data from Gong. Log into your Gong account and navigate to the section where you can download data. Usually, this will be in the form of CSV files. Ensure you have the necessary permissions and select the relevant data you want to export.
Set up your local machine to handle data processing. Install necessary tools such as Python or any other scripting language you prefer, and ensure you have Google Cloud SDK installed for interacting with BigQuery.
Use a scripting language like Python to process and transform your Gong data into a format compatible with BigQuery. This might involve cleaning data, adjusting data types, or restructuring the data to match your BigQuery schema.
Access your Google Cloud Platform account, and create a new bucket in Google Cloud Storage. This bucket will act as a staging area for your data before it is imported into BigQuery. Ensure that your account has the necessary permissions to create and manage buckets.
Use the Google Cloud SDK or the Google Cloud Console to upload your transformed data file(s) to the bucket you created in the previous step. This can be done using the `gsutil cp` command in the terminal or through the web interface of Google Cloud Console.
In your Google Cloud Platform account, navigate to BigQuery and create a new dataset if one does not already exist. Then, create a new table within that dataset. Define the schema of the table to match the structure of your data.
Use the BigQuery web interface or the `bq` command-line tool to load your data from Google Cloud Storage into BigQuery. You�ll need to specify the source URI of your data file in GCS, the destination dataset and table in BigQuery, and any necessary load options such as the field delimiter or whether the file has headers.
Following these steps will allow you to move data from Gong to BigQuery without relying on third-party connectors or integrations. Adjust the specifics according to your data and project requirements.
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:
