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Begin by exporting the required data from Freshsales. Log in to your Freshsales account, navigate to the module containing the data you want to export (like Leads, Deals, Contacts, etc.), and use the export feature to download the data as a CSV file. This will serve as the raw data source for transferring to BigQuery.
Once you have the CSV file, review and clean it up if necessary. Ensure that the data types and formats are consistent and match the schema you plan to create in BigQuery. Remove any unnecessary columns or records that you do not need to import.
If you haven't already set up a Google Cloud account, do so by visiting the Google Cloud Platform (GCP) website. Create a new project within GCP where you will store your BigQuery datasets. Ensure you have billing enabled on your account to utilize BigQuery services.
Navigate to the BigQuery section within the GCP Console. Create a new dataset by clicking on the "Create Dataset" button. Name your dataset and configure any specific settings like data location and expiration if needed.
Before importing the CSV file, define a schema for your BigQuery table. This schema should match the structure of your CSV file, specifying each column's name, data type (such as STRING, INTEGER, FLOAT, BOOLEAN, etc.), and mode (NULLABLE, REQUIRED, or REPEATED).
Upload your prepared CSV file to Google Cloud Storage (GCS). Navigate to the GCS section in the GCP Console, create a new bucket if necessary, and upload the CSV file. Note the bucket name and the file path, as you will need these for the next step.
In the BigQuery section of the GCP Console, use the "Create Table" function to load data from your CSV file in GCS to BigQuery. Select "Google Cloud Storage" as the source, enter the GCS file path, and specify the table schema you defined earlier. Configure any additional settings such as write preference (Append, Overwrite, etc.) and start the import process. Once complete, verify the data in BigQuery to ensure it matches your expectations.
By following these steps, you can successfully move data from Freshsales to BigQuery without relying on third-party connectors or integrations.
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.
Freshsales is a modern, AI-powered sales automation and customer relationship management (CRM) solution designed to help businesses streamline their sales processes and drive revenue growth. It offers a range of features, including lead and contact management, deal tracking, sales forecasting, email integration, and automation. Freshsales' AI capabilities, such as lead scoring and intelligent data capture, provide sales teams with valuable insights and intelligent recommendations. Freshsales integrates seamlessly with popular business tools, allowing for a centralized view of customer data.
Freshsales's API provides access to a wide range of data related to customer relationship management (CRM) and sales automation. The following are the categories of data that can be accessed through Freshsales's API:
1. Contacts: Information about individual contacts, including their name, email address, phone number, and job title.
2. Accounts: Information about companies or organizations, including their name, address, and industry.
3. Deals: Information about sales deals, including the deal amount, stage, and expected close date.
4. Activities: Information about activities related to sales and customer interactions, including calls, emails, and meetings.
5. Notes: Information about notes and comments related to contacts, accounts, and deals.
6. Tasks: Information about tasks related to sales and customer interactions, including due dates and priorities.
7. Custom fields: Information about custom fields that can be added to contacts, accounts, and deals to capture additional data.
8. Reports: Information about reports generated from the data in Freshsales, including sales performance reports and pipeline reports.
Overall, Freshsales's API provides access to a comprehensive set of data that can be used to improve sales and customer relationship management processes.
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:
