How to load data from Freshsales to BigQuery

Learn how to use Airbyte to synchronize your Freshsales data into BigQuery within minutes.

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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Freshsales connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up BigQuery for your extracted Freshsales data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Freshsales to BigQuery in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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Tech Lead at Symend

Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

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Chase Zieman

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“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”

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Rupak Patel

Operational Intelligence Manager

"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."

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How to Sync to Manually

Step 1: Export Data from Freshsales

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.