How to load data from Salesforce to BigQuery

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

Trusted by data-driven companies

Building your pipeline or Using Airbyte

Airbyte is the only open source solution empowering data teams  to meet all their growing custom business demands in the new AI era.

Building in-house pipelines
Bespoke pipelines are:
  • Inconsistent and inaccurate data
  • Laborious and expensive
  • Brittle and inflexible
Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
After Airbyte
Airbyte connections are:
  • Reliable and accurate
  • Extensible and scalable for all your needs
  • Deployed and governed your way
All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Salesforce connector in Airbyte

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

Set up BigQuery for your extracted Salesforce data

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

Configure the Salesforce 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.

Take a virtual tour

Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

Demo video of Airbyte Cloud

Demo video of AI Connector Builder

What sets Airbyte Apart

Modern GenAI Workflows

Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

Move Large Volumes, Fast

Quickly get up and running with a 5-minute setup that supports both incremental and full refreshes, for databases of any size.

An Extensible Open-Source Standard

More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.

Full Control & Security

Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

Enterprise Support with SLAs

Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

What our users say

Jean-Mathieu Saponaro
Data & Analytics Senior Eng Manager

"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"

Learn more
Chase Zieman headshot
Chase Zieman
Chief Data Officer

“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.”

Learn more
Alexis Weill
Data Lead

“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria.
The value of being able to scale and execute at a high level by maximizing resources is immense”

Learn more

How to Sync Salesforce to BigQuery Manually

  1. Use Salesforce Reports or Data Export Service:
    • You can manually generate reports or use the data export service provided by Salesforce to extract your data.
    • Schedule or perform an export of the relevant objects (e.g., Leads, Opportunities, Contacts).
  2. Use Salesforce APIs:
    • Utilize the Salesforce REST API or Bulk API to programmatically extract data.
    • Write a script or use a command-line tool like curl to make API requests and retrieve the data.
  1. Format the Data:
    • Ensure that the data extracted from Salesforce is in a format supported by BigQuery (CSV, JSON, Avro, or Parquet).
    • Clean and transform the data if necessary, making sure to handle any data type discrepancies.
  2. Compress the Data (Optional):
    • BigQuery supports compressed data formats, which can save on storage and improve load times.
    • Use tools like gzip to compress your CSV or JSON files.
  3. Split Large Data Files (Optional):
    • If you have very large data files, consider splitting them into smaller chunks to make the upload process more manageable and potentially parallelize the load operation.
  1. Create a Bucket:
    • Go to the Google Cloud Console and create a new storage bucket in Google Cloud Storage if you don't already have one.
  2. Upload Files:
    • Use the Google Cloud Console, gsutil, or the Google Cloud Storage API to upload your prepared data files to the GCS bucket.
  1. Create a Dataset and Table in BigQuery:
    • In the Google Cloud Console, navigate to BigQuery and create a new dataset.
    • Define a table schema that matches the structure of your Salesforce data.
  2. Load Data from GCS into BigQuery:
    • Use the BigQuery Web UI, bq command-line tool, or the BigQuery API to create a load job.
    • Specify the GCS file path, the table you're loading the data into, and any additional configurations (such as field delimiters, skip header rows, etc.).
  1. Check the Load Job:
    • After the load job completes, check for any errors or warnings that may have occurred during the import process.
  2. Query the Data:
    • Run some test queries in BigQuery to ensure that the data has been loaded correctly and matches your expectations.
  1. Scripting:
    • To avoid manual repetition, you can write scripts to automate the extraction, transformation, and loading processes.
  2. Cloud Functions or Cloud Workflows:
    • Use Google Cloud Functions or Cloud Workflows to orchestrate and automate the data pipeline.
  3. Schedule Regular Updates:
    • Set up a schedule to regularly extract data from Salesforce and update your BigQuery dataset.

Keep in mind that this manual process can be time-consuming and may require maintenance. If you find that you need to perform this operation regularly or with large volumes of data, consider using a data pipleine tool like Airbyte.

How to Sync Salesforce to BigQuery Manually - Method 2:

FAQs

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.

Salesforce is a cloud-based customer relationship management (CRM) platform providing business solutions software on a subscription basis. Salesforce is a huge force in the ecommerce world, helping businesses with marketing, commerce, service and sales, and enabling enterprises’ IT teams to collaborate easily from anywhere. Salesforces is the force behind many industries, offering healthcare, automotive, finance, media, communications, and manufacturing multichannel support. Its services are wide-ranging, with access to customer, partner, and developer communities as well as an app exchange marketplace.

Salesforce's API provides access to a wide range of data types, including:  

1. Accounts: Information about customer accounts, including contact details, billing information, and purchase history.  

2. Leads: Data on potential customers, including contact information, lead source, and lead status.  

3. Opportunities: Information on potential sales deals, including deal size, stage, and probability of closing.  

4. Contacts: Details on individual contacts associated with customer accounts, including contact information and activity history.  

5. Cases: Information on customer service cases, including case details, status, and resolution.  

6. Products: Data on products and services offered by the company, including pricing, availability, and product descriptions.  

7. Campaigns: Information on marketing campaigns, including campaign details, status, and results.  

8. Reports and Dashboards: Access to pre-built and custom reports and dashboards that provide insights into sales, marketing, and customer service performance.  

9. Custom Objects: Ability to access and manipulate custom objects created by the organization to store specific types of data.  

Overall, Salesforce's API provides access to a comprehensive set of data types that enable organizations to manage and analyze their customer relationships, sales processes, and marketing campaigns.

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: 
1. Set up Salesforce to BigQuery as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Salesforce to BigQuery and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud. 

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.

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:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter