How to load data from Salesforce to BigQuery

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

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

Set up BigQuery for your extracted Salesforce 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 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.
Try Airbyte CloudManual method (the hard way)

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.

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Setup Complexities simplified!

You don’t need to put hours into figuring out how to use Airbyte to achieve your Data Engineering goals.

Simple & Easy to use Interface

Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.

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Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.

Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes

Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.

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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 enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

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

Raman Singh

Tech Lead at Symend

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

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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.”

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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: Extract Data from Salesforce

  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.

Step 2: Prepare Data for BigQuery

  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.

Step 3: Upload Data to Google Cloud Storage (GCS)

  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.

Step 4: Load Data into BigQuery

  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.).

Step 5: Verify Data Integrity

  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.

Step 6: Automate the Process (Optional)

  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.