How to load data from Salesforce to Redshift

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

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 Redshift 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 Redshift 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.

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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.

Guided Tour: Assisting you in building connections

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.

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

To start, use Salesforce's built-in Data Export feature to extract data. Navigate to "Setup" in Salesforce, then "Data Export." Schedule a data export or perform a manual one. This will generate a ZIP file containing CSV files for each object you choose to export.

Step 2: Prepare Local Environment

Download the Salesforce data export ZIP file to your local machine. Extract the ZIP file to access the CSV files. Ensure you have all necessary files needed for your data migration.

Step 3: Set Up Amazon Redshift Cluster

If you haven't already, set up an Amazon Redshift cluster. Log in to your AWS Management Console, navigate to the Redshift service, and follow the prompts to create a new cluster. Ensure you note down the cluster endpoint, database name, and login credentials.

Step 4: Create Tables in Redshift

Before loading data, create tables in Redshift that match the structure of your Salesforce data. Use the SQL Workbench or AWS Query Editor to execute SQL commands to define tables. Pay attention to data types and field lengths to match Salesforce's schema.

Step 5: Transfer CSV Files to Amazon S3

Upload the extracted CSV files to an Amazon S3 bucket. Log into the AWS Management Console, navigate to the S3 service, and create a new bucket if necessary. Use the AWS CLI or the web interface to upload your files, ensuring the bucket is in the same region as your Redshift cluster for optimal performance.

Step 6: Load Data from S3 to Redshift

Use the COPY command in Redshift to load data from the CSV files in S3 into your Redshift tables. Connect to your Redshift cluster using SQL Workbench or the AWS Query Editor, and execute the COPY command. Make sure to specify the appropriate CSV file path, S3 bucket name, IAM role, and data format options.

Step 7: Verify and Validate Data

After loading the data, perform checks to ensure it was transferred correctly. Run SQL queries in Redshift to count rows, check for nulls, and validate data integrity against the original Salesforce data. Make adjustments as necessary and document any discrepancies for further investigation.
By following these steps, you can successfully move data from Salesforce to Amazon Redshift without relying on third-party connectors or integrations.