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.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
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
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
“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.”
"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."
Begin by setting up API access in Salesforce. Navigate to the Salesforce setup menu and create a new Connected App. This app will allow you to authenticate and access Salesforce data programmatically. Ensure you enable OAuth settings, specifying necessary OAuth scopes, such as `api` and `refresh_token`. Note down the Consumer Key and Consumer Secret, as they will be used for authentication.
Use the Salesforce REST API to authenticate and retrieve data. First, obtain an access token by making a POST request to Salesforce's token endpoint with your Consumer Key, Consumer Secret, username, password, and security token. Once authenticated, use the access token to query Salesforce data. You can perform SOQL queries via the REST API to fetch the records you need.
In the AWS Management Console, set up an S3 bucket where the Salesforce data will be stored. Configure the bucket to suit your data storage and retrieval needs, such as enabling versioning or setting up bucket policies. Note the bucket name and region, as these will be needed in subsequent steps.
Format the retrieved Salesforce data into a structure suitable for uploading to S3, such as CSV or JSON. This may involve parsing the JSON response from Salesforce, transforming it as needed, and writing it to a file. Ensure the data is structured correctly to facilitate processing by AWS Glue later.
Use AWS SDKs, such as Boto3 for Python, to upload the prepared data files to your S3 bucket. Ensure you have configured your AWS credentials correctly and have the necessary permissions to write to the S3 bucket. Use the `put_object` method to upload the file(s) to a specified bucket and key.
In the AWS Glue Console, create a Glue Crawler to catalog the data stored in S3. Configure the crawler to point to the S3 bucket and specify the data format (e.g., CSV or JSON). Run the crawler to populate the AWS Glue Data Catalog with tables reflecting the structure of your Salesforce data.
With the data cataloged, use AWS Glue jobs to transform and process the data as needed. You can write ETL scripts in Python or Scala using the AWS Glue Studio or AWS Glue Console. Define the source (from the Glue Catalog) and any necessary transformations before writing the processed data back to S3 or into a data warehouse like Amazon Redshift.
By following these steps, you can efficiently move data from Salesforce to Amazon S3 using AWS Glue without relying on third-party connectors or integrations. Adjust the specifics of each step according to your exact data and business requirements.
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.
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.
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:
