

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."
First, log in to your HubSpot account and navigate to the "Contacts" or any other data set you wish to export. Use HubSpot's native export functionality to download the data as a CSV file. To do this, go to the Contacts page, select "Export," choose your desired format (e.g., CSV), and download the file to your local machine.
Log in to the AWS Management Console and go to the S3 service. Create a new bucket where you will store the data exported from HubSpot. Ensure the bucket name is unique and choose an appropriate region. Configure permissions and bucket policies as needed to allow data upload.
Navigate to the newly created S3 bucket and upload the CSV file you exported from HubSpot. You can do this via the AWS S3 console by selecting "Upload" and choosing the file from your local machine. Ensure the file is uploaded to the correct bucket and folder path if applicable.
Go to the AWS Glue service in your AWS Management Console. Create a new Glue Crawler to automatically detect the schema of your data. Configure the crawler to point to the S3 bucket and path where you uploaded the CSV file. Run the crawler to populate the Glue Data Catalog with the schema information of the HubSpot data.
In the AWS Glue Console, create a new ETL job. Select the data source identified by the Glue Crawler. Choose the target location in S3 where you want to store the transformed data. Optionally, configure transformations if needed, such as data cleaning or format conversion. Define the job's logic using either the Glue Studio visual editor or by writing a PySpark script.
Execute the Glue ETL job to process and transform the data from HubSpot. Monitor the job's progress in the AWS Glue Console. Once completed, the transformed data will be written to the specified S3 location in the format you defined during the job setup.
After the ETL job finishes, navigate to the S3 bucket where the output data was written. Verify the integrity and correctness of the data by downloading a sample file or using AWS Athena to query the data directly in S3. Ensure the data meets your expectations and is ready for further processing or analysis.
By following these steps, you can efficiently transfer and process data from HubSpot to AWS S3 using AWS Glue without the need for third-party connectors.
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.
A platform focused on sales and inbound marketing, Hubspot helps businesses optimize their online marketing strategies for greater visibility to attract more visitors, collect leads, and convert prospects into customers. HubSpot provides a variety of essential services and strategies to move businesses forward, including social media and email marketing, website content management, search engine optimization, blogging, and analytics and reporting. Hubspot is an all-around solution for business teams to grow their customer base through effective marketing.
HubSpot's API provides access to a wide range of data categories, including:
1. Contacts: Information about individual contacts, including their name, email address, phone number, and company.
2. Companies: Information about companies, including their name, industry, and location.
3. Deals: Information about deals, including their stage, amount, and close date.
4. Tickets: Information about customer support tickets, including their status, priority, and owner.
5. Products: Information about products, including their name, price, and description.
6. Analytics: Data on website traffic, email performance, and other marketing metrics.
7. Workflows: Information about automated workflows, including their triggers, actions, and outcomes.
8. Forms: Information about forms, including their fields, submissions, and conversion rates.
9. Social media: Data on social media engagement, including likes, shares, and comments.
10. Integrations: Information about third-party integrations, including their status and configuration.
Overall, HubSpot's API provides access to a wide range of data categories that can be used to improve marketing, sales, and customer support efforts.
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: