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
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 exporting the data you need from HubSpot. Log in to your HubSpot account and navigate to the specific data section you need (e.g., contacts, companies, deals). Use the export function to download your data in a CSV format. Ensure that you have the necessary permissions and select the appropriate fields to include in your export.
Once you have the exported CSV file, review it to ensure it meets your data quality standards. Cleanse the data if necessary by removing duplicates, correcting errors, and formatting it consistently. This step helps to maintain data integrity once it's in your data lake.
Log into your AWS Management Console and create an Amazon S3 bucket where your data will be stored. Choose a unique bucket name, select the region closest to your operations for optimal performance, and configure the bucket settings to ensure security and access controls are appropriate for your organization’s requirements.
Open your AWS Management Console, navigate to the S3 service, and select the bucket you created. Use the “Upload” feature to transfer your CSV file from your local machine to the S3 bucket. You can use the AWS CLI for more automated and scriptable uploads if necessary.
Set up AWS Glue to create a data catalog for the data stored in your S3 bucket. In the AWS Glue service, create a new crawler. Configure the crawler to access the S3 bucket, and define the schema based on the CSV structure. Run the crawler to populate the AWS Glue Data Catalog with metadata about your HubSpot data.
With your data cataloged, you can now use AWS Athena to query your data directly from S3. Go to the Athena service in the AWS Management Console, and set up a database and table using the AWS Glue Data Catalog. This allows you to use standard SQL queries to access and analyze your HubSpot data in the data lake.
To ensure ongoing data transfer, create an AWS Lambda function that automates the process. Use Python or Node.js to script the Lambda function, which will export data from HubSpot via their API, upload it to S3, and trigger the AWS Glue crawler. Schedule this Lambda function with Amazon CloudWatch Events to run at regular intervals, ensuring your data lake stays up-to-date with the latest data from HubSpot.
By following these steps, you can effectively move your HubSpot data to an AWS Data Lake, leveraging AWS services without relying on third-party connectors or integrations.
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:
