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
Andre Exner
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
Chase Zieman
“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.”
Rupak Patel
"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."
To extract data from Marketo, you need to use their REST API. Start by obtaining your Marketo API credentials, including the Client ID, Client Secret, and the REST API Endpoint. These can be generated in the Marketo Admin panel under the LaunchPoint services.
Use the API credentials to authenticate your requests to Marketo's API. Begin by obtaining an access token via the `/identity/oauth/token` endpoint. Once authenticated, use the appropriate API endpoints to pull the desired data, such as leads, activities, or campaigns. Write scripts in a language like Python to automate data extraction, making HTTP GET requests to retrieve the data in JSON format.
After retrieving the JSON data from Marketo, transform it into a CSV format that can be easily handled by AWS services. You can use Python's `pandas` library to read the JSON data and convert it to CSV. This involves parsing the JSON response and structuring the data into rows and columns suitable for CSV.
Log in to your AWS Management Console and create a new S3 bucket where you will store the transformed CSV files. Configure the bucket policies and permissions to ensure secure access and data integrity. Make sure that your AWS IAM user or role has the necessary permissions to write data to this bucket.
Automate the upload of your CSV files to the S3 bucket using AWS SDKs, such as `boto3` in Python. Establish a connection to your S3 bucket, then use the `put_object` method to upload the CSV files. Ensure that each file is properly named and stored in relevant directories within the bucket for organized data management.
In AWS Glue, set up a crawler to automatically catalog the data from your S3 bucket. The crawler will scan your data files, infer the schema, and create or update tables in the AWS Glue Data Catalog. This step is essential for enabling data querying and transformation using AWS Glue ETL jobs.
With your data cataloged, create and run AWS Glue ETL jobs to further process and transform your data as needed. You can use AWS Glue's built-in transformations or write custom scripts in Python or Scala. This step allows you to clean, enrich, and prepare your data for downstream analytics or loading into other AWS services like Redshift or Athena.
By following these steps, you can efficiently transfer and manage your Marketo data within the AWS ecosystem using native tools and services, ensuring a seamless ETL process without relying on external 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.
Marketo develops the marketing automation software underlying the capabilities of inbound marketing solutions, CRM, social marketing, and other services of the same type. A powerful yet simple-to-use solution for any size company, Marketo was built by marketers for marketers, so it is designed with the needs and solutions required by real businesses in mind. Marketo aims to simplify the marketing process with an all-in-one solution that includes social marketing, event management, marketing ROI and analytics reports, CRM integration, and more.
Marketo's API provides access to a wide range of data related to marketing automation and customer engagement. The following are the categories of data that can be accessed through Marketo's API:
1. Lead data: This includes information about individual leads such as their name, email address, phone number, company, job title, and other demographic information.
2. Campaign data: This includes information about marketing campaigns such as email campaigns, social media campaigns, and other types of marketing initiatives.
3. Activity data: This includes information about the activities that leads have taken such as opening an email, clicking on a link, visiting a website, or filling out a form.
4. Analytics data: This includes information about the performance of marketing campaigns such as open rates, click-through rates, conversion rates, and other metrics.
5. Account data: This includes information about the companies that leads work for such as company size, industry, and other relevant information.
6. Custom object data: This includes information about custom objects that have been created within Marketo such as events, webinars, and other types of marketing initiatives.
Overall, Marketo's API provides access to a wealth of data that can be used to improve marketing automation and customer engagement 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:
