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 exporting the desired data from Marketo. Log in to your Marketo account, navigate to the "Leads" or "Activities" section, and use the "Export" function. Choose the appropriate filters to specify the data range and fields you wish to export. Save the exported file in a CSV format for ease of handling.
Ensure that you have a secure method to transfer data from your local machine to the Databricks environment. You can use protocols such as SCP (Secure Copy Protocol) or SFTP (Secure File Transfer Protocol) to securely move your CSV file to a cloud storage service that is accessible by Databricks, such as AWS S3, Azure Blob Storage, or Google Cloud Storage.
Use the cloud provider's interface or a command-line tool to upload your CSV file to a cloud storage bucket. Ensure that the bucket permissions allow Databricks to access the file. For example, if using AWS S3, you can use the AWS CLI command `aws s3 cp` to upload the file.
In your Databricks workspace, configure access to the cloud storage where your CSV file is located. This involves setting up credentials or IAM roles that Databricks can use to read from the cloud storage. For instance, in AWS, you can attach an IAM role to the Databricks cluster with S3 read permissions.
Launch your Databricks workspace and use a notebook to create a table in the Lakehouse. You can use Spark SQL or DataFrame API to define the schema for your data. Execute a command such as `CREATE TABLE marketo_data (...)` to define the table structure that matches the CSV file.
Use Spark to load the CSV data from the cloud storage into the Databricks table. You can use the `spark.read.csv()` method with the appropriate schema and options for delimiter, header, etc. After reading the data, write it into the table using `dataframe.write.insertInto("marketo_data")`.
Once data transfer is complete, perform verification checks to ensure data integrity and completeness. Use SQL queries or DataFrame operations within Databricks to compare row counts and data profiles. If necessary, apply any required transformations or data cleaning steps to prepare the data for analysis or further processing.
By following these steps, you can effectively move data from Marketo to a Databricks Lakehouse environment 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.
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:
