How to load data from Marketo to Databricks Lakehouse

Learn how to use Airbyte to synchronize your Marketo data into Databricks Lakehouse within minutes.

Try Airbyte CloudManual method (the hard way)

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.

Building in-house pipelines
Bespoke pipelines are:
  • Inconsistent and inaccurate data
  • Laborious and expensive
  • Brittle and inflexible
Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
After Airbyte
Airbyte connections are:
  • Reliable and accurate
  • Extensible and scalable for all your needs
  • Deployed and governed your way
All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Marketo connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Databricks Lakehouse for your extracted Marketo data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Marketo to Databricks Lakehouse in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
Try Airbyte CloudManual method (the hard way)

Take a virtual tour

Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

Demo video of Airbyte Cloud

Demo video of AI Connector Builder

Talk to our team

Setup Complexities simplified!

You don’t need to put hours into figuring out how to use Airbyte to achieve your Data Engineering goals.

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.

Try Airbyte CloudManual method (the hard way)

What sets Airbyte Apart

Modern GenAI Workflows

Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

Move Large Volumes, Fast

Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

An Extensible Open-Source Standard

More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.

Full Control & Security

Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

Enterprise Support with SLAs

Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

What our users say

Raman Singh

Tech Lead at Symend

Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

Learn more
Chase Zieman headshot

Chase Zieman

Chief Data Officer

“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.”

Learn more

Rupak Patel

Operational Intelligence Manager

"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."

Learn more

How to Sync to Manually

Step 1: Export Data from Marketo

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.