How to load data from Marketo to Weaviate

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

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Bespoke pipelines are:
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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
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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 Weaviate 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 Weaviate 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.
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Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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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.

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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.

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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.

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What our users say

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Tech Lead at Symend

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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.”

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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."

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How to Sync to Manually

Step 1: Export Data from Marketo

Begin by logging into your Marketo account. Navigate to the "Database" section and identify the specific data you want to export. Use the "List" or "Smart List" feature to compile the data. Once compiled, export the list as a CSV file. Marketo allows CSV exports for lead and activity data, which will serve as the basis for your data transfer.

Analyze the exported CSV file and determine the data schema that aligns with Weaviate's requirements. Weaviate is a vector database, so you need to define the classes and properties that will house your data. Create a JSON schema outlining how each field from the CSV will map to Weaviate's classes and properties.

Convert the CSV data into JSON format, as Weaviate accepts data in JSON. Use a script written in Python, JavaScript, or another language of your choice to read the CSV file and transform each row into a JSON object. Ensure that the JSON matches the schema you prepared in the previous step.

Set up a Weaviate instance if you haven't already. This can be done by running Weaviate locally using Docker or deploying it on a cloud platform. Follow Weaviate's installation documentation to ensure it's correctly configured and accessible.

With Weaviate running, use its RESTful API to create the schema that matches your JSON data structure. This involves defining the classes and their respective properties in Weaviate. Use API tools like Postman or command-line tools like curl to send POST requests to the Weaviate endpoint, setting up the necessary schema.

Once the schema is set, proceed to import the JSON data into Weaviate. Use the RESTful API to send POST requests with your JSON objects. Ensure that the data is loaded into the correct class and that each property is correctly populated. You might need to write a script to automate this process for large datasets.

After loading the data, perform validation checks to ensure data integrity. Query Weaviate using its API to verify that the data has been correctly imported. Check for completeness and accuracy by comparing sample records from Marketo against the records now stored in Weaviate. Make adjustments as necessary to resolve any discrepancies.