How to load data from Marketo to Snowflake destination

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

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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 Snowflake destination 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 Snowflake destination 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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Tech Lead at Symend

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Chase Zieman

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“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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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: Extract Data from Marketo

Start by utilizing Marketo's REST API to extract the necessary data. You will need to authenticate yourself using OAuth 2.0 to gain access. Once authenticated, use the API to query the specific data objects (e.g., leads, activities) you wish to export. This data can be retrieved in JSON format, which is readily processable.

Once you've extracted the JSON data from Marketo, transform it into CSV format. This can be done using a scripting language like Python. By writing a script, you can parse the JSON data, extract the required fields, and then write these fields to a CSV file. This step ensures that your data is in a format that can be easily ingested by Snowflake.

Set up your Snowflake environment if it isn't already configured. This involves creating a database and the necessary tables that will store the Marketo data. Ensure that the schema of the Snowflake tables matches the structure of your CSV data to facilitate smooth data loading.

Save your CSV files locally or upload them to a cloud storage solution that Snowflake can access, such as AWS S3, Azure Blob Storage, or Google Cloud Storage. This step involves simply moving the files to a location where Snowflake's data loading utilities can access them.

Use Snowflake's staging feature to prepare the CSV files for loading. Create an external stage if your files are in cloud storage, or use an internal stage if they are stored locally. This involves using the `CREATE STAGE` command in Snowflake, specifying the storage location, and any necessary credentials for access.

Execute the `COPY INTO` command in Snowflake to load the data from the staged CSV files into your target tables. This command transfers the data from the stage into the Snowflake tables. Make sure to handle any data type conversions or transformations needed to align with the table schema during this process.

After the data has been loaded into Snowflake, perform checks to ensure data integrity. This can involve running queries to validate record counts, checking for duplicates, and ensuring data types are consistent. Once verification is complete, clean up any temporary files or stages used during the process to maintain an organized and efficient environment.

By following these steps, you can effectively move data from Marketo to Snowflake without relying on third-party connectors or integrations.