How to load data from Marketo to Redshift

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

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Building in-house pipelines
Bespoke pipelines are:
  • Inconsistent and inaccurate data
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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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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 Redshift 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 Redshift 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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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 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

Raman Singh

Tech Lead at Symend

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

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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 exporting the desired data from Marketo. Log in to your Marketo account, navigate to the section containing the data you want to export (such as Leads or Activities), and utilize the export function to download the data as a CSV file. Ensure that you export all necessary fields required for your analysis.

Once you have the CSV file, inspect it to ensure that the data is clean and formatted correctly for Redshift. Check for any missing values, incorrect data types, or formatting issues. You may need to use a tool like Excel, Google Sheets, or a text editor to clean the data manually.

To load data into Redshift, you first need to store it in Amazon S3. Log in to your AWS Management Console and create a new S3 bucket if you don’t have one already. Upload the cleaned CSV file from Marketo to this S3 bucket, making note of the S3 URI for future reference.

Ensure that you have an Amazon Redshift cluster set up and running. If not, create a new Redshift cluster via the AWS Management Console. Once the cluster is available, note down the connection details, including the endpoint and port number, which will be used to connect and load data.

Connect to your Redshift cluster using a SQL client like SQL Workbench/J. Define the table structure in Redshift that matches the schema of your CSV file. Use the `CREATE TABLE` SQL statement to set up the table, specifying data types that correspond to those in your CSV.

Use the `COPY` command in Redshift to import data from your S3 bucket. The command syntax is:
```sql
COPY your_table_name
FROM 's3://your-bucket-name/your-file-name.csv'
IAM_ROLE 'your-iam-role-arn'
CSV;
```
Replace placeholders with your actual table name, S3 path, and IAM role ARN. This command will load the data from S3 directly into your Redshift table.

After loading the data, run SQL queries to verify its integrity and completeness. Check for row counts, data types, and sample data to ensure everything was transferred accurately. If any discrepancies are found, you may need to revisit previous steps to correct and reload the data.

This guide will help you efficiently migrate data from Marketo to Redshift using AWS's native services without relying on third-party connectors.