How to load data from Monday to Redshift

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

Try Airbyte Cloud Manual 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 Monday 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 Monday 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 Monday 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.

Try Airbyte Cloud Manual 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.

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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.

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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.

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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 Cloud Manual 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

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Tech Lead at Symend

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How to Sync to Manually

Step 1: Export Data from Monday.com

Start by exporting your data from Monday.com. Log in to your Monday.com account and navigate to the board that contains the data you want to move. Use the "Export Board to Excel" feature, which you can find under the "Board" menu. This will allow you to download the board data in Excel format (.xlsx), which can then be converted to a CSV file.

Step 2: Convert Excel to CSV Format

Open the downloaded Excel file in a spreadsheet application like Microsoft Excel or Google Sheets. Save or export the spreadsheet as a CSV file. CSV (Comma-Separated Values) is a simple and widely supported format that is ideal for data import into databases like Amazon Redshift.

Step 3: Set Up Amazon Redshift Cluster

If you haven't already, set up an Amazon Redshift cluster. Go to the AWS Management Console, navigate to the Redshift service, and create a new cluster. Ensure your cluster is properly configured with the necessary nodes and security groups to allow access from your network.

Step 4: Prepare the Data Schema in Redshift

Define the schema and tables in Redshift where you want to import the data. Connect to your Redshift cluster using SQL client tools like SQL Workbench/J. Use `CREATE TABLE` SQL statements to define the structure of the tables according to the data columns from your CSV file.

Step 5: Upload CSV to Amazon S3

Before loading data into Redshift, upload your CSV file to an Amazon S3 bucket. Go to the S3 service in the AWS Management Console, create a new bucket if necessary, and upload the CSV file. Ensure that the bucket has the correct permissions for Redshift to access it.

Step 6: Load Data from S3 to Redshift

Use the `COPY` command in Redshift to load data from the CSV file stored in S3 into your Redshift table. Connect to your Redshift database using a SQL client and execute a `COPY` command like:
```sql
COPY your_table_name
FROM 's3://your-bucket-name/your-file.csv'
IAM_ROLE 'your-iam-role-arn'
CSV
IGNOREHEADER 1;
```
Replace `your_table_name`, `your-bucket-name`, `your-file.csv`, and `your-iam-role-arn` with your actual table name, S3 bucket name, file name, and IAM role ARN that has permission to read from S3.

Step 7: Verify and Clean Up

After loading the data, verify that it has been imported correctly by running SQL queries in Redshift to check the data. Once confirmed, clean up by removing any temporary files from S3 if they are no longer needed and ensure your Redshift cluster is properly maintained for performance.

By following these steps, you can effectively transfer data from Monday.com to Amazon Redshift without relying on third-party tools.