How to load data from Harvest to Redshift

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

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Harvest 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 Harvest 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 Harvest 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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How to Sync to Manually

Step 1: Export Data from Harvest

Begin by manually exporting the data from Harvest. Log into your Harvest account and navigate to the data export section. Choose the data you wish to export, such as time entries, expenses, or project details. Export the data in a compatible format like CSV or Excel, which is suitable for manual processing and uploading into Redshift.

Store the exported files on your local machine or a secure server. Ensure that the storage location is easily accessible and has enough space for the data files. Organizing these files with clear naming conventions will facilitate easier data management and processing.

Open the exported data files and inspect them for any necessary transformations. Using a tool like Python (with pandas) or Excel, clean and format the data to ensure compatibility with Redshift's columnar storage format. This may involve data type conversions, handling missing values, or restructuring data columns for optimal loading.

If not already done, set up your AWS Redshift environment. This includes creating a Redshift cluster and configuring the necessary security groups, VPC settings, and IAM roles. Ensure that your Redshift cluster is properly set up to receive external data loads and that you have the necessary access credentials.

Based on the data structure from Harvest, define and create the necessary table schemas in Redshift. Use SQL commands within the Redshift console or a SQL client tool to specify column names, data types, and any constraints or primary keys. This schema should match the transformed data format prepared in the previous step.

To facilitate data transfer to Redshift, upload the transformed data files to an Amazon S3 bucket. Use the AWS S3 console or AWS CLI for uploading. Ensure the S3 bucket is in the same region as your Redshift cluster to avoid cross-region data transfer costs and latency issues.

Use the COPY command in Redshift to load data from the S3 bucket into your Redshift tables. This command efficiently transfers data from S3 to Redshift. Ensure that the IAM role associated with your Redshift cluster has permissions to access the S3 bucket. Execute the COPY command, specifying the S3 file path, table name, and any necessary options like data format (CSV), delimiter, and error handling settings. Monitor the process to ensure data is loaded correctly, and verify the data within Redshift once the operation is complete.