How to load data from Dremio to Redshift

Learn how to use Airbyte to synchronize your Dremio data into Redshift 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 Dremio 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 Dremio 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 Dremio 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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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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How to Sync to Manually

Step 1: Prepare the Data in Dremio

Start by ensuring the data you need to move is clean and organized in Dremio. Use Dremio’s SQL editor to select and prepare the datasets. Ensure that the data types are compatible with Redshift to avoid errors during the import process.

Use Dremio’s export functionality to save the prepared dataset to a local file. You can export the data to a CSV or Parquet format, as these are commonly supported by Amazon Redshift. Make sure to verify the exported files for data integrity.

Create an S3 bucket in your AWS account where you will temporarily store the exported data files. This step is crucial because Amazon Redshift uses S3 as the staging area for data imports. Ensure you have the correct permissions set up for reading and writing to the bucket.

Upload your exported data files to the S3 bucket you created. You can use the AWS Management Console, AWS CLI, or SDKs to transfer the files. Ensure that the data files are in an accessible location and that you note the S3 URI path, as it will be used later.

Log in to your Amazon Redshift cluster and create a table that matches the schema of the data you exported from Dremio. Use the SQL editor in the Redshift console or a client like SQL Workbench. Ensure that the column data types align with those of your exported dataset.

Use the COPY command in Redshift to load data from the S3 bucket into your Redshift table. You will need to specify the S3 URI, file format, and any other options like delimiter or compression type. Ensure you have the necessary IAM roles and policies in place to allow Redshift to access the S3 bucket.

After loading the data, validate that the import was successful by running queries in Redshift to check row counts and data integrity. Once validated, you can clean up by deleting the temporary files in the S3 bucket if they are no longer needed to save on storage costs.