How to load data from Redshift to DynamoDB

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

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Redshift connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up DynamoDB for your extracted Redshift 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 Redshift to DynamoDB 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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How to Sync to Manually

Step 1: Set up IAM Roles

Create IAM roles with the necessary permissions for Amazon Redshift, Amazon S3, AWS Lambda, and Amazon DynamoDB.

1. Redshift Role: Permissions to unload data to S3.
2. Lambda Execution Role: Permissions to read from S3 and write to DynamoDB.

Step 2: Prepare Amazon Redshift Data

1. Connect to your Redshift cluster using an SQL client.
2. Write and execute a query to select the data you want to move to DynamoDB.

Step 3: Unload Data from Redshift to S3

Use the UNLOAD command to export the data from Redshift to S3 in a CSV or JSON format.

UNLOAD ('SELECT * FROM your_table')
TO 's3://yourbucket/yourdata/'
CREDENTIALS 'aws_iam_role=arn:aws:iam::123456789012:role/YourRedshiftRole'
DELIMITER ','
ADDQUOTES
ALLOWOVERWRITE
PARALLEL OFF;

Step 4: Create an AWS Lambda Function

1. Go to the AWS Lambda Console and create a new Lambda function.
2. Assign the previously created IAM role to this Lambda function.
3. Write a script in your Lambda function to read the data from the S3 bucket and write it to DynamoDB.

Here’s a Python example using Boto3:

import boto3
import csv

s3_client = boto3.client('s3')
dynamodb = boto3.resource('dynamodb')

def lambda_handler(event, context):
bucket = 'yourbucket'
key = 'yourdata/yourfile.csv' # Adjust if you have multiple files
obj = s3_client.get_object(Bucket=bucket, Key=key)

rows = obj['Body'].read().decode('utf-8').split('\n')
table = dynamodb.Table('YourDynamoDBTable')

for row in csv.DictReader(rows):
table.put_item(Item=row)

Step 5: Trigger Lambda Function

1. You can manually invoke the Lambda function from the console or CLI.
2. Alternatively, you can set up an event trigger on the S3 bucket to invoke the Lambda function whenever new files are unloaded from Redshift.

Step 6: Monitor the Lambda Function

After triggering the Lambda function, monitor its execution and logs in the AWS Lambda Console. Ensure that the data is being written to DynamoDB as expected.

Step 7: Verify Data in DynamoDB

1. Go to the DynamoDB console.
2. Browse the table to ensure that the data has been inserted correctly.

Step 8: Clean Up

After the data transfer is complete, consider cleaning up to avoid unnecessary storage costs.

1. Delete the S3 objects if they are no longer needed.
2. Check for any failed records or logs and address them accordingly.

Additional Notes:

  • Ensure that the data types in Redshift match the data types in DynamoDB.
  • If you have a large amount of data, consider batching the writes to DynamoDB to stay within provisioned write capacity limits.
  • Monitor AWS costs, as data transfer and operations might incur charges.
  • Use AWS KMS if encryption is required for data at rest in S3 or DynamoDB.