This project aims to dynamically upload files to an Amazon Redshift database using an Amazon S3 bucket and an AWS Lambda function triggered by events.
Create a bucket in the desired region, configure access options and optionally, add a policy to the bucket to control access.
Assign the following permissions:
AmazonRedshiftFullAccess
AmazonS3ReadOnlyAccess
AWSLambda_FullAccess
Generate an Access Key for the user.
Assign the following permissions to the role:
AmazonEC2FullAccess
AmazonRedshiftFullAccess
AmazonS3ReadOnlyAccess
AWSLambdaBasicExecutionRole
Choose Python as the language.
Assign the previously created execution role.
In the Lambda function configuration, add a trigger for the S3 bucket or in the S3 bucket properties create event configuration and choose Lambda function as destination.
Configure the event type (e.g., s3:ObjectCreated:*).
Specify the prefix or suffix if necessary.
Associate the Lambda function with the same VPC used by the Redshift cluster.
Download the psycopg2 module from this repository (https://github.com/jkehler/awslambda-psycopg2) for the Python version you are using.
Add this module as a layer to the Lambda function.
Create a Redshift Cluster and associate an IAM role with permissions to use Redshift and access the S3 bucket and create a schema within the Redshift database where the data will be stored.
The code should connect to Redshift and copy the files from the S3 bucket to the database.
In the Lambda configuration, add the necessary environment variables (e.g., AWS_ACCESS_KEY and AWS_SECRET_ACCESS_KEY obtained from the previously created IAM user).
For Visualization and Notifications use CloudWatch dashboards to visualize metrics and logs in real-time.
Once the data is in Redshift, you can connect it to a data visualization tool like Tableau, Power BI, or Amazon QuickSight for analysis and reporting.
Security: Ensure that keys and sensitive information are handled securely.
Testing: Conduct thorough testing to ensure the data flow works correctly.