Step 3: Optional AWS S3 Storage and Lambda Trigger

Follow these steps to store raw tweets in S3 and set up a Lambda function that triggers initial processing (e.g., language detection or simple filtering).

3.1 Set Up S3 Bucket and Permissions

1. Create an S3 Bucket:
   • Go to the AWS S3 console and create a bucket (e.g., real-time-tweet-storage).
2. Configure Permissions:
   • Ensure the bucket has the correct permissions so that Lambda functions can access it.
   • Grant write access to allow the Kafka producer script to upload files if needed.

3.2 Batch Upload to S3 (Optional)

Modify the Kafka producer script to save every few messages as a batch file in JSON format and upload it to S3. For example:

import boto3

# AWS S3 client setup
s3 = boto3.client('s3')

def save_and_upload_batch(messages):
    # Save messages to a local file
    batch_file = 'tweet_batch.json'
    with open(batch_file, 'w') as f:
        json.dump(messages, f)

    # Upload to S3
    s3.upload_file(batch_file, 'your_bucket_name', f'batches/{batch_file}')

3.3 AWS Lambda Trigger

Set up a Lambda function that triggers every time a file is uploaded to the S3 bucket.

1. Create Lambda Function:
   • Go to AWS Lambda and create a new function with a runtime (Python 3.8 or later).
2. Add Trigger:
   • In the Lambda function, add an S3 trigger for the bucket created earlier. This setup will automatically invoke the Lambda function whenever a new batch file is uploaded.
3. Lambda Code for Initial Processing:
   • Write the processing code, for example, to filter by language or parse each tweet’s content.