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Learn how to automate PDF parsing using AWS serverless technologies like Amazon Textract, Lambda, S3, and DynamoDB. This architecture demonstrates a powerful utility for extracting text from PDFs at scale.
Original https://lnkd.in/eaHZB7QN
You Should Know:
1. AWS Services Used
- Amazon S3: Stores PDF files.
- AWS Lambda: Triggers processing when a new PDF is uploaded.
- Amazon Textract: Extracts text from PDFs.
- DynamoDB: Stores extracted text and metadata.
2. Key Commands & Setup
AWS CLI Setup
aws configure
Enter your AWS credentials (Access Key, Secret Key, Region).
Create an S3 Bucket
aws s3 mb s3://pdf-parsing-bucket
Deploy Lambda Function
aws lambda create-function \ --function-name PDFProcessor \ --runtime python3.8 \ --handler lambda_function.lambda_handler \ --role arn:aws:iam::123456789012:role/lambda-execution-role \ --zip-file fileb://function.zip
Trigger Lambda on S3 Upload
aws lambda add-permission \ --function-name PDFProcessor \ --statement-id s3-trigger \ --action "lambda:InvokeFunction" \ --principal s3.amazonaws.com \ --source-arn arn:aws:s3:::pdf-parsing-bucket
Configure S3 Event Notification
aws s3api put-bucket-notification-configuration \ --bucket pdf-parsing-bucket \ --notification-configuration file://notification.json
Example `notification.json`:
{
"LambdaFunctionConfigurations": [
{
"LambdaFunctionArn": "arn:aws:lambda:us-east-1:123456789012:function:PDFProcessor",
"Events": ["s3:ObjectCreated:"]
}
]
}
3. Python Lambda Code (Textract + DynamoDB)
import boto3
def lambda_handler(event, context):
s3 = boto3.client('s3')
textract = boto3.client('textract')
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('ExtractedText')
bucket = event['Records'][bash]['s3']['bucket']['name']
key = event['Records'][bash]['s3']['object']['key']
response = textract.start_document_text_detection(
DocumentLocation={'S3Object': {'Bucket': bucket, 'Name': key}}
)
job_id = response['JobId']
extracted_text = textract.get_document_text_detection(JobId=job_id)
table.put_item(
Item={
'DocumentID': key,
'Text': extracted_text
}
)
return {"status": "Processing completed"}
4. Verify DynamoDB Entry
aws dynamodb get-item \
--table-name ExtractedText \
--key '{"DocumentID": {"S": "example.pdf"}}'
What Undercode Say
This serverless approach eliminates infrastructure management while enabling scalable PDF processing. Key takeaways:
– Cost-Efficient: Pay only for actual usage.
– Scalable: Handles thousands of PDFs without manual intervention.
– Extendable: Can integrate with AI services like Comprehend for NLP.
For further learning, explore:
Prediction
Serverless document processing will dominate enterprise workflows, reducing reliance on manual data entry and legacy systems. Expect tighter integration with AI-driven analysis in future AWS updates.
Expected Output:
A fully automated PDF parsing system that stores extracted text in DynamoDB, triggered by S3 uploads.
IT/Security Reporter URL:
Reported By: Darryl Ruggles – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
