This project demonstrates a serverless architecture on AWS, integrating S3, Lambda, and DynamoDB for efficient text data processing. By leveraging AWS services, the solution sets a standard for cloud-based architecture.
- Amazon S3
- AWS Lambda
- Amazon DynamoDB
- AWS Development
- NLP Tokenization
- Python
- Server
- Natural Language Processing
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S3 Bucket Setup
- Created "sample-data-B00925445" bucket with proper configuration and security settings.
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Lambda Functions
- Implemented "extractFeatures" to process Named Entities and generate JSON arrays.
- Utilized "AccessDB" to update DynamoDB when new files are added to the "tags-B00925445" bucket.
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DynamoDB Integration
- Stored Named Entity data with key-value pairs, enhancing data management practices.
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Comprehensive Testing
- Rigorous testing for file upload, content validation, and successful processing and storage.
Execute thorough test cases to ensure functional integrity.
By adopting AWS Lambda, S3, and DynamoDB, this serverless architecture ensures efficient, scalable, and cost-effective processing of Named Entity data, reflecting seamless integration for a cohesive and reliable event-driven workflow.