Serverless CRUD API for managing tasks, built with AWS CDK using Python. It leverages AWS services such as API Gateway, Lambda, and DynamoDB to provide a scalable and serverless backend for task management.
## Requisites:
- Node.js
- npm (latest)
- Python (3.x)
- AWS CLI (latest)
- AWS CDK (latest)
- AWS account profile with access key and secret key configured
## Attributes:
- API Gateway that exposes the REST API
- Lambda Functions that handle the CRUD operations
- DynamoDB Table to store task data
## How to deploy
```bash
git clone https://github.com/elrf3lipes/serverless_tasks_api
cd serverless_tasks_api
cdk bootstrap "aws://<account>/<region>"
cdk synth
cdk deploy- Create a task:
curl -X POST https://<api-url>/tasks \
-H "Content-Type: application/json" \
-d '{"title": "New Task", "description": "Task description", "status": "pending"}'- Get a task:
curl https://<api-url>/tasks/<taskId>- Update a task:
curl -X PUT https://<api-url>/tasks/<taskId> \
-H "Content-Type: application/json" \
-d '{"title": "Updated Task", "description": "This task has been updated", "status": "completed"}'- Delete a task:
curl -X DELETE https://<api-url>/tasks/<taskId>- Example of Curl commands and Responses:
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This project was aimed at gaining hands-on experience in developing applications that manage real-time data, automate processes, and generate insights from raw logs. My main goal was to apply modular development practices while enhancing my knowledge in automated deployment pipelines.
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- Modular Application Development
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Pubmed_Clinical_Trials_data_extraction_1 is a comprehensive data extraction and parsing tool that integrates with Pubmed and ClinicalTrials APIs. This project utilizes powerful Python libraries including Entrez, Medline, Biopython, Requests, urllib, xml.etree, IPython, and Pandas to streamline the process of fetching and processing clinical trials data.
- API Integration: Seamless integration with Pubmed and ClinicalTrials APIs to fetch relevant clinical trials data.
- Data Parsing: Efficient parsing of XML data using 'xml.etree'.
- Data Manipulation: Advanced data manipulation and analysis using Pandas.
- Interactive Environment: Utilizes IPython for an interactive coding experience.
- Biopython: For handling biological data.
- Requests: For making HTTP requests to APIs.
- urllib: For URL handling.
- xml.etree: For parsing XML data.
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- Pandas: For data manipulation and analysis.
- Entrez: For accessing NCBI databases.
- Medline: For parsing Medline records.
I decided to start on Upwork projects just for learning and then present them to the clients, the reason I made it public is because during its development, I encountered numerous posts from people looking for similar solutions. By sharing this tool, I hope to assist others in achieving the same goals of efficiently extracting pubmed and clinical trials research affiliations.
-
Clone the repository:
git clone https://github.com/elrf3lipes/Python_Automation_Projects.git -
Navigate to the project directory:
cd Python_Automation_Projects -
Open the Jupyter Notebook:
jupyter notebook Pubmed_Clinical_Trials_data_extraction_1.ipynb
Follow the instructions within the notebook to execute the data extraction and parsing processes.
This project aims to simplify the process of extracting the affiliation data from Pubmed and ClinicalTrials APIs. By leveraging powerful Python libraries, it provides a small solution for researchers and developers working in the field of clinical data analysis.
Note: The affiliation parser and keyword counter (extract_phrases_and_countries function) may encounter many limitations due to varying XML structures from PubMed. I planned to use the OpenAI API to improve parsing, but development stopped when my client went silent. Still, I hope this tool helps others looking for similar solutions!