Gedson silva

[Schlotged]

No description provided.

[andre-ghislandi-dynamox]

@Schlotged , thanks for your PR submission! Your implementation has been evaluated and our team will reach out to you regarding the next steps. Below you can find the detailed feedback on your solution.

Challenge 1: Signal Processing API - Evaluation

Functional Requirements and User Stories

1. As a user, I want to be able to store a raw data series.

2. As a user, I want to be able to retrieve metrics about the time series.

3. As a user, I want to be able to delete a time series I've sent to the server.

4. As a user, I want to be able to retrieve the number of time series I've stored in the server.

5. As a user, I want to be able to retrieve a full time series I've stored.

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Technical Requirements

1. Use Python

2. Use a REST-API framework (ex.: FastAPI)

3. The latency between client and the server side must be below 350ms in all requests

4. Use a database to store the time series data

5. Ensure correct business logic and behavior with automated unit tests (ex.: pytest)

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Bonus

1. Deploy your application to a cloud provider and provide the API URL.

2. Implement a functionality that gives me a future prediction of the time series data.

3. Add load balancer to the application.

4. Add load tests to the application.

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Evaluation Criteria

1. Anyone should be able to follow the instructions and run the application.

   /alembic.ini and /alembic/env.py are empty files committed to the repo — dead artifacts since the project uses Tortoise's generate_schemas=True instead of Alembic migrations.
   README is written in Portuguese while the challenge spec is in English

2. Back-end code successfully integrated with persistent storage.

3. Stories were implemented according to the functional requirements.

4. Problem-solving skills and ability to handle ambiguity.

   Good decisions: repository pattern, schema validation with Pydantic, edge case handling (404, 422, empty data, duplicate conflict handling), linear regression as a prediction approach.

5. Code quality, readability, and maintainability.

   Clean project structure: routers/, services/, repositories/, schemas/, models/, core/.

   No docstrings in the Python code.

6. Code is well-organized and documented.