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Mohammad Waseem
Mohammad Waseem

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Transforming Dirty Data with Open Source API Solutions

#api #python #opensource

Cleaning Dirty Data Using API Development with Open Source Tools

Data quality is a persistent challenge in any data-driven organization. Dirty or inconsistent data hampers analytics, skews reports, and leads to poor decision-making. As a Lead QA Engineer, I’ve leveraged API development combined with open-source tools to automate the process of cleaning and standardizing data efficiently.

Understanding the Problem

Dirty data often includes missing values, inconsistent formats, duplicates, and erroneous entries. Traditional ETL pipelines can handle some aspects, but real-time data cleansing at the API level offers flexibility and immediate integration into workflows.

Choosing the Right Tools

For this approach, I rely heavily on Python-based open-source tools:

  • FastAPI: For building performant RESTful APIs
  • pandas: For data manipulation
  • SQLAlchemy: To interface with databases
  • Docker: For containerizing the API

Designing the API

The core idea is to develop an API endpoint that accepts raw data, processes it, and returns cleaned data. Here's a simplified example:

from fastapi import FastAPI
from pydantic import BaseModel
import pandas as pd
import io

app = FastAPI()

class DataInput(BaseModel):
    csv_data: str  # Expect CSV data as string

@app.post("/clean-data")
async def clean_data(data: DataInput):
    # Read CSV data into pandas DataFrame
    df = pd.read_csv(io.StringIO(data.csv_data))

    # Perform cleaning steps
    # Drop duplicates
    df.drop_duplicates(inplace=True)
    # Fill missing values
    df.fillna(method='ffill', inplace=True)
    # Standardize string data
    for col in df.select_dtypes(include=['object']).columns:
        df[col] = df[col].str.strip().str.lower()

    # Convert cleaned data back to CSV
    output = io.StringIO()
    df.to_csv(output, index=False)
    return {
        "cleaned_csv": output.getvalue()
    }
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This API accepts raw CSV data as a string, performs common cleaning operations: duplication removal, missing value imputation, and text normalization.

Deployment & Usage

Containerize the API with Docker for portability:

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
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Build and run:

docker build -t data-cleaner-api .
docker run -d -p 8000:8000 data-cleaner-api
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Clients can send POST requests with raw data:

curl -X POST "http://localhost:8000/clean-data" -H "Content-Type: application/json" -d '{"csv_data": "name,age\nJohn,30\nJane,,25\nJohn,30"}'
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Advantages of API-Based Cleaning

  • Scalability: Can process large datasets asynchronously
  • Integration: Easily connect with existing systems
  • Automation: Run cleaning routines as part of CI/CD pipelines or data ingestion workflows
  • Open Source: Use community-supported tools for customization and extension

Final Thoughts

Developing a robust, open-source-powered API for data cleaning streamlines data workflows and improves data integrity. It acts as a middleware layer, ensuring incoming data is validated and standardized before reaching storage or analytics platforms, reducing errors downstream.

By embracing this API-driven approach, QA and Data teams can collaboratively improve data quality, making analytics more reliable and actionable.

🛠️ QA Tip

I rely on TempoMail USA to keep my test environments clean.

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