Microservices

Microservices are a software architecture pattern that breaks down a large monolithic application into smaller, independently deployable services that communicate with each other using APIs.

As a data analyst, understanding the concept of microservices can be useful when working with data-driven applications. Microservices make it easier to build, deploy, and maintain data-driven applications by isolating parts of the application into smaller, manageable services.

In a microservices architecture, each service has its own codebase, data storage, and dependencies. This makes it easier to update and deploy individual services without affecting the rest of the application. It allows flexibility in choosing different technologies for different services, depending on their requirements.

Microservices can be particularly useful for real-time processing and analysis of large volumes of data. By breaking down an application into smaller services, developers can optimize each service for its specific task, allowing more efficient processing and analysis.

Working with microservices requires additional skills and knowledge, including understanding APIs, containerization, and service discovery.

A solid foundation in programming and software development is required to work effectively with microservices-based applications.

Sentiment Analysis - Flask

Create a new Flask application.

python3 -m venv env
source env/bin/activate
pip install flask
pip install textblob

Create a new route in app.py.

from flask import Flask, request
from textblob import TextBlob

app = Flask(__name__)

@app.route('/sentiment', methods=['POST'])
def sentiment():
    text = request.json['text']
    blob = TextBlob(text)
    polarity = blob.sentiment.polarity
    subjectivity = blob.sentiment.subjectivity
    return {'polarity': polarity, 'subjectivity': subjectivity}

if __name__ == '__main__':
    app.run(debug=True)

Run the app with the following command.

python app.py

Test the API with curl (or Postman).

curl --header "Content-Type: application/json" --request POST --data '{"text":"This is a positive sentence."}' http://localhost:5000/sentiment

Sentiment Analysis - AWS Lambda

Alternatively, we could create a simple function and host it on Amazon Web Services (AWS) Lambda. AWS offers a free tier that allows up to one million requests per month.