🌌 Day 71 of #100DaysOfCode in Python: Navigating Data Streaming and Processing

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Welcome to Day 71! Today, we delve into the realms of data streaming and processing, crucial components in handling big data. With technologies like Kafka and Spark, Python developers can manage real-time data flows and perform complex processing at scale.

1. Introduction to Data Streaming

• Data Streaming: The continuous flow of data generated from various sources (e.g., sensors, user activities, transactions). Unlike batch processing, streaming data is processed incrementally on a record-by-record basis or over sliding time windows.
• Applications: Real-time analytics, monitoring, event-driven applications, etc.

2. Apache Kafka

• Overview: Kafka is a distributed streaming platform capable of handling trillions of events a day. It allows for publishing, subscribing to, storing, and processing streams of records in real-time.
• Key Concepts:
• Producer: Application that publishes (writes) events to Kafka topics.
• Consumer: Application that subscribes to (reads) events from Kafka topics.
• Topic: A category or feed to which records are published.
• Use with Python…