Introduction to Distributed Streaming

1. Overview

Distributed Streaming refers to the processing and transmission of data streams across multiple servers or nodes in a network. It is essential for real-time analytics, event processing, and managing high-velocity data.

2. Key Concepts

• **Stream**: A continuous flow of data generated by events.
• **Message Broker**: A system that facilitates the exchange of messages between producers and consumers.
• **Consumer**: An application or service that receives and processes data from a stream.
• **Producer**: An application or service that generates data and sends it to a stream.

3. Distributed Streaming Architecture

A typical architecture includes:

graph TD;
                A[Producer] --> B[Message Broker];
                B --> C[Consumer];
                B --> D[Consumer];
            

In this diagram, producers send messages to a message broker, which routes messages to multiple consumers.

4. Popular Platforms

Several platforms are widely used for distributed streaming:

• Apache Kafka
• Apache Pulsar
• Amazon Kinesis
• Google Cloud Pub/Sub

5. Best Practices

• Use partitioning to scale out consumers.
• Implement data serialization for efficient message passing.
• Ensure message durability by configuring retention policies.
• Monitor latency and throughput continuously.

6. FAQs