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Real-Time Data Pipeline with Apache Kafka, Apache Flink & PostgreSQL

This repository contains a complete, working example of a real-time data pipeline built using:

• Apache Kafka – Real-time message ingestion
• Apache Flink – Stream processing engine
• PostgreSQL – Persistent storage for processed results

This setup demonstrates how data flows from producers → Kafka → Flink → Kafka → PostgreSQL, forming an end-to-end real-time data engineering workflow.

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🚀 Architecture Overview

Producer → Kafka (input_topic)
        → Flink Stream Processor
        → Kafka (output_topic)
        → PostgreSQL Database → Analytics / Reporting

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📦 Tech Stack

• Apache Kafka (via Docker Compose)
• Apache Flink (via Docker or local setup)
• PostgreSQL (via Docker Compose)
• Java + Maven for Flink job
• Python for Kafka → PostgreSQL consumer

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✅ Prerequisites

Make sure you have installed:

• Docker & Docker Compose
• Java 8+
• Maven
• Python 3 (optional if using Python consumer)

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1️⃣ Setup Kafka + PostgreSQL (Docker Compose)

Create a docker-compose.yml file:

services:
  zookeeper:
    image: confluentinc/cp-zookeeper:latest
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181

  kafka:
    image: confluentinc/cp-kafka:latest
    ports:
      - "9092:9092"
    environment:
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://localhost:9092
      KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9092
      KAFKA_CREATE_TOPICS: "input_topic:1:1,output_topic:1:1"

  postgres:
    image: postgres:latest
    environment:
      POSTGRES_USER: user
      POSTGRES_PASSWORD: password
      POSTGRES_DB: streaming_db
    ports:
      - "5432:5432"

Start services:

docker-compose up -d

Verify containers:

docker ps

Create Kafka topics (optional if not auto-created)

docker exec -it <kafka-container-id> kafka-topics --create \
  --topic input_topic --bootstrap-server localhost:9092

docker exec -it <kafka-container-id> kafka-topics --create \
  --topic output_topic --bootstrap-server localhost:9092

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2️⃣ Run Apache Flink

Run Flink with Docker

Start JobManager:

docker run -it --rm -p 8081:8081 apache/flink:1.20 jobmanager

Start TaskManager:

docker run -it --rm apache/flink:1.20 taskmanager

Access Flink Dashboard:

➡️ http://localhost:8081

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3️⃣ Build & Deploy Flink Streaming Job

Create Maven project:

mvn archetype:generate \
  -DarchetypeGroupId=org.apache.flink \
  -DarchetypeArtifactId=flink-quickstart-java \
  -DarchetypeVersion=1.16.0 \
  -DgroupId=com.example \
  -DartifactId=flink-streaming-job

Update StreamingJob.java:

public class StreamingJob {
    public static void main(String[] args) throws Exception {
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers", "localhost:9092");
        properties.setProperty("group.id", "flink-group");

        FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>(
            "input_topic",
            new SimpleStringSchema(),
            properties
        );

        FlinkKafkaProducer<String> producer = new FlinkKafkaProducer<>(
            "output_topic",
            new SimpleStringSchema(),
            properties
        );

        env.addSource(consumer)
           .map(value -> "Processed: " + value)
           .addSink(producer);

        env.execute("Flink Kafka Streaming Job");
    }
}

Package the JAR:

mvn clean package

JAR output:

target/flink-streaming-job-1.0-SNAPSHOT.jar

Deploy to Flink Dashboard

1. Go to http://localhost:8081
2. Click Upload JAR
3. Select the built JAR
4. Run the job

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4️⃣ Store Processed Data in PostgreSQL

Create a table:

CREATE TABLE processed_data (
    id SERIAL PRIMARY KEY,
    value TEXT
);

Python Kafka → Postgres Consumer

from kafka import KafkaConsumer
import psycopg2

consumer = KafkaConsumer(
    'output_topic',
    bootstrap_servers='localhost:9092',
    auto_offset_reset='earliest',
    group_id='postgres-group'
)

conn = psycopg2.connect(
    database="streaming_db",
    user="user",
    password="password",
    host="localhost",
    port="5432"
)

cursor = conn.cursor()

for message in consumer:
    data = message.value.decode('utf-8')
    cursor.execute("INSERT INTO processed_data (value) VALUES (%s)", (data,))
    conn.commit()

cursor.close()
conn.close()

Run the consumer:

python consumer.py

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📊 Future Enhancements

• Add a Schema Registry for Kafka
• Deploy to AWS/MSK + Flink on ECS/EKS
• Integrate Grafana for real-time dashboards
• Use Flink windowing/aggregation joins
• Add machine learning inference inside Flink

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🏁 Conclusion

You now have a fully functional real-time streaming pipeline using:

✔ Kafka for ingestion ✔ Flink for processing ✔ PostgreSQL for storage

This project is a great starting point for real-time analytics, event-driven systems, log processing, and modern data engineering platforms.

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⭐ Star This Repository

If this project helped you, please ⭐ the repo to support future work!

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