{"schemaVersion":"1.0","type":"Article","types":["Article"],"slug":"kafka-101-why-event-streaming-is-the-central-nervous-system-of-modern-data-r4c5r","url":"https://api.zyvop.com/kafka-101-why-event-streaming-is-the-central-nervous-system-of-modern-data-r4c5r","title":"Kafka 101: Why Event Streaming is the Central Nervous System of Modern Data","subtitle":null,"tldr":"Digging into a new universe of data streaming after IBM’s recent accquisition of...","keywords":["Kafka","Software","datastreaming","confluent","bob"],"entities":["Alain Airom (Ayrom)","Build Engineer","Kafka","Software","datastreaming","confluent","bob","ZyVOP"],"keyTakeaways":["Digging into a new universe of data streaming after IBM’s recent accquisition of Confluent!","Introduction Data is no longer a static asset — it’s a live pulse.","Following IBM’s $11B acquisition of Confluent, the bridge between real-time streaming and enterprise AI has never been shorter."],"headings":["The Producer: Capturing the Event","The Consumer: Decoupled Processing","The Cluster: Ensuring Reliability","The Monitor: Real-Time Observability","Links"],"outboundLinks":["https://github.com/aairom/kafka-101","https://github.com/apache/kafka","https://www.confluent.io/what-is-apache-kafka/","https://www.confluent.io/","https://www.confluent.io/product/confluent-platform/","https://www.confluent.io/confluent-cloud/"],"contentText":"Digging into a new universe of data streaming after IBM’s recent accquisition of Confluent! Introduction Data is no longer a static asset — it’s a live pulse. Following IBM’s $11B acquisition of Confluent, the bridge between real-time streaming and enterprise AI has never been shorter. I’m diving into the new universes of data-in-motion to see how this ‘Smart Data’ foundation is changing the game for hybrid cloud and agentic AI. 🚀 In the traditional world of data, systems often talk to each other like a game of “telephone” — one service calls another, which calls another, creating a fragile web of dependencies. If one system goes down or slows down, the entire chain breaks. Event streaming changes this paradigm by turning data into a continuous flow of events. Apache Kafka acts as the “central nervous system” for this flow. Instead of direct connections, services publish events to a central cluster, and any interested service can “tune in” to listen. This decoupling allows businesses to react to customer actions in real-time — whether that’s processing a payment, updating a live dashboard, or triggering a delivery — without the systems ever needing to know each other exists. Implementation: Building an E-commerce Stream Using two basic code samples I found on the net, one as a “producer” and one as a “consumer” to explain the concepts of Kafka, I asked Bob to build a simple comprehensive application. Simple Producer from confluent_kafka import Producerimport json # Configuration for connecting to the Kafka cluster config = {'bootstrap.servers': 'localhost:9092'}producer = Producer(config) def delivery_report(err, msg): if err is not None: print(f\"Message delivery failed: {err}\") else: print(f\"Order sent to {msg.topic()} [{msg.partition()}]\") # Simulate an order order_data = { \"order_id\": 1001, \"user\": \"jane_doe\", \"total\": 59.99, \"items\": [\"Wireless Mouse\", \"Keyboard\"]} # Trigger the send (Asynchronous) producer.produce( 'orders', key=\"1001\", value=json.dumps(order_data), callback=delivery_report) producer.flush() # Wait for any outstanding messages to be delivered Enter fullscreen mode Exit fullscreen mode Simple Consumer from confluent_kafka import Consumer config = { 'bootstrap.servers': 'localhost:9092', 'group.id': 'shipping-group', # Helps Kafka track which orders this group has seen 'auto.offset.reset': 'earliest'} consumer = Consumer(config)consumer.subscribe(['orders']) try: while True: msg = consumer.poll(1.0) # Check for new messages every 1 second if msg is None: continue if msg.error(): print(f\"Consumer error: {msg.error()}\") continue print(f\"Received Order: {msg.value().decode('utf-8')}\") print(\"Action: Initiating packaging and shipping...\")finally: consumer.close()Enter fullscreen mode Exit fullscreen mode After reading my conceptual samples, Bob provided the application structure which follows. The Producer: Capturing the Event The Producer (found in src/producer.py) acts as the storefront. When an order is placed, it creates a JSON-serializable dictionary containing the order ID, user details, and items. It uses the confluent-kafka library to \"produce\" this message to a topic called orders, using the order_id as a key to ensure all updates for a specific order land in the same partition. A delivery callback function is utilized to provide immediate feedback on whether the message successfully reached the Kafka broker or if a retry is necessary. \"\"\" Kafka Producer - E-commerce Order Processing System Simulates an online store sending order events to Kafka \"\"\" from confluent_kafka import Producerimport jsonimport timeimport randomfrom datetime import datetime # Configuration for connecting to the Kafka cluster config = { 'bootstrap.servers': 'localhost:9092', 'client.id': 'ecommerce-producer'} producer = Producer(config) def delivery_report(err, msg): \"\"\" Callback function called once for each message produced to indicate delivery result. Triggered by poll() or flush(). \"\"\" if err is not None: print(f\"❌ Message delivery failed: {err}\") else: print(f\"✅ Order sent to topic '{msg.topic()}' [partition {msg.partition()}] at offset {msg.offset()}\") # Sample product catalog PRODUCTS = [ {\"name\": \"Wireless Mouse\", \"price\": 29.99}, {\"name\": \"Mechanical Keyboard\", \"price\": 89.99}, {\"name\": \"USB-C Cable\", \"price\": 12.99}, {\"name\": \"Laptop Stand\", \"price\": 45.00}, {\"name\": \"Webcam HD\", \"price\": 79.99}, {\"name\": \"Headphones\", \"price\": 149.99}, {\"name\": \"Monitor 27\\\"\", \"price\": 299.99}, {\"name\": \"Desk Lamp\", \"price\": 34.99}] USERS = [\"alice_smith\", \"bob_jones\", \"charlie_brown\", \"diana_prince\", \"eve_adams\"] def generate_order(order_id): \"\"\"Generate a random order\"\"\" num_items = random.randint(1, 4) items = random.sample(PRODUCTS, num_items) order_data = { \"order_id\": order_id, \"user\": random.choice(USERS), \"timestamp\": datetime.now().isoformat(), \"items\": [item[\"name\"] for item in items], \"total\": round(sum(item[\"price\"] for item in items), 2), \"status\": \"pending\" } return order_data def produce_orders(num_orders=10, delay=2): \"\"\" Produce multiple orders to Kafka Args: num_orders: Number of orders to generate delay: Delay in seconds between orders \"\"\" print(f\"🚀 Starting E-commerce Order Producer\") print(f\"📊 Will produce {num_orders} orders with {delay}s delay between each\") print(f\"🔗 Connected to Kafka at: {config['bootstrap.servers']}\") print(\"-\" * 70) for i in range(1, num_orders + 1): try: # Generate order order_data = generate_order(1000 + i) # Print order details print(f\"\\n📦 Order #{order_data['order_id']}:\") print(f\" User: {order_data['user']}\") print(f\" Items: {', '.join(order_data['items'])}\") print(f\" Total: ${order_data['total']}\") # Produce message to Kafka producer.produce( topic='orders', key=str(order_data['order_id']), value=json.dumps(order_data), callback=delivery_report ) # Trigger delivery reports by polling producer.poll(0) # Wait before next order if i < num_orders: time.sleep(delay) except KeyboardInterrupt: print(\"\\n⚠️ Producer interrupted by user\") break except Exception as e: print(f\"❌ Error producing message: {e}\") # Wait for all messages to be delivered print(\"\\n⏳ Flushing remaining messages...\") producer.flush() print(\"✅ All orders sent successfully!\") if __name__ == \"__main__\": import sys # Parse command line arguments num_orders = int(sys.argv[1]) if len(sys.argv) > 1 else 10 delay = float(sys.argv[2]) if len(sys.argv) > 2 else 2 try: produce_orders(num_orders, delay) except KeyboardInterrupt: print(\"\\n👋 Producer stopped\") except Exception as e: print(f\"❌ Fatal error: {e}\") # Made with Bob Enter fullscreen mode Exit fullscreen mode The Consumer: Decoupled Processing The Consumer logic (found in src/consumer.py) demonstrates the power of Consumer Groups. Multiple independent services—Shipping, Email, and Analytics—all subscribe to the same orders topic simultaneously. Because each service belongs to its own group (e.g., shipping-group), Kafka tracks their progress (offsets) individually. This means the Shipping department can process messages at its own pace without affecting the speed of the Email service or the Analytics dashboard. \"\"\" Kafka Consumer - E-commerce Order Processing System Simulates different departments (Shipping, Email, Analytics) consuming order events \"\"\" from confluent_kafka import Consumer, KafkaErrorimport jsonimport sysfrom datetime import datetime def create_consumer(group_id, bootstrap_servers='localhost:9092'): \"\"\" Create and configure a Kafka consumer Args: group_id: Consumer group identifier bootstrap_servers: Kafka broker addresses Returns: Configured Consumer instance \"\"\" config = { 'bootstrap.servers': bootstrap_servers, 'group.id': group_id, 'auto.offset.reset': 'earliest', # Start from beginning if no offset exists 'enable.auto.commit': True, 'auto.commit.interval.ms': 1000, 'session.timeout.ms': 6000, 'client.id': f'{group_id}-client' } return Consumer(config) def process_shipping(order): \"\"\"Process order for shipping department\"\"\" print(f\"\\n📦 SHIPPING DEPARTMENT\") print(f\" Order ID: {order['order_id']}\") print(f\" Customer: {order['user']}\") print(f\" Items to pack: {', '.join(order['items'])}\") print(f\" ✅ Initiating packaging and shipping process...\") def process_email(order): \"\"\"Process order for email service\"\"\" print(f\"\\n📧 EMAIL SERVICE\") print(f\" Order ID: {order['order_id']}\") print(f\" Recipient: {order['user']}\") print(f\" Total: ${order['total']}\") print(f\" ✅ Sending order confirmation email...\") def process_analytics(order): \"\"\"Process order for analytics dashboard\"\"\" print(f\"\\n📊 ANALYTICS DASHBOARD\") print(f\" Order ID: {order['order_id']}\") print(f\" Revenue: ${order['total']}\") print(f\" Items count: {len(order['items'])}\") print(f\" Timestamp: {order['timestamp']}\") print(f\" ✅ Updating live sales dashboard...\") # Department processors mapping PROCESSORS = { 'shipping-group': process_shipping, 'email-group': process_email, 'analytics-group': process_analytics} def consume_orders(group_id, topic='orders'): \"\"\" Consume orders from Kafka topic Args: group_id: Consumer group (shipping-group, email-group, analytics-group) topic: Kafka topic to subscribe to \"\"\" consumer = create_consumer(group_id) consumer.subscribe([topic]) processor = PROCESSORS.get(group_id, process_shipping) department = group_id.replace('-group', '').upper() print(f\"🚀 Starting {department} Consumer\") print(f\"👥 Consumer Group: {group_id}\") print(f\"📋 Subscribed to topic: {topic}\") print(f\"🔗 Connected to Kafka at: localhost:9092\") print(\"-\" * 70) print(\"⏳ Waiting for messages... (Press Ctrl+C to stop)\\n\") try: message_count = 0 while True: # Poll for messages (timeout in seconds) msg = consumer.poll(timeout=1.0) if msg is None: continue if msg.error(): if msg.error().code() == KafkaError._PARTITION_EOF: # End of partition event print(f\"📍 Reached end of partition {msg.partition()}\") else: print(f\"❌ Consumer error: {msg.error()}\") continue # Process the message try: message_count += 1 order_data = json.loads(msg.value().decode('utf-8')) print(f\"\\n{'='*70}\") print(f\"📨 Message #{message_count} received from partition {msg.partition()} at offset {msg.offset()}\") # Call the appropriate processor processor(order_data) print(f\"{'='*70}\") except json.JSONDecodeError as e: print(f\"❌ Failed to decode message: {e}\") except Exception as e: print(f\"❌ Error processing message: {e}\") except KeyboardInterrupt: print(f\"\\n\\n⚠️ Consumer interrupted by user\") print(f\"📊 Total messages processed: {message_count}\") finally: # Close the consumer to commit final offsets print(\"🔒 Closing consumer...\") consumer.close() print(\"✅ Consumer closed successfully\") if __name__ == \"__main__\": # Parse command line arguments if len(sys.argv) < 2: print(\"Usage: python consumer.py <group_id> [topic]\") print(\"\\nAvailable consumer groups:\") print(\" - shipping-group : Processes orders for shipping\") print(\" - email-group : Sends confirmation emails\") print(\" - analytics-group : Updates analytics dashboard\") print(\"\\nExample: python consumer.py shipping-group\") sys.exit(1) group_id = sys.argv[1] topic = sys.argv[2] if len(sys.argv) > 2 else 'orders' if group_id not in PROCESSORS: print(f\"⚠️ Warning: Unknown group_id '{group_id}'. Using default processor.\") try: consume_orders(group_id, topic) except Exception as e: print(f\"❌ Fatal error: {e}\") sys.exit(1) # Made with Bob Enter fullscreen mode Exit fullscreen mode The Cluster: Ensuring Reliability The heart of the system is the Broker Kafka Cluster (defined in docker-compose.yml and [Architecture.md](https://github.com/aairom/kafka-101/blob/main/Docs/Architecture.md)). By running three brokers coordinated by Zookeeper, the architecture ensures High Availability. Each data partition is replicated across at least two brokers; if one broker fails, another automatically takes over as the \"leader,\" ensuring that no orders are lost and the system remains online. This fault tolerance is what makes Kafka \"production-ready\" compared to simple message queues. version: '3.8' services:zookeeper: image: confluentinc/cp-zookeeper:7.5.0 hostname: zookeeper container_name: zookeeper ports: - \"2181:2181\" environment: ZOOKEEPER_CLIENT_PORT: 2181 ZOOKEEPER_TICK_TIME: 2000 volumes: - zookeeper-data:/var/lib/zookeeper/data - zookeeper-logs:/var/lib/zookeeper/log kafka-broker-1: image: confluentinc/cp-kafka:7.5.0 hostname: kafka-broker-1 container_name: kafka-broker-1 depends_on: - zookeeper ports: - \"9092:9092\" - \"19092:19092\" environment: KAFKA_BROKER_ID: 1 KAFKA_ZOOKEEPER_CONNECT: 'zookeeper:2181' KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka-broker-1:29092,PLAINTEXT_HOST://localhost:9092 KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 2 KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 2 KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 2 KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0 KAFKA_AUTO_CREATE_TOPICS_ENABLE: 'true' KAFKA_JMX_PORT: 9101 KAFKA_JMX_HOSTNAME: localhost volumes: - kafka-broker-1-data:/var/lib/kafka/data kafka-broker-2: image: confluentinc/cp-kafka:7.5.0 hostname: kafka-broker-2 container_name: kafka-broker-2 depends_on: - zookeeper ports: - \"9093:9093\" - \"19093:19093\" environment: KAFKA_BROKER_ID: 2 KAFKA_ZOOKEEPER_CONNECT: 'zookeeper:2181' KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka-broker-2:29093,PLAINTEXT_HOST://localhost:9093 KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 2 KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 2 KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 2 KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0 KAFKA_AUTO_CREATE_TOPICS_ENABLE: 'true' KAFKA_JMX_PORT: 9102 KAFKA_JMX_HOSTNAME: localhost volumes: - kafka-broker-2-data:/var/lib/kafka/data kafka-broker-3: image: confluentinc/cp-kafka:7.5.0 hostname: kafka-broker-3 container_name: kafka-broker-3 depends_on: - zookeeper ports: - \"9094:9094\" - \"19094:19094\" environment: KAFKA_BROKER_ID: 3 KAFKA_ZOOKEEPER_CONNECT: 'zookeeper:2181' KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka-broker-3:29094,PLAINTEXT_HOST://localhost:9094 KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 2 KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 2 KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 2 KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0 KAFKA_AUTO_CREATE_TOPICS_ENABLE: 'true' KAFKA_JMX_PORT: 9103 KAFKA_JMX_HOSTNAME: localhost volumes: - kafka-broker-3-data:/var/lib/kafka/data volumes:zookeeper-data:zookeeper-logs:kafka-broker-1-data:kafka-broker-2-data:kafka-broker-3-data: # Made with BobEnter fullscreen mode Exit fullscreen mode The Monitor: Real-Time Observability To manage this distributed flow, the Monitor Runner (monitor_runner.py) provides a unified view of the entire ecosystem. It orchestrates the lifecycle of the producer and all consumer threads while capturing their standard output into a web-based dashboard. This allows developers to see the \"path of a message\" in real-time: from the moment the Producer sends Order #1001 to the moment all three consumer groups acknowledge and process it. \"\"\" Kafka Monitor Web Server Provides a web interface to monitor producer and consumer outputs in real-time \"\"\" import asyncioimport jsonimport subprocessimport threadingfrom datetime import datetimefrom pathlib import Pathfrom http.server import HTTPServer, SimpleHTTPRequestHandlerimport socketserver class TerminalMonitor: \"\"\"Monitor terminal outputs and store them\"\"\" def __init__(self): self.outputs = { 'producer': [], 'shipping': [], 'email': [], 'analytics': [] } self.max_lines = 1000 # Keep last 1000 lines per terminal def add_output(self, terminal_id, line): \"\"\"Add a line to terminal output\"\"\" timestamp = datetime.now().strftime('%H:%M:%S') entry = { 'timestamp': timestamp, 'line': line } if terminal_id in self.outputs: self.outputs[terminal_id].append(entry) # Keep only last max_lines if len(self.outputs[terminal_id]) > self.max_lines: self.outputs[terminal_id] = self.outputs[terminal_id][-self.max_lines:] def get_outputs(self): \"\"\"Get all terminal outputs\"\"\" return self.outputs def clear_terminal(self, terminal_id): \"\"\"Clear a specific terminal\"\"\" if terminal_id in self.outputs: self.outputs[terminal_id] = [] def clear_all(self): \"\"\"Clear all terminals\"\"\" for terminal_id in self.outputs: self.outputs[terminal_id] = [] # Global monitor instance monitor = TerminalMonitor() class MonitorRequestHandler(SimpleHTTPRequestHandler): \"\"\"Custom HTTP request handler\"\"\" def do_GET(self): \"\"\"Handle GET requests\"\"\" if self.path == '/': self.path = '/monitor.html' elif self.path == '/api/outputs': self.send_response(200) self.send_header('Content-type', 'application/json') self.send_header('Access-Control-Allow-Origin', '*') self.end_headers() self.wfile.write(json.dumps(monitor.get_outputs()).encode()) return elif self.path == '/api/clear': monitor.clear_all() self.send_response(200) self.send_header('Content-type', 'application/json') self.end_headers() self.wfile.write(json.dumps({'status': 'cleared'}).encode()) return return SimpleHTTPRequestHandler.do_GET(self) def log_message(self, format, *args): \"\"\"Suppress default logging\"\"\" pass def run_command_and_monitor(command, terminal_id, cwd=None): \"\"\"Run a command and monitor its output\"\"\" try: process = subprocess.Popen( command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, bufsize=1, cwd=cwd, shell=True ) monitor.add_output(terminal_id, f\"🚀 Starting: {command}\") for line in iter(process.stdout.readline, ''): if line: monitor.add_output(terminal_id, line.rstrip()) process.wait() monitor.add_output(terminal_id, f\"✅ Process completed with exit code: {process.returncode}\") except Exception as e: monitor.add_output(terminal_id, f\"❌ Error: {str(e)}\") def start_web_server(port=8080): \"\"\"Start the web server\"\"\" handler = MonitorRequestHandler with socketserver.TCPServer((\"\", port), handler) as httpd: print(f\"🌐 Web Monitor Server running at http://localhost:{port}\") print(f\"📊 Open your browser to view the dashboard\") print(f\"Press Ctrl+C to stop\") httpd.serve_forever() def main(): \"\"\"Main function\"\"\" import sys if len(sys.argv) > 1 and sys.argv[1] == 'server': # Just run the web server port = int(sys.argv[2]) if len(sys.argv) > 2 else 8080 start_web_server(port) else: print(\"Usage:\") print(\" python3 src/web_monitor.py server [port]\") print(\"\\nExample:\") print(\" python3 src/web_monitor.py server 8080\") if __name__ == \"__main__\": main() # Made with Bob Enter fullscreen mode Exit fullscreen mode ┌─────────────────────────────────────────────────────────────┐ │ ⚡ Kafka Cluster Monitor [Live] [🔄] [🗑️] │ ├──────────────────────────────┬──────────────────────────────┤ │ 📤 Producer │ 📦 Shipping Consumer │ │ [0 lines] │ [0 lines] │ │ │ │ │ [09:30:15] 🚀 Starting... │ [09:30:17] 🚀 Starting... │ │ [09:30:16] Order #1001 │ [09:30:18] Received Order │ │ [09:30:16] ✅ Sent │ [09:30:18] ✅ Processing │ │ │ │ ├──────────────────────────────┼──────────────────────────────┤ │ 📧 Email Consumer │ 📊 Analytics Consumer │ │ [0 lines] │ [0 lines] │ │ │ │ │ [09:30:17] 🚀 Starting... │ [09:30:17] 🚀 Starting... │ │ [09:30:18] Received Order │ [09:30:18] Received Order │ │ [09:30:18] ✅ Email sent │ [09:30:18] ✅ Updated │ │ │ │ └──────────────────────────────┴──────────────────────────────┘ │ Last updated: 09:30:20 Auto-scroll: ON │ └─────────────────────────────────────────────────────────────┘ Enter fullscreen mode Exit fullscreen mode Alongside with the monitor, we can also monitor the events on the console output. ./scripts/run-producer.sh 10 2 🚀 Starting Kafka Producer ========================= 📊 Configuration: - Number of orders: 10 - Delay between orders: 2s 🚀 Starting E-commerce Order Producer 📊 Will produce 10 orders with 2.0s delay between each 🔗 Connected to Kafka at: localhost:9092 ---------------------------------------------------------------------- 📦 Order #1001: User: bob_jones Items: USB-C Cable, Mechanical Keyboard, Desk Lamp Total: $137.97 📦 Order #1002: User: diana_prince Items: Wireless Mouse Total: $29.99 ✅ Order sent to topic 'orders' [partition 0] at offset 0 📦 Order #1003: User: bob_jones Items: USB-C Cable Total: $12.99 ✅ Order sent to topic 'orders' [partition 0] at offset 1 📦 Order #1004: User: bob_jones Items: Mechanical Keyboard, Headphones, Webcam HD Total: $319.97 ✅ Order sent to topic 'orders' [partition 0] at offset 2 📦 Order #1005: User: diana_prince Items: Laptop Stand, Monitor 27\", Wireless Mouse, Webcam HD Total: $454.97 ✅ Order sent to topic 'orders' [partition 0] at offset 3 📦 Order #1006: User: diana_prince Items: Headphones, Laptop Stand Total: $194.99 ✅ Order sent to topic 'orders' [partition 0] at offset 4 📦 Order #1007: User: charlie_brown Items: Laptop Stand, Headphones, Wireless Mouse Total: $224.98 ✅ Order sent to topic 'orders' [partition 0] at offset 5 📦 Order #1008: User: charlie_brown Items: Laptop Stand Total: $45.0 ✅ Order sent to topic 'orders' [partition 0] at offset 6 📦 Order #1009: User: alice_smith Items: Mechanical Keyboard, Desk Lamp Total: $124.98 ✅ Order sent to topic 'orders' [partition 0] at offset 7 📦 Order #1010: User: eve_adams Items: Wireless Mouse Total: $29.99 ✅ Order sent to topic 'orders' [partition 0] at offset 8 ⏳ Flushing remaining messages... ✅ Order sent to topic 'orders' [partition 0] at offset 9 ✅ All orders sent successfully! ✅ Producer finished Enter fullscreen mode Exit fullscreen mode ./scripts/run-consumer.sh shipping-group 🚀 Starting Kafka Consumer ========================== 📊 Configuration: - Consumer Group: shipping-group - Topic: orders 🚀 Starting SHIPPING Consumer 👥 Consumer Group: shipping-group 📋 Subscribed to topic: orders 🔗 Connected to Kafka at: localhost:9092 ---------------------------------------------------------------------- ⏳ Waiting for messages... (Press Ctrl+C to stop)====================================================================== 📨 Message #1 received from partition 0 at offset 0 📦 SHIPPING DEPARTMENT Order ID: 1001 Customer: bob_jones Items to pack: USB-C Cable, Mechanical Keyboard, Desk Lamp ✅ Initiating packaging and shipping process... ====================================================================== ====================================================================== 📨 Message #2 received from partition 0 at offset 1 📦 SHIPPING DEPARTMENT Order ID: 1002 Customer: diana_prince Items to pack: Wireless Mouse ✅ Initiating packaging and shipping process... ====================================================================== ====================================================================== 📨 Message #3 received from partition 0 at offset 2 📦 SHIPPING DEPARTMENT Order ID: 1003 Customer: bob_jones Items to pack: USB-C Cable ✅ Initiating packaging and shipping process... ====================================================================== ====================================================================== 📨 Message #4 received from partition 0 at offset 3 📦 SHIPPING DEPARTMENT Order ID: 1004 Customer: bob_jones Items to pack: Mechanical Keyboard, Headphones, Webcam HD ✅ Initiating packaging and shipping process... ====================================================================== ====================================================================== 📨 Message #5 received from partition 0 at offset 4 📦 SHIPPING DEPARTMENT Order ID: 1005 Customer: diana_prince Items to pack: Laptop Stand, Monitor 27\", Wireless Mouse, Webcam HD ✅ Initiating packaging and shipping process... ====================================================================== ====================================================================== 📨 Message #6 received from partition 0 at offset 5 📦 SHIPPING DEPARTMENT Order ID: 1006 Customer: diana_prince Items to pack: Headphones, Laptop Stand ✅ Initiating packaging and shipping process... ====================================================================== ====================================================================== 📨 Message #7 received from partition 0 at offset 6 📦 SHIPPING DEPARTMENT Order ID: 1007 Customer: charlie_brown Items to pack: Laptop Stand, Headphones, Wireless Mouse ✅ Initiating packaging and shipping process... ====================================================================== ====================================================================== 📨 Message #8 received from partition 0 at offset 7 📦 SHIPPING DEPARTMENT Order ID: 1008 Customer: charlie_brown Items to pack: Laptop Stand ✅ Initiating packaging and shipping process... ====================================================================== ====================================================================== 📨 Message #9 received from partition 0 at offset 8 📦 SHIPPING DEPARTMENT Order ID: 1009 Customer: alice_smith Items to pack: Mechanical Keyboard, Desk Lamp ✅ Initiating packaging and shipping process... ====================================================================== ====================================================================== 📨 Message #10 received from partition 0 at offset 9 📦 SHIPPING DEPARTMENT Order ID: 1010 Customer: eve_adams Items to pack: Wireless Mouse ✅ Initiating packaging and shipping process... ====================================================================== Enter fullscreen mode Exit fullscreen mode /scripts/run-consumer.sh email-group zsh: no such file or directory: /scripts/run-consumer.sh > ./scripts/run-consumer.sh email-group🚀 Starting Kafka Consumer ========================== 📊 Configuration: - Consumer Group: email-group - Topic: orders 🚀 Starting EMAIL Consumer 👥 Consumer Group: email-group 📋 Subscribed to topic: orders 🔗 Connected to Kafka at: localhost:9092 ---------------------------------------------------------------------- ⏳ Waiting for messages... (Press Ctrl+C to stop)====================================================================== 📨 Message #1 received from partition 0 at offset 0 📧 EMAIL SERVICE Order ID: 1001 Recipient: bob_jones Total: $137.97 ✅ Sending order confirmation email... ====================================================================== ====================================================================== 📨 Message #2 received from partition 0 at offset 1 📧 EMAIL SERVICE Order ID: 1002 Recipient: diana_prince Total: $29.99 ✅ Sending order confirmation email... ====================================================================== ====================================================================== 📨 Message #3 received from partition 0 at offset 2 📧 EMAIL SERVICE Order ID: 1003 Recipient: bob_jones Total: $12.99 ✅ Sending order confirmation email... ====================================================================== ====================================================================== 📨 Message #4 received from partition 0 at offset 3 📧 EMAIL SERVICE Order ID: 1004 Recipient: bob_jones Total: $319.97 ✅ Sending order confirmation email... ====================================================================== ====================================================================== 📨 Message #5 received from partition 0 at offset 4 📧 EMAIL SERVICE Order ID: 1005 Recipient: diana_prince Total: $454.97 ✅ Sending order confirmation email... ====================================================================== ====================================================================== 📨 Message #6 received from partition 0 at offset 5 📧 EMAIL SERVICE Order ID: 1006 Recipient: diana_prince Total: $194.99 ✅ Sending order confirmation email... ====================================================================== ====================================================================== 📨 Message #7 received from partition 0 at offset 6 📧 EMAIL SERVICE Order ID: 1007 Recipient: charlie_brown Total: $224.98 ✅ Sending order confirmation email... ====================================================================== ====================================================================== 📨 Message #8 received from partition 0 at offset 7 📧 EMAIL SERVICE Order ID: 1008 Recipient: charlie_brown Total: $45.0 ✅ Sending order confirmation email... ====================================================================== ====================================================================== 📨 Message #9 received from partition 0 at offset 8 📧 EMAIL SERVICE Order ID: 1009 Recipient: alice_smith Total: $124.98 ✅ Sending order confirmation email... ====================================================================== ====================================================================== 📨 Message #10 received from partition 0 at offset 9 📧 EMAIL SERVICE Order ID: 1010 Recipient: eve_adams Total: $29.99 ✅ Sending order confirmation email... ====================================================================== Enter fullscreen mode Exit fullscreen mode There we go with out Kafka cluster! That’s a wrap 💯 Conclusion Streaming matters because data is most valuable the moment it is created. By using a Kafka cluster, we move away from “batch processing” and toward real-time intelligence. The implementation shown here — with a replicated cluster, a robust producer, and independent consumer groups — provides a blueprint for systems that are not only fast but also incredibly resilient. Whether you are scaling to handle thousands of orders per second or simply trying to decouple your microservices, Kafka provides the reliable foundation needed for a modern, event-driven architecture. >>> Thanks for reading <<< Links GitHub repository for the code sample: https://github.com/aairom/kafka-101 Apache Kafka repository: https://github.com/apache/kafka Introduction to Apache Kafka: https://www.confluent.io/what-is-apache-kafka/ Confluent: https://www.confluent.io/ Confluent Platform: https://www.confluent.io/product/confluent-platform/ Confluent Cloud: https://www.confluent.io/confluent-cloud/","contentHash":"sha256:f88cf5a87e56c09cc76461cd22b09c55cc91e49d388f782e22072e6921a903df","authorName":"Alain Airom (Ayrom)","authorUrl":"https://api.zyvop.com/author/alain","authorSameAs":["https://github.com/aairom","https://www.linkedin.com/in/aairom/"],"category":null,"tags":["Kafka","Software","datastreaming","confluent","bob"],"audience":"Developers, software engineers, and students learning Kafka","tone":"Professional, build engineer perspective","readingTimeMinutes":15,"wordCount":3286,"faqs":null,"primaryTopic":"Kafka","publishedAt":"2026-09-11T05:12:02.912Z","updatedAt":"2026-09-11T05:12:02.912Z","canonicalUrl":"https://dev.to/aairom/kafka-101-why-event-streaming-is-the-central-nervous-system-of-modern-data-474f"}