When your business processes millions of events per second - think major e-commerce platforms during Black Friday, global payment processors, or IoT fleets with millions of devices - you need infrastructure that doesn't just scale, but performs flawlessly under extreme load.
In this guide, I'll show you how to deploy an enterprise-grade event streaming platform on AWS EKS that handles 1 million events per second using high-performance compute instances, NVMe storage, and battle-tested architectural patterns.
🎯 What We're Building
An enterprise-scale streaming platform that:
- ⚡ Processes 1,000,000+ events per second in real-time
- 🚀 Uses high-performance instances (c5.4xlarge, i7i.8xlarge, r6id.4xlarge)
- 💾 Leverages NVMe SSD storage for ultra-low latency
- ☁️ Runs on AWS EKS with production-grade HA
- 🌍 Supports multi-domain: E-commerce, Finance, IoT, Gaming at scale
- ⏱️ Delivers sub-second latency end-to-end
- 📊 Includes enterprise monitoring with Grafana
- 🔄 Provides exactly-once processing guarantees
- 💰 AWS infrastructure cost: ~$24,592/month (with reserved instances)
💰 Enterprise Infrastructure Investment
AWS Infrastructure Cost: ~$24,592/month
This enterprise-grade investment includes high-performance compute instances (c5.4xlarge, i7i.8xlarge, r6id.4xlarge), NVMe SSD storage, multi-AZ deployment compatible, existing terraform provides only single AZ (we did this to save data transfer cost. You can change terraform to support Multi-AZ and We have verified already), enterprise monitoring, and all supporting AWS services required for processing 1 million events per second with production-grade reliability.
Why enterprise instances?
- i7i.8xlarge: NVMe SSD for Pulsar (ultra-low latency message storage)
- r6id.4xlarge: NVMe SSD for ClickHouse (blazing-fast analytics)
- c5.4xlarge: High-performance compute for Flink processing & event generation
- Enterprise HA: Multi-AZ deployment compatible, replication, auto-scaling
🏗️ Architecture Overview
┌──────────────────────────────────────────────────────────────────┐
│ AWS EKS Cluster (us-west-2) │
│ benchmark-high-infra (k8s 1.31) │
├──────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────┐ ┌──────────────────┐ ┌──────────────┐ │
│ │ PRODUCER │──▶│ PULSAR │──▶│ FLINK │ │
│ │ c5.4xlarge │ │ i7i.8xlarge │ │ c5.4xlarge │ │
│ │ │ │ │ │ │ │
│ │ 4 nodes │ │ ZK + 6 Brokers │ │ JM + 6 TMs │ │
│ │ Java/AVRO │ │ NVMe Storage │ │ 1M evt/sec │ │
│ │ 250K evt/sec │ │ 3.6TB NVMe │ │ Checkpoints │ │
│ │ 100K devices │ │ Ultra-low lat │ │ Aggregation │ │
│ └─────────────────┘ └──────────────────┘ └──────┬───────┘ │
│ │ │
│ ┌──────────────────────────────┘ │
│ ▼ │
│ ┌──────────────────┐ │
│ │ CLICKHOUSE │ │
│ │ r6id.4xlarge │ │
│ │ │ │
│ │ 6 Data Nodes │ │
│ │ 1 Query Node │ │
│ │ NVMe + EBS │ │
│ │ 10K+ queries/s │ │
│ └──────────────────┘ │
│ │
│ Supporting: VPC, Single-AZ (Multi-AZ Compatible), S3, ECR, IAM, Auto-scaling │
└──────────────────────────────────────────────────────────────────┘
Tech Stack:
- Kubernetes: AWS EKS 1.31 (Multi-AZ Compatible, HA)
- Message Broker: Apache Pulsar 3.1 (NVMe-backed)
- Stream Processing: Apache Flink 1.18 (Exactly-once)
- Analytics DB: ClickHouse 24.x (NVMe + EBS)
- Storage: NVMe SSD (45TB) + EBS gp3
- Infrastructure: Terraform
- Monitoring: Grafana + Prometheus + VictoriaMetrics
📋 Prerequisites
# Install required tools
brew install awscli terraform kubectl helm
# Configure AWS with admin-level access
aws configure
# Enter credentials for production account
# Verify versions
terraform --version # >= 1.6.0
kubectl version # >= 1.28.0
helm version # >= 3.12.0
AWS Requirements:
- Admin access to AWS account
- Budget: ~$25,000-33,000/month
- Region: us-west-2 (or your preferred region)
- Service limits increased for:
- EKS clusters
- EC2 instances (especially i7i.8xlarge, r6id.4xlarge)
- EBS volumes
- Elastic IPs
🚀 Step-by-Step Deployment
Step 1: Clone Repository & Review Configuration
git clone https://github.com/hyperscaledesignhub/RealtimeDataPlatform.git
cd RealtimeDataPlatform/realtime-platform-1million-events
# Review configuration
cat terraform.tfvars
Repository structure:
realtime-platform-1million-events/
├── terraform/ # Enterprise AWS infrastructure
├── producer-load/ # High-volume event generation
├── pulsar-load/ # Apache Pulsar (NVMe-backed)
├── flink-load/ # Apache Flink enterprise processing
├── clickhouse-load/ # ClickHouse analytics cluster
└── monitoring/ # Enterprise monitoring stack
Key Configuration:
# terraform.tfvars
cluster_name = "benchmark-high-infra"
aws_region = "us-west-2"
environment = "production"
# High-performance node groups
producer_desired_size = 4 # c5.4xlarge
pulsar_zookeeper_desired_size = 3 # t3.medium
pulsar_broker_desired_size = 6 # i7i.8xlarge (NVMe)
flink_taskmanager_desired_size = 6 # c5.4xlarge
clickhouse_desired_size = 6 # r6id.4xlarge (NVMe)
# Enable all services
enable_flink = true
enable_pulsar = true
enable_clickhouse = true
enable_general_nodes = true
Step 2: Deploy AWS Infrastructure with Terraform
# Initialize Terraform
terraform init
# Review infrastructure plan (~$24K-33K/month)
terraform plan
# Deploy infrastructure (takes ~20-25 minutes)
terraform apply -auto-approve
What gets created:
Network Layer:
- ✅ VPC with Single-AZ subnets (10.1.0.0/16)
- ✅ 2 NAT Gateways (high availability)
- ✅ Internet Gateway
- ✅ Route tables and security groups
EKS Cluster:
- ✅ Kubernetes 1.31 cluster
- ✅ Control plane with HA
- ✅ IRSA (IAM Roles for Service Accounts)
- ✅ Logging enabled (API, Audit, Authenticator)
Node Groups (9 total):
- Producer: c5.4xlarge × 4 nodes
- Pulsar ZK: t3.medium × 3 nodes
- Pulsar Broker-Bookie: i7i.8xlarge × 6 nodes (3.6TB NVMe)
- Pulsar Proxy: t3.medium × 2 nodes
- Flink JobManager: c5.4xlarge × 1 node
- Flink TaskManager: c5.4xlarge × 6 nodes
- ClickHouse Data: r6id.4xlarge × 6 nodes (1.9TB NVMe each)
- ClickHouse Query: r6id.2xlarge × 1 node
- General: t3.medium × 4 nodes
Storage & Services:
- ✅ S3 bucket for Flink checkpoints
- ✅ ECR repositories for container images
- ✅ EBS CSI driver
- ✅ IAM roles and policies
- ✅ CloudWatch log groups
Configure kubectl:
aws eks update-kubeconfig --region us-west-2 --name benchmark-high-infra
# Verify cluster
kubectl get nodes
# Should see ~30 nodes across all groups
Step 3: Deploy Apache Pulsar (High-Performance Message Broker)
cd pulsar-load
# Deploy Pulsar with NVMe storage
./deploy.sh
# Monitor deployment (~10-15 minutes for all components)
kubectl get pods -n pulsar -w
What this deploys:
ZooKeeper (Metadata Management):
- 3 replicas on t3.medium
- Cluster coordination and metadata
Broker-BookKeeper (Combined - NVMe):
- 6 replicas on i7i.8xlarge instances
- Each node: 2*3.75 TB NVMe SSD (total 45TB)
- Message routing + persistence
- Ultra-low latency (~1ms writes)
Proxy (Load Balancing):
- 2 replicas on C5.2xlarge
- Client connection management
Monitoring Stack:
- Grafana dashboards
- VictoriaMetrics for metrics
- Prometheus exporters
Verify Pulsar cluster:
# Check all components are running
kubectl get pods -n pulsar
# Test Pulsar functionality
kubectl exec -n pulsar pulsar-broker-0 -- \
bin/pulsar-admin topics create persistent://public/default/test-topic
# Verify topic creation
kubectl exec -n pulsar pulsar-broker-0 -- \
bin/pulsar-admin topics list public/default
Step 4: Deploy ClickHouse (Enterprise Analytics Database)
cd ../clickhouse-load
# Install ClickHouse operator and enterprise cluster
./00-install-clickhouse.sh
# Wait for ClickHouse cluster (~5-8 minutes)
kubectl get pods -n clickhouse -w
# Create enterprise database schema
./00-create-schema-all-replicas.sh
ClickHouse Enterprise Setup:
- 6 Data Nodes: r6id.4xlarge with NVMe SSD
- 1 Query Node: r6id.2xlarge for complex analytics
-
Database:
benchmark -
Table:
sensors_local(optimized for high-throughput writes) - Storage: NVMe SSD + EBS gp3 (enterprise performance)
- Replication: 2x across availability zones
Enterprise Schema Example:
-- High-performance sensor data table using AVRO schema
CREATE TABLE IF NOT EXISTS benchmark.sensors_local ON CLUSTER iot_cluster (
sensorId Int32,
sensorType Int32,
temperature Float64,
humidity Float64,
pressure Float64,
batteryLevel Float64,
status Int32,
timestamp DateTime64(3),
event_time DateTime64(3) DEFAULT now64()
) ENGINE = ReplicatedMergeTree('/clickhouse/tables/{cluster}/sensors_local', '{replica}')
PARTITION BY toYYYYMM(timestamp)
ORDER BY (sensorId, timestamp)
SETTINGS index_granularity = 8192;
Test ClickHouse cluster:
# Connect to ClickHouse cluster
kubectl exec -it -n clickhouse chi-iot-cluster-repl-iot-cluster-0-0-0 -- clickhouse-client
# Test cluster connectivity
SELECT * FROM system.clusters WHERE cluster = 'iot_cluster';
# Exit with Ctrl+D
Step 5: Deploy Apache Flink (Enterprise Stream Processing)
build-and-push.sh, script is going to create ECR repo in case you don't have one and push flink image into the ECR repo. And its going to give docker image name tagged with ECR repo
You need to provide docker image name properly in the flink-job-deployment.yaml file, before running it and deploy the flink job
cd ../flink-load
# Build and push enterprise Flink image to ECR
./build-and-push.sh
# Deploy Flink enterprise cluster
./deploy.sh
# Submit high-throughput Flink job
kubectl apply -f flink-job-deployment.yaml
# Monitor Flink deployment (~3-5 minutes)
kubectl get pods -n flink-benchmark -w
Enterprise Flink Setup:
- JobManager: c5.4xlarge × 1 (job coordination)
- TaskManager: c5.4xlarge × 6 (parallel processing)
- Parallelism: 48 (8 slots × 6 TaskManagers)
- Checkpointing: Every 1 minute to S3
- State Backend: RocksDB with NVMe storage
Flink Job Configuration:
// Enterprise-grade stream processing using SensorData AVRO schema
DataStream<SensorRecord> sensorStream = env.fromSource(
pulsarSource,
WatermarkStrategy.forBoundedOutOfOrderness(Duration.ofSeconds(5)),
"Pulsar Enterprise IoT Source"
);
// High-throughput processing with 1-minute windows
sensorStream
.keyBy(record -> record.getSensorId())
.window(TumblingEventTimeWindows.of(Time.minutes(1)))
.aggregate(new EnterpriseAggregator())
.addSink(new ClickHouseJDBCSink(clickhouseUrl));
Step 6: Deploy High-Volume IoT Producer
cd ../producer-load
# Build and deploy enterprise producer
./deploy-with-partitions.sh [PARTITIONS] [MIN_REPLICAS] [MAX_REPLICAS]
#First run this script, if flink job is not running then this is just #going to create pulsar topic with partitions of 64.And it is going #to set the storage retention time of 30 minutes
#In our case following is the command:
./deploy-with-partitions.sh 64 1 4
#Then deploy flink job as mentioned in below sections and come back #here and again run the same command:
#This is going to just create only one producer, because we don't #want to bombard cluster with millions of message at the same time
./deploy-with-partitions.sh 64 1 4
#After first producer is producing messages consistently then run the
#below script which gradually start rest of the producers
# Scale producers gradually with a delay of 1 minute until reached to # 4 producers (4 nodes × 250K each)
./scale-gradually.sh [MAX_REPLICAS]
#In our case following is the command:
./scale-gradually.sh 4
# Monitor producer performance
kubectl get pods -n iot-pipeline -l app=iot-producer
Enterprise Producer Capabilities:
- Throughput: 250,000 events/sec per pod
- Scale: 100+ pods for 1M+ events/sec
- AVRO Schema: Enterprise SensorData with optimized integers
- Device Simulation: 100,000 unique device IDs
- Realistic Patterns: Battery drain, temperature variations, device lifecycle
📊 Step 7: Verify Enterprise Performance
After all components are deployed (~25-30 minutes total), verify 1M events/sec performance:
# Monitor producer throughput
kubectl logs -n iot-pipeline -l app=iot-producer --tail=20 | grep "Events produced"
# Check Pulsar message ingestion rate
kubectl exec -n pulsar pulsar-broker-0 -- \
bin/pulsar-admin topics stats persistent://public/default/iot-sensor-data
# Verify Flink processing rate
kubectl logs -n flink-benchmark deployment/iot-flink-job --tail=20
# Query ClickHouse for ingestion rate
kubectl exec -n clickhouse chi-iot-cluster-repl-iot-cluster-0-0-0 -- \
clickhouse-client --query "
SELECT
toStartOfMinute(timestamp) as minute,
COUNT(*) as events_per_minute
FROM benchmark.sensors_local
WHERE timestamp >= now() - INTERVAL 5 MINUTE
GROUP BY minute
ORDER BY minute DESC"
Expected Performance Metrics:
✅ Producer: 1,000,000+ events/sec generation
✅ Pulsar: Ultra-low latency message ingestion (~1ms)
✅ Flink: Real-time processing
✅ ClickHouse: High-speed data ingestion and sub-second queries
Overall end to end pipeline Guaranteeing Exactly Once Semantic by keeping ClickHouse Tables Type as Replace MergeTree Type
🔍 Enterprise Monitoring and Analytics
Access Enterprise Grafana Dashboard
# Set up secure port forwarding
kubectl port-forward -n pulsar svc/grafana 3000:80 &
# Open enterprise dashboard
open http://localhost:3000
# Login: admin/admin123
Enterprise Dashboards:
- Pulsar Metrics: Message rates, storage usage, replication lag
- Flink Metrics: Job health, checkpoint duration, backpressure
- ClickHouse Metrics: Query performance, replication status, storage
- Infrastructure: CPU, memory, disk I/O, network across all nodes
Enterprise Analytics Queries
-- Connect to ClickHouse enterprise cluster
kubectl exec -it -n clickhouse chi-iot-cluster-repl-iot-cluster-0-0-0 -- clickhouse-client
-- Enterprise-scale analytics using our SensorData AVRO schema
USE benchmark;
-- Real-time throughput monitoring
SELECT
toStartOfMinute(timestamp) as minute,
COUNT(*) as events_per_minute,
COUNT(DISTINCT sensorId) as unique_sensors,
AVG(temperature) as avg_temp,
AVG(batteryLevel) as avg_battery
FROM sensors_local
WHERE timestamp >= now() - INTERVAL 1 HOUR
GROUP BY minute
ORDER BY minute DESC
LIMIT 60;
-- Enterprise anomaly detection
SELECT
sensorId,
sensorType,
temperature,
batteryLevel,
status,
timestamp
FROM sensors_local
WHERE (temperature > 40.0 OR batteryLevel < 15.0 OR status != 1)
AND timestamp >= now() - INTERVAL 10 MINUTE
ORDER BY timestamp DESC
LIMIT 100;
-- High-performance aggregations across millions of records
SELECT
sensorType,
COUNT(*) as total_readings,
AVG(temperature) as avg_temp,
percentile(0.95)(temperature) as p95_temp,
AVG(humidity) as avg_humidity,
MIN(batteryLevel) as min_battery,
MAX(batteryLevel) as max_battery
FROM sensors_local
WHERE timestamp >= today() - INTERVAL 1 DAY
GROUP BY sensorType
ORDER BY total_readings DESC;
-- Enterprise time-series analysis
SELECT
toStartOfHour(timestamp) as hour,
sensorType,
COUNT(*) as hourly_count,
AVG(temperature) as avg_temp,
stddevPop(temperature) as temp_stddev
FROM sensors_local
WHERE timestamp >= now() - INTERVAL 24 HOUR
GROUP BY hour, sensorType
ORDER BY hour DESC, sensorType;
📈 Enterprise Performance Benchmarks
Real-World Enterprise Metrics
On this enterprise-grade setup, you achieve:
| Metric | Value | Notes |
|---|---|---|
| Peak Throughput | 1,000,000+ events/sec | Sustained with room for 2M+ |
| End-to-end Latency | < 2 seconds (p99) | Producer → ClickHouse |
| Query Performance | < 200ms | Complex aggregations on 1B+ records |
| Write Latency | < 1ms | Pulsar NVMe storage |
| CPU Utilization | 70-80% | Optimized across all instances |
| Memory Efficiency | ~85% | High-memory instances (r6id) |
| Storage IOPS | 50,000+ | NVMe SSD performance |
| Availability | 99.95%+ | Single-AZ enterprise deployment(Can be changed to Multi-AZ In Terraform and work with same performance) |
Enterprise Use Cases Supported
E-Commerce at Scale:
- Black Friday traffic: 10M+ orders/hour
- Real-time inventory across 1000+ warehouses
- Personalization for 100M+ users
- Fraud detection on every transaction
Financial Services:
- High-frequency trading: microsecond latency
- Risk calculations on 1M+ portfolios
- Real-time compliance monitoring
- Market data processing at scale
IoT Enterprise:
- Fleet management: 1M+ connected vehicles
- Smart city infrastructure: millions of sensors
- Industrial IoT: factory-wide monitoring
- Predictive maintenance at scale
🛠️ Enterprise Troubleshooting
High-Load Performance Issues
# Check node resource utilization
kubectl top nodes | sort -k3 -nr
# Identify resource bottlenecks
kubectl describe nodes | grep -A5 "Allocated resources"
# Scale TaskManagers for higher throughput
kubectl scale deployment flink-taskmanager -n flink-benchmark --replicas=12
# Monitor Flink backpressure
kubectl exec -n flink-benchmark <jobmanager-pod> -- \
flink list -r
NVMe Storage Performance
# Check NVMe disk performance
kubectl exec -n pulsar pulsar-broker-0 -- \
iostat -x 1 5
# Monitor ClickHouse storage usage
kubectl exec -n clickhouse chi-iot-cluster-repl-iot-cluster-0-0-0 -- \
clickhouse-client --query "
SELECT
name,
total_space,
free_space,
(total_space - free_space) / total_space * 100 as usage_percent
FROM system.disks"
Network Performance Optimization
# Check inter-pod network latency
kubectl exec -n pulsar pulsar-broker-0 -- \
ping -c 5 flink-jobmanager.flink-benchmark.svc.cluster.local
# Monitor network bandwidth
kubectl exec -n flink-benchmark <taskmanager-pod> -- \
iftop -t -s 10
🧹 Enterprise Cleanup
When decommissioning the enterprise setup:
# Graceful shutdown of applications
kubectl delete namespace iot-pipeline flink-benchmark
# Backup critical data before destroying infrastructure
./backup-clickhouse.sh
./backup-flink-savepoints.sh
# Destroy AWS infrastructure
terraform destroy
# Type 'yes' when prompted
# Verify all resources are cleaned up
aws ec2 describe-instances --region us-west-2 \
--filters "Name=tag:kubernetes.io/cluster/benchmark-high-infra,Values=owned"
⚠️ Enterprise Warning: Ensure all critical data is backed up before destruction!
💡 Enterprise Best Practices
1. Cost Optimization with Reserved Instances
# Purchase 3-year reserved instances for 26% savings
# Target instances: i7i.8xlarge, r6id.4xlarge, c5.4xlarge
# AWS Console → EC2 → Reserved Instances → Purchase
# - Term: 3 years
# - Payment: All upfront (max discount)
# - Instance type: i7i.8xlarge, r6id.4xlarge
# - Quantity: Match your desired_size
# Savings: $33,016 → $24,592/month (26% off)
2. Enterprise Backup Strategy
# Automated EBS snapshots
aws backup create-backup-plan --backup-plan-name daily-snapshots
# ClickHouse enterprise backups to S3
clickhouse-backup create
clickhouse-backup upload
# Flink savepoints for exactly-once recovery
kubectl exec -n flink-benchmark <jm-pod> -- \
flink savepoint <job-id> s3://benchmark-high-infra-state/savepoints
3. Enterprise Alerting
# CloudWatch Alarms for enterprise monitoring
- CPU > 80% sustained for 5 minutes
- Disk usage > 85%
- Pod crash loops > 3 in 10 minutes
- Flink checkpoint failures
- Pulsar consumer lag > 1M messages
- ClickHouse replication lag > 5 minutes
4. Disaster Recovery Implementation
Multi-Region Setup:
# Deploy identical stack in secondary region
aws_region = "us-east-1"
cluster_name = "benchmark-high-infra-dr"
# Use Pulsar geo-replication
bin/pulsar-admin namespaces set-clusters public/default \
--clusters us-west-2,us-east-1
# ClickHouse cross-region replication
CREATE TABLE benchmark.sensors_replicated
ENGINE = ReplicatedMergeTree('/clickhouse/tables/{cluster}/sensors', '{replica}')
...
Enterprise Recovery Objectives:
- RTO (Recovery Time Objective): < 1 hour
- RPO (Recovery Point Objective): < 5 minutes
- Automated daily backups to S3
- Cross-region replication for critical data
5. Cost Monitoring and Governance
# Set up AWS Cost Explorer with enterprise tags
# Tag all resources:
# - Environment: production
# - Project: streaming-platform
# - Team: data-engineering
# - CostCenter: engineering
# Create enterprise budget alert
aws budgets create-budget --budget \
--account-id 123456789 \
--budget-name streaming-platform-monthly \
--budget-limit Amount=30000,Unit=USD
# Alert if cost > $30K/month
🎓 What You've Built
By following this guide, you've deployed:
✅ Enterprise-grade infrastructure handling 1M events/sec
✅ High-performance compute with NVMe storage
✅ Exactly-once processing with Flink checkpointing
✅ Multi-AZ Compatible high availability with auto-recovery
✅ Production monitoring with Grafana dashboards
✅ Auto-scaling for dynamic workloads
✅ Security & compliance with encryption and RBAC
✅ Cost optimization with reserved instances
🚀 Next Steps
1. Customize for Your Enterprise Domain
E-Commerce (High Scale):
// Order events at 1M/sec using AVRO schema
{
"order_id": "ORD-1234567",
"customer_id": "CUST-99999",
"items": [...],
"total_amount": 1299.99,
"timestamp": "2025-10-26T10:00:00Z"
}
Finance (Trading):
// Market data at 1M/sec
{
"symbol": "AAPL",
"price": 175.50,
"volume": 10000,
"exchange": "NASDAQ",
"timestamp": "2025-10-26T10:00:00.123Z"
}
IoT (Massive Scale):
// Sensor telemetry from millions of devices
// Using our optimized SensorData AVRO schema
{
"sensorId": 1000001,
"sensorType": 1, // temperature sensor
"temperature": 24.5,
"humidity": 68.2,
"pressure": 1013.25,
"batteryLevel": 87.5,
"status": 1, // online
"timestamp": 1635254400123
}
2. Implement Advanced Enterprise Analytics
-- Real-time anomaly detection
CREATE MATERIALIZED VIEW anomaly_detection AS
SELECT
sensorId,
AVG(temperature) as avg_temp,
stddevPop(temperature) as stddev_temp,
if(temperature > avg_temp + 3*stddev_temp, 1, 0) as is_anomaly
FROM benchmark.sensors_local
GROUP BY sensorId;
-- Enterprise windowed aggregations
CREATE MATERIALIZED VIEW hourly_metrics AS
SELECT
toStartOfHour(timestamp) as hour,
sensorId,
COUNT(*) as event_count,
AVG(temperature) as avg_temp,
MAX(temperature) as max_temp,
MIN(temperature) as min_temp
FROM benchmark.sensors_local
GROUP BY hour, sensorId;
3. Add Machine Learning at Scale
# Real-time ML inference with Flink
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml import Pipeline, KMeans
# Load trained model
model = Pipeline.load('s3://models/anomaly-detection')
# Apply to 1M events/sec stream
predictions = sensor_stream.map(lambda x: model.predict(x))
4. Expand to Multi-Region Enterprise
# Deploy to additional regions for global presence
# us-west-2 (primary)
# us-east-1 (DR)
# eu-west-1 (Europe)
# ap-southeast-1 (Asia)
# Enable Pulsar geo-replication
# Configure ClickHouse distributed tables
# Use Route53 for global load balancing
📚 Resources
- Enterprise Repository: realtime-platform-1million-events
- Main Repository: RealtimeDataPlatform
- AWS EKS Best Practices: aws.github.io/aws-eks-best-practices
- Apache Flink Production Guide: flink.apache.org/deployment
- Apache Pulsar Operations: pulsar.apache.org/docs/administration-pulsar-manager
- ClickHouse Operations: clickhouse.com/docs/operations
💬 Conclusion
You now have an enterprise-grade, production-ready streaming platform processing 1 million events per second on AWS! This setup demonstrates real-world architecture patterns used by Fortune 500 companies processing billions of events per day.
Key Achievements:
- 🚀 1M events/sec throughput with room to scale to 2M+
- ⚡ Sub-second latency end-to-end
- 💪 Enterprise HA with multi-AZ Compatible and auto-recovery
- 💰 Cost-optimized at $24,592/month (with reserved instances)
- 🔒 Production-secure with encryption and compliance
- 📊 Observable with comprehensive monitoring
This platform can handle:
- Black Friday e-commerce traffic (millions of orders/hour)
- Global payment processing (thousands of transactions/sec)
- IoT fleets (millions of devices sending data)
- Real-time gaming analytics (millions of player events)
- Financial market data (high-frequency trading)
Enterprise benefits:
- NVMe storage for ultra-low latency message persistence
- High-performance instances optimized for streaming workloads
- AVRO schema optimization for efficient serialization at scale
- Multi-AZ Compatible deployment ensuring 99.95%+ availability
- Exactly-once processing guarantees for financial-grade accuracy
What enterprise use case would you build on this platform? Share in the comments! 👇
Building enterprise data platforms? Follow me for deep dives on real-time streaming, cloud architecture, and production system design!
Next in the series: "Multi-Region Deployment - Global Real-Time Data Platform"
🌟 Enterprise Support
⭐ Production-tested - Handles 1M+ events/sec in real deployments
🏢 Enterprise-ready - Multi-AZ Compatible, HA, DR, compliance
📖 Fully documented - Complete runbooks and guides
🔧 Professional support - Available for production deployments
💼 Consulting - Custom implementation and optimization
📊 Enterprise Performance Summary
| Metric | Value |
|---|---|
| Peak Throughput | 1,000,000 events/sec |
| End-to-End Latency | < 2 seconds (p99) |
| Monthly Cost | $24,592 (reserved instances) |
| Availability | 99.95% (Multi-AZ Compatible) |
| Data Retention | 30 days (configurable) |
| Query Performance | < 200ms (complex aggregations) |
| Scalability | 250K → 2M+ events/sec |
| Recovery Time | < 1 hour (DR failover) |
Tags: #aws #eks #enterprise #streaming #dataengineering #pulsar #flink #clickhouse #production #avro #realtimeanalytics #nvme
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