Enterprise Streaming Architecture: Kafka, Pulsar, and Platform Strategy

Introduction

Event streaming has evolved from niche technology to enterprise infrastructure standard. What began as a solution for log aggregation now powers real-time analytics, event-driven architectures, and the connective tissue between modern distributed systems.

For CTOs evaluating streaming investments, the decisions are consequential. Streaming platforms become deeply embedded in application architectures—the data they carry, the integrations they enable, and the operational patterns they require all create long-term dependencies.

This guide examines enterprise streaming strategy: when streaming delivers value, how to evaluate platform options, and what operational maturity is required for production success.

The Strategic Case for Streaming

Beyond Batch Processing

Traditional enterprise data architectures relied on batch processing:

• Collect data throughout the day
• Process overnight in batch jobs
• Deliver insights the next morning

This pattern served for decades but creates inherent limitations:

• Decisions based on stale data
• Batch job failures create cascading delays
• No support for real-time customer experiences
• Integration complexity with point-to-point connections

Streaming inverts this model: process data as it arrives, deliver insights immediately, react to events in real-time.

Use Cases Driving Adoption

Real-Time Analytics

Dashboards and metrics with seconds of latency:

• Operational monitoring and alerting
• Customer behaviour analysis
• Financial transaction monitoring
• Supply chain visibility

Event-Driven Architecture

Decoupled services communicating through events:

• Microservices integration
• Domain event propagation
• Saga orchestration
• CQRS implementations

Data Integration

Central nervous system for enterprise data:

• Change data capture from databases
• System-to-system integration
• Data lake and warehouse population
• Third-party data ingestion

Stream Processing

Continuous computation on flowing data:

• Fraud detection with immediate response
• Personalisation engines
• IoT data processing
• Machine learning feature computation

Business Impact Potential

Streaming investment typically delivers value through:

Speed to Insight

• Reducing analytics latency from hours to seconds
• Enabling proactive rather than reactive decisions
• Supporting real-time customer experiences

Operational Efficiency

• Eliminating batch job maintenance burden
• Reducing integration complexity through standardisation
• Enabling self-service data access patterns

New Capabilities

• Real-time personalisation previously impossible
• Event-driven automation opportunities
• Competitive differentiation through responsiveness

Platform Landscape

Apache Kafka

The market leader, originally developed at LinkedIn and now stewarded by the Apache Software Foundation with commercial support from Confluent.

Architecture Characteristics

Kafka uses a distributed commit log architecture:

• Partitioned topics for parallel processing
• Consumer groups for scalable consumption
• Replication for fault tolerance
• Log compaction for state management

Strengths

• Ecosystem maturity: Kafka Connect, Kafka Streams, KSQL, extensive third-party integrations
• Performance: Proven at extreme scale (trillions of messages per day at leading tech companies)
• Community: Largest community, most resources, widest talent pool
• Stability: Battle-tested across thousands of production deployments

Considerations

• Operational complexity: Requires expertise for production management
• Storage architecture: Coupled storage and compute, less flexible for some patterns
• Multi-tenancy: Requires careful configuration for isolation
• Geo-replication: Possible but complex to configure correctly

Deployment Options

• Self-managed on infrastructure
• Confluent Cloud (fully managed)
• Amazon MSK (managed Kafka on AWS)
• Azure Event Hubs (Kafka-compatible)
• Instaclustr, Aiven, and other managed providers

Apache Pulsar

Developed at Yahoo and contributed to Apache Foundation, with commercial support from StreamNative.

Architecture Characteristics

Pulsar separates storage and compute:

• Apache BookKeeper for persistent storage
• Stateless broker layer for serving
• Native multi-tenancy with isolation
• Built-in geo-replication

Strengths

• Architecture flexibility: Separated storage enables independent scaling
• Multi-tenancy: Built-in tenant and namespace isolation
• Geo-replication: Native support for cross-region deployment
• Unified messaging: Supports both streaming and queuing patterns
• Tiered storage: Automatic offloading to object storage

Considerations

• Ecosystem maturity: Smaller ecosystem than Kafka, though growing
• Operational complexity: More components to manage (brokers + BookKeeper)
• Community size: Smaller talent pool, fewer resources
• Commercial support: Less extensive than Confluent offerings

Deployment Options

• Self-managed on infrastructure
• StreamNative Cloud (fully managed)
• DataStax Astra Streaming
• Limited cloud provider managed options

Amazon Kinesis

AWS-native streaming service, tightly integrated with AWS ecosystem.

Strengths

• AWS integration: Native connections to Lambda, Firehose, Analytics
• Operational simplicity: Serverless model reduces management
• Scaling: Automatic scaling within limits
• Cost model: Pay-per-use without infrastructure overhead

Considerations

• AWS lock-in: Not portable to other environments
• Capability limitations: Less flexible than Kafka for complex patterns
• Scale constraints: Per-shard limits require careful design
• Ecosystem: Limited to AWS services and partners

Best fit: AWS-committed organisations with moderate scale requirements.

Azure Event Hubs

Microsoft’s managed streaming service with Kafka compatibility.

Strengths

• Azure integration: Native connections to Azure services
• Kafka compatibility: Can migrate existing Kafka workloads
• Enterprise features: Built-in security and compliance
• Serverless tier: Consumption-based pricing option

Considerations

• Azure lock-in: Primary value within Azure ecosystem
• Kafka compatibility gaps: Not 100% feature parity
• Performance variance: Shared infrastructure impacts

Best fit: Azure-committed enterprises seeking managed streaming.

Google Cloud Pub/Sub

Google’s managed messaging service for event-driven systems.

Strengths

• Global scale: Google’s infrastructure backing
• Simplicity: Minimal operational overhead
• GCP integration: Native BigQuery, Dataflow connections
• Serverless: True pay-per-message pricing

Considerations

• Different semantics: Not Kafka-compatible, different API model
• GCP lock-in: Limited value outside GCP
• Ordering limitations: Requires careful design for ordered processing

Best fit: GCP-native workloads prioritising simplicity over flexibility.

Evaluation Framework

Requirements Assessment

Before platform selection, clarify requirements:

Volume and Velocity

• Messages per second (average and peak)
• Message size distribution
• Growth projections over 3-5 years
• Burst handling requirements

Latency Requirements

• End-to-end latency tolerance
• Processing latency budgets
• Delivery guarantee needs (at-least-once, exactly-once)

Durability and Retention

• Data retention requirements
• Compliance and audit needs
• Replay capability requirements
• Recovery time objectives

Operational Context

• Team expertise and preferences
• Existing infrastructure investments
• Multi-cloud or hybrid requirements
• Security and compliance constraints

Comparison Matrix

Factor	Kafka	Pulsar	Kinesis	Event Hubs
Ecosystem	Excellent	Good	AWS-focused	Azure-focused
Performance	Excellent	Excellent	Good	Good
Operations	Complex	Complex	Simple	Moderate
Multi-tenancy	Configuration	Native	Limited	Good
Geo-replication	Complex	Native	Limited	Built-in
Cost at Scale	Lower	Lower	Higher	Moderate
Portability	High	High	AWS-only	Azure-primary

Decision Framework

Choose Kafka when:

• Ecosystem and community support are priorities
• Team has or can develop Kafka expertise
• Running in multiple cloud environments
• Maximum third-party integration options needed
• Confluent partnership provides value

Choose Pulsar when:

• Multi-tenancy is primary requirement
• Geo-replication is critical from day one
• Tiered storage offers significant cost benefit
• Starting fresh without Kafka investment
• Queue semantics needed alongside streaming

Choose Managed Cloud Services when:

• Minimising operational overhead is priority
• Committed to single cloud provider
• Scale requirements fit within service limits
• Integration with cloud-native services dominates

Architecture Patterns

Event-Driven Microservices

Streaming platforms enable decoupled service communication:

Service A → Event Topic → Service B
                       → Service C
                       → Service D

Design Considerations

• Event schema management and evolution
• Consumer group coordination
• Error handling and dead letter patterns
• Event sourcing and replay capabilities

Best Practices

• Define clear event contracts with schema registry
• Design for consumer failure and retry
• Implement idempotency in consumers
• Plan for schema evolution from the start

Change Data Capture

Stream database changes for downstream consumption:

Database → CDC Connector → Streaming Platform → Consumers

Popular CDC Tools

• Debezium (open source, Kafka Connect based)
• AWS DMS for Kinesis
• Fivetran, Airbyte for managed CDC

Considerations

• Database performance impact
• Schema change handling
• Ordering guarantees
• Initial snapshot management

Real-Time Analytics Pipeline

Feed analytical systems with streaming data:

Sources → Streaming Platform → Stream Processing → Analytics Store
                            → Data Lake/Warehouse
                            → Real-time Dashboards

Processing Options

• Kafka Streams (embedded in applications)
• Apache Flink (dedicated stream processor)
• Spark Streaming (batch-oriented streaming)
• Cloud-native options (Kinesis Analytics, Dataflow)

Hybrid Architecture

Most enterprises combine streaming with existing patterns:

Streaming for:

• Real-time event propagation
• Low-latency integration
• Event sourcing

Batch for:

• Large-scale transformations
• Historical reprocessing
• Cost-optimised analytics

The goal is appropriate tool selection, not dogmatic streaming everywhere.

Production Operations

Capacity Planning

Sizing Considerations

Broker sizing depends on:

• Message throughput requirements
• Message size distribution
• Replication factor
• Consumer parallelism needs
• Storage retention period

Scaling Patterns

Kafka:

• Add brokers and rebalance partitions
• Increase partition count (one-way operation)
• Separate clusters for isolation

Pulsar:

• Scale brokers independently from storage
• Add BookKeeper nodes for capacity
• Leverage tiered storage for cost

Cost Optimisation

• Right-size retention periods
• Use tiered storage where available
• Optimise message formats (compression, serialisation)
• Monitor and eliminate unused topics

Monitoring and Observability

Key Metrics

Platform health:

• Broker availability and leadership distribution
• Replication lag and under-replicated partitions
• Request latency (produce, consume)
• Disk and memory utilisation

Consumer health:

• Consumer lag by group and partition
• Processing throughput
• Error rates and retries
• Rebalance frequency

Tooling

• Prometheus/Grafana for metrics
• Confluent Control Center or equivalent
• Custom dashboards for business metrics
• Alerting for operational thresholds

Security Implementation

Authentication

• SASL mechanisms (SCRAM, OAUTHBEARER, Kerberos)
• TLS client certificates
• Cloud provider IAM integration

Authorisation

• ACLs for topic-level access control
• RBAC for administrative operations
• Integration with enterprise identity systems

Encryption

• TLS for data in transit
• At-rest encryption for persistent storage
• Key management integration

Compliance

• Audit logging for access and operations
• Data classification and handling
• Retention policy enforcement
• Cross-border data considerations

Disaster Recovery

Recovery Objectives

Define requirements:

• RPO (Recovery Point Objective): How much data loss is acceptable?
• RTO (Recovery Time Objective): How quickly must service restore?

Replication Strategies

Kafka:

• MirrorMaker 2 for cross-cluster replication
• Confluent Replicator for commercial option
• Consider replication lag in RPO calculations

Pulsar:

• Native geo-replication between clusters
• Synchronous or asynchronous options
• Built-in failover capabilities

Recovery Procedures

• Documented failover processes
• Regular DR testing
• Automated failover where appropriate
• Client reconfiguration strategies

Organisational Readiness

Team Capabilities

Successful streaming requires expertise:

Core Skills

• Distributed systems fundamentals
• Platform administration and tuning
• Performance analysis and optimisation
• Security configuration

Development Skills

• Event-driven design patterns
• Schema management
• Consumer implementation best practices
• Testing approaches for streaming

Build vs Buy

Consider managed services if:

• Limited streaming expertise available
• Operational simplicity prioritised
• Scale fits within managed service limits
• Budget allows for premium pricing

Invest in self-managed if:

• Scale economics favour self-hosting
• Customisation requirements exist
• Multi-cloud deployment needed
• Team expertise available or buildable

Governance Framework

Platform Standards

Establish and enforce:

• Topic naming conventions
• Schema requirements and registry usage
• Retention and partitioning standards
• Security baseline configurations

Change Management

Control changes that affect consumers:

• Schema evolution policies
• Breaking change communication
• Topic lifecycle management
• Consumer coordination

Cost Management

Track and allocate costs:

• Chargeback by team or application
• Usage monitoring and optimisation
• Budget alerting and controls

Implementation Roadmap

Phase 1: Foundation (Months 1-3)

Infrastructure

• Deploy platform in non-production
• Establish monitoring and alerting
• Configure security baseline
• Document operational procedures

Enablement

• Train core team on platform
• Develop internal documentation
• Create starter templates
• Establish support model

Pilot

• Select low-risk use case
• Implement end-to-end
• Validate operational readiness
• Gather lessons learned

Phase 2: Expansion (Months 4-9)

Production Deployment

• Promote to production environment
• Implement disaster recovery
• Establish SLAs and monitoring
• Build operational runbooks

Use Case Expansion

• Onboard additional applications
• Develop patterns and libraries
• Create self-service capabilities
• Expand team expertise

Governance

• Implement topic lifecycle management
• Deploy schema registry
• Establish change management
• Create cost tracking

Phase 3: Optimisation (Months 10-12+)

Scale and Performance

• Tune for production workloads
• Optimise resource utilisation
• Implement auto-scaling where possible
• Address operational pain points

Advanced Capabilities

• Stream processing implementations
• Complex event processing
• Real-time ML feature stores
• Event sourcing patterns

Platform Maturity

• Self-service onboarding
• Internal platform as a product
• Continuous improvement process
• Advanced monitoring and automation

Strategic Recommendations

For Greenfield Implementations

Starting fresh offers flexibility:

1. Start with clear use cases rather than building infrastructure seeking applications
2. Choose managed services initially to reduce operational burden
3. Establish governance early before technical debt accumulates
4. Plan for growth but don’t over-engineer for hypothetical scale

For Migration from Existing Systems

Moving from legacy integration:

1. Map current integration patterns to understand full scope
2. Prioritise by value and complexity for migration sequencing
3. Run parallel systems during transition with careful cutover
4. Preserve optionality for rollback during migration

Platform Strategy

For enterprise-wide streaming:

1. Standardise on primary platform to consolidate expertise
2. Allow exceptions with justification for specific requirements
3. Invest in platform team to support adoption
4. Treat platform as product with roadmap and stakeholder management

Conclusion

Streaming platforms represent fundamental infrastructure for modern enterprises. The shift from batch to real-time, from point-to-point to event-driven, from rigid to responsive—streaming enables all of these transformations.

The platform selection decision should match organisational context: technical requirements, team capabilities, existing investments, and strategic direction. Kafka remains the safe choice with maximum ecosystem support. Pulsar offers architectural advantages for specific requirements. Managed services trade flexibility for operational simplicity.

Whatever platform is selected, success depends on operational maturity, clear governance, and sustained investment in team capabilities. The technology is proven—the challenge is organisational readiness to leverage it effectively.

Sources

1. Confluent. (2025). State of Data Streaming Report. Confluent Research.
2. Apache Software Foundation. (2025). Apache Kafka Documentation. https://kafka.apache.org/documentation/
3. Apache Software Foundation. (2025). Apache Pulsar Documentation. https://pulsar.apache.org/docs/
4. Narkhede, N., Shapira, G., & Palino, T. (2023). Kafka: The Definitive Guide, 2nd Edition. O’Reilly Media.

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Strategic guidance for technology leaders building real-time data infrastructure.