Data integration—connecting disparate systems and data sources—remains among the most challenging aspects of enterprise technology. Despite decades of evolution, organizations still struggle with fragmented data, point-to-point integrations, and data quality issues. Modern data integration requires strategic approach, not just tactical connections.
This guide provides a framework for data integration, addressing patterns, technologies, and strategic approaches.
The Integration Challenge
Why Integration Remains Hard
System proliferation: More systems than ever to connect.
Data volume: Exponential growth in data volumes.
Real-time expectations: Increasing demand for timely data.
Cloud complexity: Hybrid and multi-cloud environments.
Data quality: Integration amplifies quality issues.
Skill scarcity: Integration expertise in short supply.
Integration Landscape Evolution
Point-to-point era: Direct connections between systems.
ETL era: Extract-transform-load batch processing.
ESB era: Enterprise service bus for middleware.
API era: API-first integration.
Event era: Event-driven and streaming integration.
Each era adds patterns without eliminating predecessors.
Integration Patterns
Pattern 1: APIs and Services
Synchronous service-based integration:
Characteristics:
- Request-response interaction
- Real-time data access
- Point-in-time queries
Use cases:
- Transaction processing
- Real-time lookups
- Service composition
Technologies:
- REST APIs
- GraphQL
- gRPC
Considerations:
- API design and versioning
- Rate limiting and throttling
- Security and authentication
Pattern 2: Event-Driven Integration
Asynchronous event-based integration:
Characteristics:
- Publish-subscribe model
- Decoupled producers and consumers
- Event streams
Use cases:
- Real-time data distribution
- System decoupling
- Event sourcing
Technologies:
- Apache Kafka
- AWS Kinesis, Azure Event Hubs
- Message queues (RabbitMQ, AWS SQS)
Considerations:
- Event schema management
- Exactly-once processing
- Event ordering
Pattern 3: Batch Integration (ETL/ELT)
Scheduled bulk data movement:
Characteristics:
- Scheduled execution
- Large data volumes
- Transformation processing
ETL (Extract-Transform-Load):
- Transform during movement
- Traditional data warehouse pattern
- Often on-premises
ELT (Extract-Load-Transform):
- Load then transform
- Cloud data warehouse pattern
- Leverages target processing
Technologies:
- Traditional: Informatica, Talend, IBM DataStage
- Modern: dbt, Fivetran, Airbyte
Pattern 4: Data Virtualization
Virtual integration without movement:
Characteristics:
- Federated queries across sources
- Data stays in place
- Real-time access
Use cases:
- Unified data access layer
- Reducing data movement
- Rapid integration
Considerations:
- Performance for complex queries
- Source system impact
- Not suited for all use cases
Pattern 5: Reverse ETL
Moving data warehouse data to operational systems:
Characteristics:
- Warehouse as source
- Operational systems as targets
- Activating analytics data
Use cases:
- Syncing analytics to CRM
- Customer segmentation to marketing
- ML scores to operations
Technologies:
- Census, Hightouch, Airship
Integration Architecture
Integration Layer Design
Organizing integration capability:
Integration layer components:
- API gateway
- Integration platform
- Event streaming platform
- Data pipeline tools
Architectural principles:
- Loose coupling
- Reusability
- Composability
- Observability
Integration Platform Selection
Choosing integration technology:
iPaaS (Integration Platform as a Service):
- Cloud-based integration
- Pre-built connectors
- Low-code development
- Examples: MuleSoft, Boomi, Workato
Data integration platforms:
- Focused on data movement
- ETL/ELT processing
- Data quality and governance
- Examples: Informatica, Fivetran
Event streaming platforms:
- Event-driven architecture
- Stream processing
- Examples: Confluent, AWS Kinesis
Implementation Approach
Assessment
Understanding current state:
Integration inventory: What integrations exist?
Pain point identification: Where are problems?
Pattern analysis: What patterns are in use?
Technology assessment: What tools are deployed?
Strategy Development
Planning integration approach:
Pattern selection: Which patterns for which use cases?
Technology strategy: Platform and tool choices.
Governance model: Standards and oversight.
Build vs. buy: What to build, what to acquire.
Implementation
Building capabilities:
Platform deployment: Implementing integration infrastructure.
Interface development: Building integrations.
Data quality: Addressing quality issues.
Monitoring: Observability for integrations.
Key Takeaways
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Multiple patterns coexist: APIs, events, batch each serve different needs.
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Architecture matters: Thoughtful integration architecture prevents chaos.
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Platform investment pays off: Investing in integration platforms reduces long-term effort.
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Data quality is integration problem: Integration surfaces and should address quality issues.
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Events enable decoupling: Event-driven patterns reduce system coupling complexity.
Frequently Asked Questions
Which integration pattern should we use? Depends on use case: APIs for synchronous transactions, events for decoupled real-time, batch for volume, virtualization for federated access.
Should we use iPaaS or build? iPaaS for breadth of connectors and speed; build for unique requirements or scale economics. Most organizations use both.
How do we handle data quality in integration? Address at source when possible. Data quality rules in pipelines. Data quality monitoring. Governance processes.
What about real-time vs. batch? Real-time for urgent data; batch for volume and complexity. Both have place. Don't force real-time where batch is sufficient.
How do we manage API versions? Version in URL or header. Maintain multiple versions during transition. Clear deprecation policy. API management platform helps.
What about security for integrations? Authentication (OAuth, API keys), authorization, encryption in transit, data masking, audit logging.