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DATA INTEGRATION

Connect the enterprise. Move data with purpose.

Enterprise data rarely lives in one place. We design and engineer integration architectures that connect applications, platforms and data sources into a reliable flow of information — ready for analytics, AI and better decisions.

ERP | CRM | APIs | FILES | CLOUD | LEGACY
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INTEGRATION LAYER
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DATA PLATFORM
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ANALYTICS
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AI
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BUSINESS VALUE
THE INTEGRATION CHALLENGE

Data becomes valuable only when the enterprise can move it, trust it and use it.

The modern enterprise integration problem is rooted in fragmentation. Critical data is trapped in legacy applications, isolated cloud platforms, and on-premise databases.

This fragmentation creates duplicate data, inconsistent formats, and disconnected APIs. Batch-heavy pipelines struggle to keep up with growing real-time requirements, while increasing analytics and AI demands require seamless, governed data flows across the entire organization.

FRAGMENTED
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CONNECTED
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CONSISTENT
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AVAILABLE
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INTELLIGENT

Integration is the connective tissue of the modern data estate.

A modern data platform cannot create enterprise value if critical information remains trapped across systems, applications and operational silos.

01 — CONNECT

Connect applications, systems and data sources securely.

02 — TRANSFORM

Standardize and transform information as it moves.

03 — TRUST

Preserve data quality, consistency and governance.

04 — ACTIVATE

Make trusted data available to analytics, AI and business processes.

THE ENTERPRISE DATA LANDSCAPE

Connect the systems that run the business.

BUSINESS SYSTEMS
ERP
CRM
HR
Finance
Supply Chain
DATA SOURCES
Databases
Files
Documents
Events
Operational Systems
APPLICATIONS
Enterprise Applications
SaaS
Custom Applications
Microservices
APIs & SERVICES
REST APIs
SOAP
Event APIs
Third-party Services
CLOUD & ON-PREMISE
Azure
AWS
Hybrid Environments
Legacy Infrastructure
DATA PLATFORMS
Data Lakes
Warehouses
Lakehouses
Modern Data Platforms
CHOOSE THE RIGHT PATTERN

Integration architecture should follow the business requirement.

01
BATCH INTEGRATION
For scheduled movement of large data volumes across enterprise warehouses and platforms. Solves high-throughput analytical requirements.
02
REAL-TIME INTEGRATION
For events, transactions and time-sensitive information. Solves latency issues for applications requiring immediate data consistency.
03
API-LED INTEGRATION
For reusable application and service connectivity. Exposes data securely across internal and external microservices.
04
EVENT-DRIVEN INTEGRATION
For loosely coupled, responsive enterprise architectures. Allows systems to react asynchronously to state changes.
05
ETL / ELT
For structured transformation and analytical workloads within cloud-native environments and data lakehouses.
06
DATA SYNCHRONIZATION
For maintaining consistency across operational systems, ensuring master data remains accurate enterprise-wide.

From source systems to trusted enterprise data.

DISCOVER
INPUT
Systems, interfaces, dependencies
ACTIVITY
Map the integration landscape
OUTPUT
Integration inventory
CONNECT
INPUT
Applications and sources
ACTIVITY
Establish interfaces and connectivity
OUTPUT
Reliable connections
INGEST
INPUT
Raw enterprise data
ACTIVITY
Extract and securely move data
OUTPUT
Ingested raw data
TRANSFORM
INPUT
Extracted raw datasets
ACTIVITY
Clean, format and standardize
OUTPUT
Structured data
VALIDATE
INPUT
Transformed data
ACTIVITY
Apply quality and governance checks
OUTPUT
Trusted data
DELIVER
INPUT
Trusted business data
ACTIVITY
Route to platforms and analytics
OUTPUT
Usable data products
MONITOR
INPUT
Active pipelines
ACTIVITY
Observe performance and SLA
OUTPUT
Operational visibility
A MODERN INTEGRATION ARCHITECTURE

Build the integration layer around the enterprise — not around individual tools.

SOURCE SYSTEMS
ERP CRM HR OPERATIONS FILES APIS
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CONNECTIVITY
APIs CONNECTORS EVENTS FILE TRANSFER
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INTEGRATION
ORCHESTRATION ETL/ELT TRANSFORMATION ROUTING
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DATA PLATFORM
LAKE WAREHOUSE LAKEHOUSE
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CONSUMPTION
BI ANALYTICS AI APPLICATIONS
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BUSINESS VALUE
DECISIONS | AUTOMATION | CUSTOMER EXPERIENCE | OPERATIONS

Integration without trust simply moves bad data faster.

DATA QUALITY

Ensure accuracy through active validation, standardization, deduplication, completeness tracking, and consistency rules.

GOVERNANCE

Maintain strict controls via clear data ownership, tracked lineage, controlled access, enforced policies, and robust auditability.

OBSERVABILITY

Build confidence with continuous pipeline monitoring, immediate failure detection, smart alerts, operational visibility, and SLA awareness.

INTEGRATION
+
QUALITY
+
GOVERNANCE
+
OBSERVABILITY
TRUSTED DATA FLOW

Connect legacy estates to modern cloud platforms without disrupting the business.

We understand that not every organization should immediately move everything to the cloud. We design phased approaches that solve for legacy modernization, secure cloud migration, complex hybrid environments, SaaS integration, and API modernization over time.

ON-PREMISE
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HYBRID CONNECTIVITY
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CLOUD INTEGRATION
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MODERN DATA PLATFORM
TECHNOLOGY ECOSYSTEM

Architecture first. Technology where it fits.

CLOUD
  • Azure
  • AWS
DATA PLATFORMS
INTEGRATION
  • APIs
  • ETL / ELT
  • Event-driven architecture
ENGINEERING
  • Data pipelines
  • Orchestration
  • Transformation
ANALYTICS
  • BI
  • Reporting
  • Advanced Analytics
GOVERNANCE
  • Quality
  • Lineage
  • Security
  • Observability

Integration that improves how the enterprise operates.

CONNECTED OPERATIONS

Information flows seamlessly across business systems.

FASTER ACCESS TO DATA

Teams spend less time finding and preparing information.

BETTER DATA QUALITY

Consistent and governed information across all systems.

ANALYTICS & AI READINESS

Trusted data becomes available for advanced analytics and AI.

LOWER COMPLEXITY

A deliberate integration architecture reduces point-to-point sprawl.

A controlled path from integration complexity to a scalable data estate.

01
ASSESS

Understand the existing integration landscape.

02
PRIORITIZE

Identify the highest-value integration opportunities.

03
ARCHITECT

Define the target integration architecture.

04
DELIVER

Build and modernize in controlled increments.

05
OPTIMIZE

Monitor, improve and scale.

Tangible integration deliverables.

Integration Landscape Assessment
System & Interface Inventory
Integration Architecture
Integration Pattern Recommendations
Target-State Architecture
Migration / Modernization Roadmap
Data Quality & Governance Considerations
Delivery Priorities
Integration Operating Model

Integration designed for the enterprise you are building.

BUSINESS-ALIGNED

We begin with business priorities and operating realities.

ENGINEERING-LED

Architecture and implementation decisions are grounded in engineering discipline.

GOVERNED BY DESIGN

Quality, security and governance are considered from the start.

BUILT TO SCALE

The integration architecture is designed to evolve with the enterprise.

Frequently Asked Questions

What is enterprise data integration?
It is the architectural process of connecting disparate systems, applications, and databases so that information can flow securely and reliably across the organization to support operations, analytics, and AI.
How do you integrate legacy and cloud systems?
We utilize hybrid integration patterns such as secure API gateways, dedicated connectors, and event-driven architectures to safely bridge on-premise legacy systems with modern cloud platforms without requiring immediate legacy retirement.
When should an organization use APIs versus ETL or event-driven integration?
APIs are ideal for synchronous, service-to-service communication. ETL/ELT is best for large-scale, batch analytical transformations. Event-driven architectures excel when systems must react to real-time state changes asynchronously. We match the pattern to the business requirement.
How do you approach real-time data integration?
We implement streaming pipelines and event brokers (like Kafka or Event Grid) combined with Change Data Capture (CDC) to capture and route transactions the moment they occur, minimizing latency.
How do you maintain data quality during integration?
We embed data quality checks directly into the integration pipelines, enforcing validation, standardization, and deduplication rules before data reaches downstream systems or analytics platforms.
How does data integration support analytics and AI?
AI and analytics models require massive amounts of high-quality, diverse data to function correctly. Integration provides the robust plumbing necessary to aggregate, cleanse, and deliver that data to the platforms where data science happens.
Can you modernize integrations without replacing every legacy system?
Yes. We use an API-led or abstraction-layer approach. By wrapping legacy systems in modern interfaces, we can integrate them into the new data estate while planning their eventual retirement in controlled phases.
How do you approach integration architecture and roadmap planning?
We start with an assessment of the current systems inventory and business goals. From there, we design a target architecture and a phased delivery roadmap that prioritizes high-value integrations first, managing risk and proving ROI early.

Connect your data estate for what comes next.

Whether the priority is modernizing legacy integrations, connecting cloud platforms or preparing trusted data for analytics and AI, Anlage can help define the architecture and path forward.

Discuss Your Integration Agenda Explore Modern Data Foundations