Data
Integration

Data
Integration

Data must flow to create value

Business data is generated in ERP, CRM, PIM, commerce, production, and numerous other systems. If these systems remain isolated from one another, it leads to information gaps, manual reconciliation, and inconsistent decisions.

We develop data integration solutions that securely move and transform information between cloud, on-premises, and SaaS systems and make it available for operational processes, analytics, and AI—from integration strategy and architecture through implementation to ongoing operations.

Let’s discuss your integration needs:
experts@striped-giraffe.com

Data integration is now faster and more cost-effective than ever – eliminating data silos and enabling informed decisions in real-time.

Martina Deffner, CEO at Striped Giraffe

Challenge

When Data Silos Slow Down Processes and Decision-Making

Many integration landscapes have evolved incrementally over the years. Individual interfaces may address immediate requirements, but over time they often create complex dependencies and a growing maintenance burden.

Typical challenges include:

• isolated data across different systems,
• inconsistent formats and data models,
• numerous point-to-point connections that are difficult to maintain,
• delayed or incomplete data availability,
• manual exports and file transfers,
• limited transparency into data flows and dependencies,
• data quality issues at system boundaries,
• limited scalability of existing integrations,
• significant effort required for migrations and system replacements.

The consequences range from slow processes and inconsistent reporting to errors in customer, delivery, and billing workflows.

What data
Integration
covers

The Right Integration Approach for Every Requirement

Modern data integration does not rely on a single technology. The right approach depends on the data source, target system, required level of timeliness, and the business process involved.

Batch and File-Based Integration

Data is extracted, processed, and made available at defined intervals. This approach is particularly suitable for large data volumes and processes that do not require immediate updates.

ETL and ELT

Data is extracted from source systems and transformed either before or after it is loaded. We develop scalable pipelines for data warehouses, data lakes, and lakehouse platforms.

API-Based Integration

APIs enable standardized and controlled data exchange between applications. We design, integrate, and manage APIs, including authentication, versioning, monitoring, and access control.

Real-Time and Streaming Integration

Events and data changes are processed and transmitted as they occur. This enables operational systems, analytics platforms, and digital services to respond in near real time.

Change Data Capture

Changes in source systems are detected automatically and transferred to downstream systems without the need to reload complete datasets at regular intervals.

Event-Driven Integration

Systems communicate through business events rather than direct dependencies. This enables more flexible and loosely coupled architectures.

Data Virtualization

Data from different sources is made available through a shared access layer without necessarily being physically replicated.

Integration Platforms and iPaaS

Central platforms support the development, orchestration, and monitoring of data flows across cloud and on-premises systems.

Transforming
data

More Than Moving Data from A to B

Data integration often involves more than transferring information between systems. Data must also be prepared and refined to meet both business and technical requirements.

This includes:

• mapping different data models,
• standardizing formats and units,
• applying business and technical validation,
• cleansing and enriching data,
• matching and deduplicating records,
• aggregating and consolidating data,
• deriving new attributes,
• handling incomplete or erroneous records,
• maintaining historical records and traceability.

The result is data flows that not only work technically, but also deliver information that can be used reliably across the business.

API &
Integration
Platforms

Developing and Operating Interfaces with Control and Confidence

We help organizations build integration landscapes in which interfaces can be reused and operated securely, transparently, and reliably.

This includes:

• API strategy and API design,
• REST, GraphQL, and event-driven interfaces,
• API gateways and access control,
• lifecycle and version management,
• integration platforms and iPaaS,
• centralized monitoring and error handling,
• reusable integration components,
• connectivity across SaaS, cloud, and on-premises applications.

We select technologies based on your requirements, existing architecture, and long-term maintainability – not on vendor preference.

Orchestration &
Transparency

Managing Data Flows Reliably

We create transparency into:

• which data originates from which sources,
• which transformations are applied,
• which systems depend on a particular data flow,
• when processes have been completed successfully,
• where errors or delays occur,
• which quality and service levels are being achieved.

Orchestration, monitoring, logging, and alerting help ensure that data flows operate reliably in production and that issues are identified at an early stage.

Data
Integration
for
Analytics & AI

The Right Information at the Right Time

Analytics and AI rely on data from numerous operational sources. When that data is provided only intermittently, remains incomplete, or undergoes transformations that cannot be traced, reports and models lose their reliability and value.

We develop data pipelines that:

• connect relevant data sources,
• bring together structured and unstructured data,
• process both batch and real-time data,
• automate data quality checks,
• document data lineage and processing steps,
• provide data for analytics platforms and AI applications,
• integrate new sources flexibly.

The result is a robust data foundation for reporting, forecasting, automation, and AI in production.

Data
preparation

ETL, ELT, and Data Preparation

High-performance data integration provides the foundation for more efficient processes, reliable analytics, and new data-driven applications.

Today, integration is no longer limited to extracting data from different sources, transforming it, and loading it into a data warehouse. Depending on the architecture and use case, ETL or ELT processes, APIs, Change Data Capture, streaming, and other integration patterns may be required.

As part of the integration process, data can also be profiled, cleansed, standardized, validated, enriched, deduplicated, and consolidated. What matters is that the processed data meets the business and technical quality requirements of the respective use case.

The result is consistent and traceable data that provides a reliable foundation for business intelligence, advanced analytics, and AI. At the same time, it enables integrated views of customers, products, suppliers, and other key business entities across systems.

Our
Approach

From Integration Strategy to Stable Operations

Phase 1: Analyze the Landscape and Requirements

We assess systems, interfaces, data flows, and business dependencies.

Potential outcomes include:

• a system and data landscape map,
• an assessment of existing integrations,
• identification of critical data flows,
• evaluation of performance and stability,
• prioritization of use cases,
• technical and business requirements.

Phase 2: Develop the Target Architecture

We define the right combination of integration patterns and technologies.

Potential outcomes include:

• integration principles and a target architecture,
• selection of suitable integration patterns,
• an API and event concept,
• a batch, streaming, and CDC architecture,
• a security and authorization concept,
• a governance and operating model.

Phase 3: Implement Data Flows

We develop, test, and integrate the required pipelines and interfaces.

Potential outcomes include:

• ETL and ELT pipelines,
• APIs and events,
• mappings and transformation logic,
• data quality checks,
• error-handling and restart mechanisms,
• migration of existing integrations.

Phase 4: Ensure Stable Operations and Continuous Development

We establish monitoring, documentation, and clear responsibilities.

Potential outcomes include:

• end-to-end monitoring,
• logging and alerting,
• operations and support processes,
• performance optimization,
• automated testing,
• continuous modernization.

Business
value

Less Complexity. Faster Processes. Better Data.

Faster Business Processes

Automated data flows reduce manual transfers, waiting times, and process interruptions caused by disconnected systems.

Lower Integration Costs

Reusable interfaces, standardized patterns, and centralized platforms reduce development and maintenance effort.

More Consistent Information

Harmonized data models and controlled transformations help eliminate inconsistencies across systems.

More Timely Decisions

Real-time and streaming integration makes relevant information available faster for operational processes and analytics.

Greater Scalability

New systems, data sources, and business models can be integrated more flexibly into the existing landscape.

More Reliable Digital Services

Monitoring, error handling, and transparent dependencies improve the reliability of business-critical processes.

A Stronger Foundation for Analytics and AI

Integrated and traceable data flows provide a reliable data supply for reporting, models, and automation.

Together with our colleagues from the e-commerce sector, we can support you throughout the entire data life cycle.