Data governance and data lineage: quality for AI
The success of AI initiatives depends on data lineage transparency and Data Governance maturity. Learn how to avoid poor data quality traps and prepare enterprise IT architecture for AI scaling.
INSIGHTS & PRACTICE
UnityBase
Practical guides to data, integrations, enterprise platforms and applied AI.
Choose a topic
65 publications
Event-driven architecture: Kafka vs RabbitMQ and reliable event delivery
Comparing Kafka and RabbitMQ for building reliable event-driven architectures. Learn how to avoid integration issues and ensure data integrity.
When to use ESB versus point-to-point integration
Comparing architectural approaches: when direct connections become inefficient and how implementing an integration bus helps avoid complexity during scaling.
System integration and MDM: architectural patterns for scaling without chaos
Transitioning from point-to-point integrations to standardized EIP patterns, API Gateways, and managed Master Data Management to preserve corporate data integrity.
Overview of BPM platforms: process orchestration using BPMN 2.0 and DMN standards
An analysis of architectural approaches to business process automation. Comparing Scriptum, Camunda, Bonita, and UiPath to eliminate shadow processes and enable flexible modeling.
Modern ECM architecture: balancing dynamic workflows and immutable archives
How to build a corporate ECM architecture that combines flexible approval workflows with ISO 15489-1 requirements and long-term legal validity of electronic signatures.
BPM and document management: defining the boundary
Documents store facts, while business processes manage actions. Distinguish between DMS/ECM and BPM to avoid data silos and securely integrate AI agents.
Managing mass URL injection and SEO threats
A sudden surge in indexed URLs may signal a security breach or a technical configuration issue. Learn how to localize threats and protect SEO without hasty actions.
Integration architecture patterns for AI readiness: from chaos to Data Fabric
AI readiness is defined by architectural data maturity. Transitioning from point-to-point integrations to a managed layer with a unified API contract prevents data processing errors.
Architectural defense against AI Prompt Injection and leaks
How to protect corporate LLM systems from Prompt Injection and data leaks at the architectural level in 2025, following NIST AI RMF and OWASP standards.
AI-Native System Architecture: Ensuring 'Keepalive' Quality and AI Risk Management
AI-native system architecture demands an integrated approach to quality and AI risk management. We examine frameworks, security platforms, and organizational factors.
Enterprise developer: from code to AI system management
AI-native development is transforming the role of the enterprise developer in 2026-2027, shifting focus from writing code to managing AI systems, security, and domain-specific models.