A coherent data architecture view - You will connect data strategy, enterprise architecture and roadmap planning into one clear picture, making it easier to align technical decisions with measurable business goals.
Stronger data modeling skills - You will learn how conceptual, logical and physical models differ and how to move from design to implementation, so you can build clearer and more implementation-ready solutions.
Practical MDM and metadata use - You will understand how to organize master data, dictionaries and classifications, and how to use metadata catalogs and lineage to assess change impact across systems faster.
Better technology choices - You will compare relational and NoSQL databases, replication patterns and storage approaches, so you can choose an architecture that fits data scale, structure and change dynamics.
More effective data integration - You will work through ETL, ELT, Reverse ETL, CDC, batch and streaming integration, plus APIs and integration platforms, helping you design smoother data flows across systems.
Confidence in analytics design - You will grasp the differences between Inmon, Kimball and Data Vault, and when to use star schemas, snowflakes and data marts to build a stronger BI-ready analytical layer.
Clarity on modern platforms - You will assess the strengths and trade-offs of Data Lake, Lakehouse, Data Fabric and Data Mesh, so you can choose the right balance between centralized and decentralized data models.
Stronger governance and security - You will learn how to combine data quality, governance roles, access policies, classification and privacy by design to support BI, ML and AI solutions in a compliant way.
At the organizational level, data architecture links strategy, operating principles, and execution planning. This section establishes core concepts, the relationship to enterprise architecture, and the role of vision, goals, metrics, capabilities, frameworks, high-level architecture, and the data roadmap.
From enterprise-wide structure to implementation details, data models organize how information is described and built. The section covers the corporate data model, conceptual, logical, and physical levels, common modeling notations, and the transition from model design into implementation.
Reference and master data create the shared vocabulary required for consistent processing across systems. The section defines RDM and MDM, covers dictionaries, codes, classifications, and common master data domains, then addresses the practical challenges of governing and maintaining them.
Metadata management provides the context needed to understand data assets beyond their technical storage. This part distinguishes key metadata types, shows how catalogs and dictionaries serve as a central knowledge point, and explains lineage, impact analysis, and common management challenges.
Relational and NoSQL technologies are examined from the perspective of storage patterns and architectural fit. The discussion clarifies where each database approach works best, how data structures differ, and why replication matters for availability, consistency, and distributed data management.
Building dependable data flows involves selecting integration patterns that match latency, source behavior, and target use. The section examines ETL, ELT, Reverse ETL, batch and streaming, CDC, orchestration, API-based communication, and the role of IPaaS and API Gateway platforms.
Designing analytical storage requires choosing the right modeling approach for reporting and access. Here the program contrasts Inmon and Kimball, introduces Data Vault, explains star and snowflake schemas, and positions the Data Mart as an access layer for analytical consumption.
Modern platform design is discussed through the architectural patterns most often used in large-scale data environments. The section compares medallion architecture, Data Lake, Data Lakehouse, and Data Fabric, focusing on their structure, operating model, and typical architectural use cases.
When organizations distribute responsibility closer to business domains, architecture must balance autonomy with consistency. This section covers Data Mesh principles, Domain Ownership, data as a product, data contracts, self-service platforms, and federated governance, including key benefits and trade-offs.
Reliable analytics depends on clear quality criteria and effective governance mechanisms. Participants work through data quality dimensions, profiling, and both reactive and proactive quality management, then place these practices within governance roles, policies, and operational processes.
Regulatory requirements shape how data architecture handles privacy, access, and control. This part addresses GDPR and related regulations, data classification, masking and anonymization, as well as RBAC, ABAC, and PBAC models in the context of IT Controls and privacy by design.
Semantic layer becomes the bridge between raw data assets and business use in BI, ML, and AI scenarios. The section explains how data architecture supports generative AI, extends reporting into intelligent decision systems, and enables process automation and optimization with AI models.
Database fundamentals - You should be comfortable with tables, relationships, keys and basic data operations, so you can easily follow the parts focused on modeling and data storage.
IT systems awareness - You should know the main building blocks of an IT landscape, such as applications, interfaces, databases and integrations, to relate the patterns to real solutions.
Analytics basics - You should understand the purpose of reporting, data warehouses and data flows, so you can better compare analytical approaches and architectural options.
Quality and security awareness - You should have a basic understanding of data quality, access rights and information protection, so governance, privacy and compliance topics are easier to grasp.
