data architecture

Training process

Training needs analysis

If you have specific requirements regarding the training programme, we will carry out a training needs analysis for you. This will guide us on which aspects of the programme should receive greater emphasis, so that the training programme meets your specific needs.

What will you gain?

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.

Training programme

1. Introduction to data architecture

basic concepts,
data strategy. The role of data architecture in the organization's strategy,
connection with enterprise architecture,
vision, goals, metrics,
capabilities, principles, frameworks,
high-level data architecture,
data roadmap.

2. Data models

corporate data model,
modeling levels: conceptual, logical, physical. Roles of models,
most commonly encountered modeling notations,
transition from model to implementation.

3. Reference and master data (RDM/MDM)

definitions,
dictionaries, codes, classifications,
typical master data domains,
challenges related to master data management.

4. Metadata and metadata management

types of metadata,
metadata catalog and dictionary as a central point of knowledge,
metadata lineage (data lineage) and change impact analysis,
typical challenges related to metadata management.

5. Data storage - technologies and patterns

relational databases,
non-relational databases (NoSQL),
data replication.

6. Data integration - patterns and approaches

ETL, ELT, Reverse ETL,
streaming and batch integration,
Change Data Capture (CDC),
orchestration of integration processes,
API as a communication interface,
integration platforms (IPaaS, API Gateway).

7. Data warehouse and analytical modeling

Inmon vs Kimball,
Data Vault,
star schema and snowflake schema,
Data Mart as an access layer.

8. Modern data platform architectures

medallion architecture,
Data Lake,
Data Lakehouse,
Data Fabric.

9. Data Mesh and decentralized architecture

Data Mesh principles,
Domain Ownership,
data as a product,
data contracts,
self-service platform,
federated governance,
advantages and disadvantages of data mesh.

10. Data Quality and Data Governance

data quality dimensions,
reactive and proactive data quality management,
data profiling,
typical roles related to data governance,
data governance policies and processes.

11. Data security and privacy

GDPR and other regulations in data architecture,
data classification,
masking, anonymization,
access control (RBAC, ABAC, PBAC) and impact on IT Controls,
Privacy by design / security by design.

12. Data architecture as the foundation of BI, ML, AI

semantic layer,
data architecture supporting generative AI,
from reporting to intelligent decision-making systems,
automation and optimization of data management processes thanks to AI models.

What are the prerequisites for participating in the training?

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.