BEM content 

(for all partners) 

 Title/name of the credential  Data Warehouse (DWH) – Design, Integration and Data Management 
Function of the micro-credentials / purpose   The focus of this microcredential is to equip learners with foundational knowledge for creating and implementing effective DWH solutions, using Entity – Relationship (ER), dimensional model (Star and Snowflake schemas) and advanced data integration techniques. Learners will also gain expertise in data integration using Structured Query Language (SQL) Server Integration Services (SSIS) for incremental data loads. With this course, participants will be able to manage complex DWH systems supporting business intelligence and analytic processes. 

 
Possible target groups  Individuals of all backgrounds and ages with prior knowledge in SQL 

 
Branch/sector of application  Information Technologies  

Data Engineering 

Business Intelligence  

 
Fields of application / work environment  IT Infrastructure and Database Management 

Data Warehouse Teams 

Business Intelligence and Analytics 

 
Typical work/professional tasks 

 

 Designing DWH architecture and optimising its processes 

Structuring databases using Star and Snowflake schemas 

Executing data integration processes (ETL – Extract, Transform and Load) including incremental data load using SSIS 

Ensuring proper data integration, it’s consistency and accuracy for analysis, decision-making and other business intelligence operations 

 

 
Learning outcomes (personal and job related)  Knowledge  Skills  Competences 
Knowledge: 

In-depth understanding of core principles and usage of DWH design and architectures, and all its elements.  

Knowledge of database structuring of Star and Snowflake schemas, their differences and application 

Comprehensive understanding of data integration techniques and the role of SSIS in managing data loads. 

Understanding the importance of diligently managing multidimensional data, to ensure consistency and accuracy. 

 

Skills: 

Ability to: 

  • Design and implement DWH database using Star and Snowflake schemas; 
  • Execute data integration and ETL processes using  SSIS for incremental data loads; 
  • Manage multidimensional data for data consistency and reliability ; 
  • Optimise DWH performance for business intelligence purposes.  

 

 

Learning outcomes should be formulated in commonly accepted way, see the link:https://eurspace.eu/ecvet/pedagogicalkit/framework-for-defining-learning-outcomes-knowledge-skills-competence/ 

Can be used the formulation format of National Qualification Framework descriptors, adjusting and applying that format for relevant job. 

 

 

 

 

 

 
Validation   criteria  procedures 
Validation will be conducted through a practical assignment. 

 

Procedure:  

Students will need to complete a final project consisting of database design, data integration and ETL processes. Database design will be checked through their ability to design and implement a DWH database focusing on Star and Snowflake schemas, while data integration will be tested on their proficiency in executing incremental data loads and managing multidimensional data. 

  

Criteria:  

Successful delivery of a project that demonstrates ability to create a working DWH database and  successfully perform incremental data loads with SSIS. 

 

 
Recognised/accepted (documented by MoU)  Name of companies 

Target Group  

 
Provider(s)  Private EduTech companies, Vocational-Educational schools  

 
Additional information 

(if needed) 

 Entry level / prerequisites 

 

 Advanced knowledge of Structured Query Language. 

 

20 hours (10 theoretical + 1o practical) 
Possible duration (recommendation) 
Specific content (national) 

(if needed) 

 Position in the chain of educational programmes  Standalone micro credential 

 

 
Reference to NQF 
Credits