03 July,2011 by Jack Vamvas
Is there a difference between a Database Architect and Data Architect ? I was talking with a colleague , who is creating a job description for a Data Architect. As we discussed the skillset and characteristics of the two roles – it became obvious there is some overlap – but the two functions are different
The Database Architect Role and Data Architect are involved in the same workflow – but become involved at different times . Depending on the organisation – there can be different configurations of these roles.
The Data Architect – also called a Modeler, looks at the wider use of data , they should be involved at the outset of the project ensuring : metadata , defining enterprise use ,logic data models are established. The process is independent of the RDBMS.
When the logical models are created ,the Database Architect decides which RDBMS and how the infrastructure is implemented. They tend be experts in one or more RDBMS systems. The database architect is concerned with performance, optimisation, capacity planning, data storage techniques and DBA challenges
As an added note : the DBA role will work with the Database Architect to develop the relevant knowledge in being to support the database systems.
The larger organisations may have 2-3 “physical” DBAs and 2-3 “logical” modelers. Quite often a Senior DBA will also act as the Database Architect
Data Migration Tools
Data Modeling – Enterprise Data Modeling with an emphasis on transactional enterprise data models meeting normalization rules
Data Integration - disparate source systems and disparate data formats.
Database Design – Structured methods for design
Data Architecture – Conceptual Model methods
Security Architecture expert
Database Engine in-depth
ETL tools – Designing and implementing ETL processes
Database Design Patterns – OLTP ,DSS
Database Usage Methods – OLTP , DSS
Data Storage Techniques. Defining database server IO profiles and mapping to storage methods such as DAS, SAN, SSD, RAID levels ,PCIe attached .
Database Optimization Techniques such as Partitioned Tables, Queues,sql programming, Exceution Plans, Data File management to support different usage ,full range of Backup and Restore strategies, Indexes, XML optimization
Performance Tuning – Strategies for improved performance across the whole Performance Stack
Disaster Recovery and High Availability - Techniques such as Linux Clustering,Tivoli Storage Automation,HADR, SAN Replication
Extending functionality, sometimes integrating with legacy systems
Integration with External Applications -
Let me know how these terms are applied in your organisation.