Unified Data Architecture

Unified Data Architecture is about covering all aspects of data with respect to organizational architectures. There is no pre-defined Unified Data Architecture for everyone.

Let’s see how many Architectures we can have, in an organization with respect to Data.

• Architecture for Source Applications i.e., OLTP
• Architecture for Data Warehouse i.e., OLAP and Cubes
• Architecture for Data Lake, Delta Lake, Data Mesh, Data Vault, Business Vault, Data Hub, Data Fabric
• Architecture for Advance Analytics i.e., for Machine Learning and Deep Learning etc.
• Data Virtualization Architecture

As highlighted above, if not all but there can be many Data Architectures in-place in every organization. In the current era, where data has become the central focus element for everyone, one must design Unified Data Architecture for 30,000 feet view before designing Data Architectures for each component mentioned above.

Just like Enterprise Architecture is about Business Architecture, Application Architecture, Technical Architecture and Data Architecture, all these must follow Enterprise Architecture standards. Same way, all Data Architectures in an organization must follow Unified Data Architecture standards.

Few approaches to build Unified Data Architectures

• Enhancement in existing Legacy Architecture
• Logical Data Warehousing and Data Virtualization Architecture
• Organizational Driven Data Architecture like Data Lake, Data Warehouse etc.
• Business or LOB Driven Data Architecture Like Data Mesh, Data Mart etc.
• Multi-Cloud Architecture
• Hybrid Architecture (on-prem and cloud)

The elements involve in most of the Unified Data Architectures

• Data Produces i.e., source systems.
• Data Consumption Layer i.e., ETL/ ELT processes
• Data Storages i.e., Data stores
• Advance Analysis
• Data Serving Layer
• Data Management i.e., metadata, data quality, data security & privacy, data governance, metadata, master data, reference data, etc.