In data warehousing and enterprise data management, what is physical data integration and how does it combine data from multiple source systems into a central store such as a data warehouse?

Introduction / Context:
Physical data integration is an important concept in data warehousing, business intelligence, and enterprise data management. Organisations usually have many operational systems, each with its own database, formats, and semantics. To analyse information across the entire business, they often build a central data warehouse or data mart where data from these diverse sources is physically brought together. This question tests understanding of what physical data integration means and how it differs from purely virtual or logical integration approaches.

Given Data / Assumptions:

• There are multiple heterogeneous source systems, such as enterprise resource planning, customer relationship management, and legacy applications.
• The organisation wants a unified, consistent view of data for reporting and analytics.
• A central repository such as a data warehouse or operational data store is available.
• Data must be moved, transformed, and stored, not only accessed virtually at query time.

Concept / Approach:
Physical data integration refers to processes that copy and consolidate data into a target store. Typically, this is implemented through extract, transform, load pipelines. Data is extracted from source systems, cleansed and transformed into a common model, and then loaded into a central database. Once loaded, the integrated data can be indexed, summarised, and optimised for analytical queries. This approach contrasts with virtual data integration, where queries federate data in place across multiple systems without physically consolidating it.

Step-by-Step Solution:
Step 1: Identify that physical integration involves moving data, not just linking to it.Step 2: Recognise that extract, transform, load tools or similar pipelines read from operational sources on a schedule.Step 3: During transformation, data is cleaned, deduplicated, conformed to common dimensions, and mapped into a unified schema.Step 4: The transformed data is then loaded into target tables inside a warehouse, data mart, or operational store.Step 5: Users and analytics tools query the integrated repository rather than thousands of fragmented transactional tables in the original systems.

Verification / Alternative check:
A useful way to verify the definition is to think about what exists after the integration process completes. With physical data integration, you can backup, index, and optimise the integrated repository independently of the sources because the data is actually copied there. If the warehouse server is temporarily offline, the integrated data still exists. This distinguishes physical integration from virtual solutions that depend on live access to all sources at query time with no persistent consolidated store.

Why Other Options Are Wrong:
Option B describes virtual data integration or data federation, where no physical copy is maintained and queries span source systems dynamically. Option C is about physical hardware layout and not about integrating data. Option D refers to a user interface layer that combines screens but does not address the movement or consolidation of underlying datasets. Therefore these options do not capture the core idea of physically extracting, transforming, and loading data into a central store.

Common Pitfalls:
A common pitfall is to assume that physical integration is always real time. Many warehouses load data in batches, for example nightly, which introduces latency. Another mistake is underestimating the effort required to clean and reconcile conflicting definitions across systems. Without careful data quality management, a physically integrated warehouse can still contain inconsistent or duplicate records. Architects must balance performance, freshness, and complexity when designing physical integration pipelines and should document business rules clearly so that future changes do not break integrated views.

Final Answer:
Correct answer: A process that physically extracts, transforms, and loads data from multiple sources into a consolidated database or data warehouse