Database Management Systems

In Master Data Management (MDM), the Registry architectural style provides a centralized reference that doesn't store the actual data but maintains indices/pointers to source systems, enabling real-time access to master data across the organization.

Confidentiality (protecting from unauthorized access), Integrity (preventing unauthorized modification), and Availability (ensuring authorized access when needed) form the CIA triad, which are the three fundamental goals of data security.

ELT (Extract, Load, Transform) loads data first into the target system, then uses the target database's processing engine for transformations. This differs from ETL, which transforms before loading using a separate ETL tool.

OLAP cubes are fundamentally multidimensional structures that allow analysis across multiple dimensions (time, geography, product, etc.) simultaneously, enabling complex queries and aggregations that relational databases can't efficiently handle.

Enterprise Information Management (EIM) is a broad discipline encompassing data governance, and data governance is indeed a key focus area within EIM frameworks. EIM provides the organizational structure for managing information as an asset.

Aggregation in OLAP pre-calculates and stores summarized measures (sums, averages, etc.) at various dimensional levels. This optimization dramatically improves query performance by avoiding runtime calculations on large datasets.

Rotating (or 'pivot') in OLAP changes the dimensional orientation of the report - essentially rotating the cube to view the same data from a different perspective, swapping rows and columns to highlight different dimensions.

Report bursting minimizes database queries by generating separate report outputs for different recipients or groups from a single execution pass, rather than running the entire report multiple times. This reduces database load and improves performance. Option B is incorrect because report bursting executes once, not multiple times. Option C is incorrect because it actually reduces network traffic by sending targeted data.

An operational system, such as an OLTP system, is designed to run day-to-day business operations in real time using up-to-date, current data rather than historical summaries.

The staging area is the correct interim location for storing data extracted from source systems before it's transformed and loaded into the data warehouse. It provides a buffer where data can be validated, cleansed, and transformed without impacting source systems. A data mart is a subset of the data warehouse, ODS (Operational Data Store) stores current operational data, and staging is the correct answer.

Data warehouses are organized around important subject areas like customers, products, or sales, making them different from operational systems that are organized around business processes. They contain historical data (not just current), have consistent naming conventions, and are not updated by end users. Option C is correct because subject-oriented organization is a key characteristic of data warehouses.

Data profiling is the correct answer - it's the practice of examining data to understand its content, quality, and structure. It includes analyzing data patterns, identifying anomalies, and assessing data quality metrics. Data enrichment adds information, data caving is not a standard term, and data refinement typically refers to improving data quality after profiling has identified issues.

Data cleansing is the correct process for removing or correcting erroneous, incomplete, or old data from databases. It involves identifying and fixing data quality issues like errors, inconsistencies, and outdated records. Data eradication is not a standard term, data unloading refers to extracting data, and data purging typically refers to deleting old data regardless of quality.

Master data management (MDM) is the correct answer - it identifies and maintains a single, unified view of reference data (like customer, product, or supplier data) across the enterprise. MDM ensures consistency and accuracy of core business entities. Data Quality Management focuses on data quality, Metadata Management manages data about data, and Data Security Management protects data from unauthorized access.

ETL (Extract, Transform, Load) is recommended for large data volumes, low data quality, and frequent loads because transformations happen before loading, allowing for complex data cleaning and validation. ETL can handle poor data quality better than ELT (Extract, Load, Transform), which transforms after loading. ELTL adds logging, and EII (Enterprise Information Integration) provides virtual views.