To combine data from multiple sources into a single, consistent view.

To improve data quality and accuracy.

To enhance data security and privacy.

To reduce data storage costs.

Data heterogeneity

Data redundancy

Data inconsistency

All of the above

To merge data from multiple sources into a single, unified repository.

To improve data accessibility and usability.

To enhance data security and governance.

To reduce data storage costs.

Improved data consistency and accuracy.

Enhanced data accessibility and usability.

Simplified data management and analysis.

All of the above

Data integration combines data from multiple sources, while data consolidation merges data into a single repository.

Data integration focuses on data quality and consistency, while data consolidation focuses on data storage and management.

Data integration is a one-time process, while data consolidation is an ongoing process.

None of the above

ETL (Extract, Transform, Load) tools

Data virtualization tools

Data warehousing tools

All of the above

Data quality ensures that the data used for integration and consolidation is accurate, consistent, and complete.

Data quality helps identify and correct errors and inconsistencies in the data.

Data quality improves the overall reliability and trustworthiness of the integrated and consolidated data.

All of the above

Data warehousing

Data federation

Data mart creation

All of the above

To store and manage large volumes of integrated data.

To provide a central repository for data analysis and reporting.

To improve data accessibility and usability for business users.

All of the above

Data heterogeneity

Data redundancy

Data inconsistency

All of the above

Metadata provides information about the structure, content, and usage of data.

Metadata helps identify and resolve data inconsistencies and errors.

Metadata facilitates data discovery and understanding for users.

All of the above

Establish clear data governance and stewardship policies.

Implement data quality controls and monitoring.

Use standardized data formats and structures.

All of the above

Data integration combines data from multiple sources, while data migration moves data from one system to another.

Data integration focuses on data quality and consistency, while data migration focuses on data movement and transformation.

Data integration is a one-time process, while data migration is an ongoing process.

None of the above

Hub-and-spoke architecture

Data federation architecture

Data warehousing architecture

All of the above

To store and manage large volumes of raw data in its native format.

To provide a central repository for data analysis and exploration.

To facilitate data sharing and collaboration among different teams and departments.

All of the above