Data Integration, Data Quality, and Master Data Management

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What is data integration?

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Data integration is the process of combining, consolidating, and merging data from multiple disparate sources to attain a single, uniform view of the data.

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What are the key components of data integration?

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Key components include Data Ingestion, ETL (Extract, Transform, Load), ELT (Extract, Load, Transform), Data Streaming, Application Integration, and Data Virtualization.

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What is ETL in data integration?

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ETL stands for Extract, Transform, Load, which involves extracting data from sources, transforming it into a usable format, and loading it into a centralized location.

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What is ELT in data integration?

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ELT stands for Extract, Load, Transform, where data is extracted, loaded into the target system, and then transformed, suitable for large datasets and real-time integration.

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What is data streaming?

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Data streaming is the continuous movement of data in real-time from source to target, often used for analytics-ready data.

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What is the importance of data quality in data integration?

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Data quality ensures that the integrated data is accurate, complete, and consistent, preventing incorrect insights and decision-making.

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What are the steps in ensuring data quality?

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Steps include Data Cleansing, Data Standardization, Data Validation, and Data Profiling.

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What is Master Data Management (MDM)?

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MDM is the process of creating and maintaining a single, accurate, and unified view of an organization's core business data.

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How does MDM contribute to data integration?

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MDM acts as a centralized repository for master data, ensuring consistency across all systems and applications.

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What are the benefits of Master Data Management?

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Benefits include consistency across systems, improved decision-making, and integration efficiency.