Flashcards

Data Virtualization

Data integration approach

Data Virtualization is an approach to data integration that allows for the creation of virtual views of data from multiple sources, without the need for physically moving or copying the data. This approach enables real-time access to data from disparate sources with minimal impact on performance and storage cost.

5 minutes 5 Questions

Data Virtualization is a data integration approach that allows applications to access and query data across multiple disparate sources without moving or copying the physical data. It creates an abstraction layer that presents data from various sources as if it were from a single virtual database. For Big Data Engineers, data virtualization provides several advantages. It enables real-time access to data residing in different systems like data lakes, cloud storage, NoSQL databases, and traditional relational databases. This is particularly valuable when dealing with massive volumes of data where ETL processes would be time-consuming and resource-intensive. The technology works by creating a semantic layer that maps to source systems while handling complexities like differing data formats, query languages, and protocols. When a query is made, the virtualization platform transforms it into source-specific queries, executes them, and consolidates the results. Key benefits include: 1. Reduced data duplication and storage costs 2. Near real-time data access 3. Simplified data governance since data remains at the source 4. Agility in responding to changing business requirements 5. Unified view across siloed data systems In Big Data environments, virtualization complements other integration approaches. While data lakes physically store raw data, virtualization provides a way to access both lake data and other enterprise systems through a unified interface. Implementation considerations include performance optimization, caching strategies, and security integration. Modern virtualization platforms offer advanced features like query optimization, data transformation capabilities, and metadata management. As data volumes continue growing exponentially, virtualization serves as a pragmatic approach to balance immediate data access needs with strategic data management goals.

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Big Data Engineer - Data Virtualization Example Questions

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Question 1

What is the primary advantage of using Data Virtualization?

Better data security Increased data accuracy Faster data processing Real-time access to data Lower data storage costs Improved data governance

Correct Answer: Real-time access to data

One of the primary advantages of using Data Virtualization is the ability to gain real-time access to data from disparate sources without having to physically integrate the data. This enables faster decision-making and more efficient use of data.

Question 2

What is the main challenge of performing Data Virtualization on unstructured data?

The technology required to virtualize unstructured data is too expensive Virtualizing unstructured data is no different than virtualizing structured data Virtualizing unstructured data can lead to data loss or corruption The main challenge of performing Data Virtualization on unstructured data is that unstructured data often lacks the metadata required for effective virtualization Unstructured data is too large to virtualize effectively

Correct Answer: The main challenge of performing Data Virtualization on unstructured data is that unstructured data often lacks the metadata required for effective virtualization

The main challenge of performing Data Virtualization on unstructured data is the lack of metadata. Unstructured data is often composed of files or documents that may lack the underlying structure of a relational database, and thus may not have the metadata required for effective virtualization. This can be overcome by using advanced techniques like machine learning to automatically classify and tag unstructured data.

Question 3

What is the process of combining data from different sources into a physical integrated view called?

Data Merging Data Federation Data Normalization Data Integration Data Accumulation Data Staging

Correct Answer: Data Integration

Data integration refers to the process of combining data from different sources into a physical integrated view. This is different from data federation, which involves combining data from different sources into a virtualized view without physically integrating the data.

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