Data Virtualization Vs. Data Warehouse: Which is Better?

Companies often struggle to put data to work due to various reasons. One key reason is the ever-increasing volume and variety of data generated. Secondly, the data produced resides in siloed applications and databases that do not integrate. This makes it difficult to achieve seamless data access, security, and governance. As a result, several data integration solutions have emerged to ease data management. Data virtualization and warehouses are two common technologies used to manage data integration problems.

But what is data virtualization? What is a data warehouse? How do these two solutions differ? And which one is better? Let’s find out more about solutions to help you get the most value from your data.

What is Data Virtualization?

As the name suggests, data virtualization provides an abstract logical layer that virtually connects data from multiple sources without moving the data to a centralized storage system. This means all the development tasks involved in building data integration solutions in no longer necessary. So, data virtualization eliminates the need to create API calls and data pipelines, increasing the speed-to-market.

Data virtualization lets you integrate data from different sources, maintaining the data in place. This allows you to generate dashboards and reports, creating value from business data. You can think of it as an alternative for data warehousing, whereby you gather data from different sources and store a copy in a new data store.

What You Should Know About Data Virtualization

Here are some key things you should know about data virtualization:

• Approach: Data virtualization provides a unified, logical view of data from different sources without physically moving the data or integrating the data sources.
• Data latency: Data virtualization provides direct access to data from different sources. This minimizes latency, facilitating near-real-time data access.
• Data integration: Data virtualization allows real-time data integration without the need for data replication.
• Flexibility and agility: Data virtualization provides greater agility because you can add new data sources easily. You don’t need to change the underlying ETL processes or data structures.
• Data governance: Data virtualization often poses challenges related to data governance and quality. This happens because data is accessed in real-time from multiple sources without centralized control.
• Complexity: Implementing data virtualization is often challenging due to its complexity. You must maintain data connections, handle security, and ensure performance across different sources.

Data Warehouse

Unlike data virtualization, a data warehouse collects data from different sources for centralized storage. It is a centralized repository for structured data. With data warehousing, you extract data from multiple systems, transform it to clean it up, and replicate it before loading it into the warehouse. This means additional operational overhead is necessary to build and maintain a data warehouse, increasing technical debt.

What You Need to Know About a Data Warehouse

Here are some things you need to know about data warehouses before implementing them:

• Approach: A data warehouse is a centralized repository that integrates and stores historical and structured data from different sources. It involves ETL processes to consolidate and structure data for analysis and reporting.
• Performance: A data warehouse is optimized for complex aggregations and queries, providing fast query response times for analytical purposes.
• Data integration: A data warehouse consolidates data from different sources into a centralized schema, enabling complex analytics and reporting across the organization.
• Data consistency: A data warehouse stores historical data. This allows users to track changes and perform trend analysis over time. Also, the data is transformed and cleansed, ensuring accuracy and consistency.
• Implementation period: Since building a data warehouse involves developing and implementing ETL processes, it is more time-consuming.
• Scalability: A data warehouse can manage large data volumes. It is designed with scalability in mind making it ideal for enterprise-level analytics.

Data Virtualization Vs. Data Warehouses

Although data virtualization and data warehouse differ significantly, they exhibit some commonalities. Here are some similarities and differences between the two data management solutions:

Similarities

1. Purpose: Both solutions address big data integration challenges by making data more accessible to business users.
2. Category: Both options address data architecture and integration.

Differences

On the other hand, data virtualization differs widely from a data warehouse. Here are some key distinguishing factors:

• Data Location: As initially stated, data virtualization offers an abstract logical layer that virtually integrates data from different sources. This means data remains where it is. On the contrary, a data warehouse architecture involves migrating data to a centralized storage repository.
• Implementation time: Data virtualization enables faster integration of data sources since you don’t need to replicate the data – it stays where it is. On the other hand, building a data warehouse is time-consuming. You must design and implement extract, transform, and load (ETL) processes that require upfront planning.
• Agility: The data virtualization architecture provides a new way of connecting data across silos in an organization. The main differentiator is that it covers both analytical and transactional systems. Transactional data is constantly changing data to support applications such as CRM. On the other hand, a data warehouse only handles analytical data. Analytical data is historical, unchanging, or immutable.

Which One is Better - Data Virtualization or a Data Warehouse

Data virtualization and data warehouses are both beneficial data management solutions. Which is better depends on your requirements and specific use case.

For instance, data virtualization is suitable when you need real-time access to information from different sources. Also, this option would be more applicable if you want more flexibility in integrating new data sources. It is helpful in scenarios such as data services integration, agile analytics, and data exploration. Other instances where data virtualization would be more suitable include when:

• You have too much information at the edge to move into a data warehouse.
• You cannot move your data into data warehouses because of compliance limitations.
• You want to handle unplanned queries that need access to data stored outside a data warehouse.

On the other hand, a data warehouse would be a better fit when you need centralized storage for historical data analysis, reporting, and enterprise-wide analytics. This solution is commonly used for data mining, business intelligence (BI), and decision support systems (DSS).

Final Thoughts

Data virtualization and data warehouses are both great data integration solutions. Choosing between the two depends on your requirements and use cases. Therefore, you should carefully assess your data integration goals and business requirements to determine the one that suits your needs. However, if you find your needs cutting across the strengths of data warehouses and data virtualization, combining them would be a more appropriate solution. This will help you get the most value from your data.