What is Data Virtualization? An In-Depth Guide for Data Teams [2023]

If you‘ve struggled with the complexity of integrating data across siloed systems and rapidly changing analytics needs, data virtualization may offer the agility your organization needs.

In this comprehensive guide, we’ll unpack everything data architects and analytics leaders need to know about data virtualization, including:

• Key benefits for analytics use cases
• How data virtualization architectures work
• Comparison to traditional ETL data warehousing
• Criteria for evaluating solutions
• Implementation best practices
• And more…

By the end, you’ll understand if data virtualization could help overcome your data integration challenges – and how to make this approach successful.

What Exactly is Data Virtualization?

Before diving into the details, let‘s clearly define data virtualization:

Data virtualization refers to solutions that provide unified access to data from multiple, heterogeneous sources via a layer that abstracts the technical complexities of data integration.

Rather than physically copying data into a warehouse, data virtualization connects to sources directly when queries are executed. This approach combines:

• Data federation – distributed queries across locations
• Abstraction layer – middleware hides complexity

Data virtualization architecture (click to expand)

When queries hit the virtual layer, the data virtualization engine determines relevant sources, executes federated queries, and performs any transformation logic before returning an integrated result set.

This simplified experience removes the need to replicate, move, and standardize data up front via ETL. The complexity is handled behind the scenes.

Now that we’ve defined the core concepts, let’s look at why data virtualization has become essential.

The Rising Importance of Data Virtualization

Gartner predicts that adoption of data virtualization tools will grow at around 20% annually over the next several years.

Several key factors are fueling this rapid growth:

The Data Explosion

• The world creates 2.5 quintillion bytes of data daily (IBM)
• Data volume growing at 55-65% per year (IDC)
• 90% of data created in last 2 years alone (IBM)

Trying to centralize all this distributed data via batch ETL has become infeasible.

The Rise of Cloud and Hybrid Environments

• >85% of enterprises have a multi-cloud strategy (Flexera)
• 49% of data now resides in the cloud (IBM)

Connecting data across traditional data centers and cloud services is extremely complex.

Agile Analytics

• 61% of organizations say they need to optimize analytics and decision-making (MIT)
• 42% struggle with inflexible analytics environments (Dresner)

Businesses need to rapidly integrate new data sources to meet emerging insights needs.

Real-Time Demands

• 65% of enterprises seeking to enable real-time analytics (Ventana)
• Streaming data market estimated at $50 billion by 2022 (MarketsandMarkets)

Batch processing cycles can’t keep up with the demands of real-time business.

Data virtualization solves many of the headaches created by these trends – enabling unified access to distributed data at scale, with flexibility, lower latency, and less replication.

Key Benefits of Data Virtualization

Let‘s explore some of the top reasons forward-looking organizations are adopting data virtualization:

1. Faster Time-to-Value

Implementing traditional data centralization with ETL is complex:

With data virtualization, you can skip these time-consuming tasks and start analyzing data in days or weeks instead of months.

2. Agility

• Add, remove, or change data sources without disruption
• Extend to new use cases and changing needs rapidly
• Experiment with new data combinations for deeper insights

3. Productivity

• Empower users with self-service access
• Focus IT on high-value tasks vs. plumbing
• Shift analytics teams from wrangling data to extracting insights

4. Cost Efficiency

• Avoid redundant copies and infrastructure
• Consolidate tools and leverage existing investments
• Start small and scale out as needs grow

5. Performance

• Query latest operational data in real-time
• Cache common queries to optimize speed
• Maintain performance across petabyte-scale data

For the right use cases, data virtualization can complement or even replace traditional ETL processes – accelerating delivery of business insights.

But how do you determine if your use case is a fit?

When to Consider Data Virtualization

Data virtualization brings the most value for certain analytics use cases:

✅ Real-time reporting – Analysis of streaming or transactional data

✅ Self-service analytics – Enabling business users to explore data

✅ Agile analytics – Rapid integration of new data needed for insights

✅ Data science – Quickly combining datasets for exploration

✅ Cloud analytics – Creating unified view across cloud data silos

✅ Master data management – Resolving conflicts and gaps in definitions

✅ Test/dev data – Provisioning virtual copies of data

However, traditional ETL may still be optimal for:

❌ Highly-optimized, high-performance analytics with cleansed and conformed data

❌ Use cases requiring ETL-style data transformation

Assess your specific needs to choose the right approach. Data virtualization and ETL can also complement each other in a data architecture.

Okay, convinced data virtualization belongs in your analytics stack? Let‘s explore how it actually works under the hood…

Data Virtualization Architecture Explained

Understanding the architecture will help you evaluate solutions. Key components include:

Data Sources

• Relational and NoSQL databases
• Apps, files, object stores, etc.

Abstraction Layer

• Provides integrated logical view
• Handles query federation and transformation
• Manages connectivity, caching, security

Data Services

• APIs for data access (ODBC/JDBC, REST, etc.)
• Enable consumption by BI, applications, etc.

Data Management

• Catalog, lineage, usage stats
• Template management
• Monitoring, scheduling, etc.

This simplified architecture removes complexity for the analytics user, while providing integrated access to distributed data in a scalable and performant way.

Now let‘s look at how leading data virtualization platforms stack up.

Top Data Virtualization Solutions

Many tools now provide some level of data virtualization capabilities. Below is a comparison of leading options:

Open source data virtualization tools like Apache Drill, Presto, and Apache Ignite are also growing in adoption. Cloud data platforms also incorporate virtualization features.

Consider your existing tech stack, use cases, and functional requirements when evaluating options. But beyond software, success requires following best practices…

How to Implement Data Virtualization Successfully

Follow these steps to ensure your data virtualization initiative meets its goals:

Start with a focused business problem – Resist “boil the ocean” scope creep and tie the project to tangible impact.

Assess existing architecture – Inventory your infrastructure and data landscape up front.

Define metrics for success – Quantify performance, cost, agility improvements and track them.

Tackle data quality issues – Profile sources to uncover inconsistencies and gaps needing cleanup.

Test thoroughly – Validate performance under load across priority usage scenarios.

Share insights, gather feedback – Involve users early and iterate based on their needs.

Start small, demonstrate quick wins – Focus your initial scope and expand based on proven value.

Plan for ongoing optimization – Data virtualization requires continuous tuning like any complex architecture.

With the right strategy tailored to your organization‘s needs and data ecosystem, data virtualization can deliver enormous value – saving time, money, and frustration on the journey to insights.

Does Data Virtualization Belong in Your Architecture?

Data virtualization is a powerful option for any organization struggling with:

• Complexity of integrating distributed data at scale
• Long delays delivering analytics on new data sources
• Drain on IT resources or data teams for data wrangling vs insight
• High costs of traditional extract, transform, and load processes

Hopefully this guide has helped shed light on how data virtualization works, key benefits and use cases, and best practices for implementation.

To discuss your analytics goals and data architecture needs in more detail, schedule a consultation with our team of data integration experts. We‘ve helped leading organizations across industries to successfully adopt modern data platforms – and can provide guidance tailored to your specific environment and challenges.