If you‘ve been hearing the term data virtualization thrown around but aren‘t quite sure what it means, this comprehensive guide aims to explore all aspects from key concepts to real-world implementations.
With enabling technologies like cloud, AI and 5G propelling data-first strategies, the average enterprise today deals with over 1,000 data sources and counting. Unlocking integrated intelligence from such distributed systems is easier said than done.
This fragmented landscape strains IT resources trying to harmonize access. It also hampers users looking to drive decisions with analytics derived from complete, consistent and immediate data.
While traditional methods like data warehousing address these imperatives through physical consolidation, such approaches falter at the velocity and variety of change required today.
Enter data virtualization – an architecture that removes the need to replicate data in order to use it together. By providing an abstraction layer that delivers unified data services without disruption, it serves as connective tissue for the digital business.
Defining Data Virtualization
As we explore what data virtualization entails and how it contrasts from existing techniques, let‘s level set on some key concepts:
Virtual data layer – This metadata layer acts as an access interface, shielding users from technical intricacies of underlying sources. It understands connectivities, locations and formats of sources while presenting logical views of the unified data.
Data abstraction – By mapping how data elements across sources link together and handling consolidation, the complexities of translation are hidden from those needing information regardless of source formats or schemas.
Data services – Things like query optimization, data quality management and governance get embedded within the data virtualization layer. This improves ease-of-use along with performance, accuracy and security.
Logical data warehouse – The virtual layer provides connectivity to data "as served", across the real-time spectrum, essentially acting as an automated data warehouse constructed on the fly based on user needs.
ETL vs ELT – In traditional extraction, transformation and loading (ETL), data gets moved first before it gets processed. With data virtualization, transformation happens as late as possible in the process closer to consumption – hence ELT (extract, load, transform). This improves flexibility.
In plain terms, data virtualization makes data usage seamless regardless of its format, location or platform. Technologies like Denodo, Tibco Data Virtualization, Oracle Data Services and IBM Data Virtualization enable this next generation of data integration by creating and leveraging virtual data layers.
Now that the key terminology is clearer, let‘s examine why data virtualization has become essential.
The Case for Data Virtualization
While data warehouses laid the data consolidation groundwork and remain indispensable for historical reporting, modern analytics demands overwhelm traditional pipelines. Maintaining consistency across real-time systems, ephemeral data like IoT sensor streams, unstructured data like mobile clicked and myriad other sources taxes rigid ETL.
And this challenge only explodes as organizations aim to incorporate more data more rapidly to improve decision automation and AI recommendations. Downtime for maintenance cuts off intelligence. Security risks increase data access directly from sources. Data drift affects reporting accuracy. Opportunities get missed waiting on batch updates.
These pain points drive the need for data virtualization alongside existing warehousing and lakes. Leading research affirms the return on investment:
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McKinsey found that firms leveraging data virtualization improved time to market for new offerings by 80% while cutting IT costs over 60%
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Gartner noted over 65% of large organizations will have deployed data virtualization capabilities by 2023
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Forrester concluded data virtualization delivers analytics insights 3x faster compared to traditional methods
The most compelling driver for this shift? Real-time unified data access in a scalable yet trusted way.
Let‘s examine the must-have benefits this new data integration approach offers:
As shown above, data virtualization essentially acts as a logical data layer that breaks data silos to accelerate insights. You integrate information from across sources virtually, only pulling data at query time rather than ingesting copies. This adds responsiveness while minimizing disruption.
Specifically, data virtualization opens possibilities by providing:
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Immediate Availability: Through live query federation, you access unified views of distributed data without delays from batch scheduling
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Flexibility: Connectors abstract source technicalities allowing integration in days instead of months
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Trust: Governance controls like Row Level Security, dynamic masking and obfuscation manage access built-in
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Cloud Optionality: Deploy across on-prem, cloud or hybrid – even emulate cloud data services on-premises
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Agility: Add new sources without disrupting existing flows or descending into sprawl
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Efficiency: By only moving data on demand, costs of unnecessary replication get avoided
With capabilities like integrated caching, in-memory acceleration and machine learning optimization, the progressive data virtualization platform essentially serves as an automated accelerator for analytics and application workflows – both reactive and proactive.
Now the question becomes where can these joint benefits of automation, trust and visibility move the needle for business?
Key Data Virtualization Use Cases
The versatility of data virtualization allows enterprises to pursue a number of high-value use cases:
1. Customer 360° Applications
Stitching together insights around prospect and customer interactions historically requires extensive plumbing. With data virtualization, you can rapidly combine relevant slices from CRM, purchases, web clicks, call transcripts, mobile taps and more to create comprehensive views that enhance targeting and experiences.
2. SaaS Application Migration
Transitioning sprawling on-premise application portfolios to the cloud while maintaining availability poses data connectivity challenges. Data virtualization smoothly handles the staging by “cloudfying” data services first. This shields downstream systems from the shift behind the scenes.
3. IoT Analytics
Making sense of sensor event streams often necessitates correlation with contextual data like equipment age, location attributes, maintenance logs and operator details dispersed across operational repositories. Creating physical copies balloons storage while achieving only static reconciliation. Data virtualization federates these connections dynamically instead allowing fluid analysis.
4. Data Marketplace Monetization
Forward-thinking enterprises look to expose slices of information they collect externally in secure ways as value-added offerings. Rather than enable direct access that can leak vulnerabilities, data virtualization provides controlled data services for premium distribution.
5. Algorithm Training
The foundation for machine learning – properly configured, non-biased data – remains elusive given the spread across enterprise infrastructure. Virtualizing access provides a mechanism for data scientists to transparently interact with subsets as needed without unnecessary replication.
6. Embedded Real-Time Intelligence
More applications seek insights not as an afterthought but intertwined with transactions. This embedded decisioning relies on contextual model scoring as events occur based on historical patterns. Making such analytical data seamlessly available at run time is where data virtualization shines.
As evident, by abstracting away infrastructure constraints behind logical interfaces, data virtualization fuel data-driven initiatives that otherwise end up hamstrung by disconnects.
But given the breadth of use cases, success necessitates keeping some key considerations in mind…
Key Implementation Considerations
Deploying an agile logical data fabric requires balancing priorities around semantic consistency, integrated governance, performance SLAs and hybrid support:
Handle Historical Data Smartly
While data virtualization focuses on current information, analytics like AI/ML model training needs historical patterns. Blend virtualization with data lakes. Apply ELT toward persistence while enabling access across data lifecycles.
Embrace Polyglot Persistence
Critical to smooth adoption is integrating data virtualization with existing analytics platforms instead of a rip-and-replace approach. Look for data virtualization layers allowing unified exploration first before optimizing storage/processing.
Set Realistic Access Control
Making all data available with equal access seems flexible but lacks governance rigour. Conduct usage analysis to define policies – which roles see which attributes under which conditions. Build with guardrails, not afterthoughts.
Start with Business Analytics Flows
The biggest pitfall is overengineering the platform before proving business value. Prioritize building use case MVPs for highest impact analytics/applications first. Let solution expand iteratively on proven merits.
Performance Test Continually
Simulate production concurrent workloads early via shadow testing. Database tuning and horizontal scaling may still be required despite caching. Plan for capacity buffer through infrastructure-as-code architectures.
Instrument for Observability
Monitor query latencies by use cases selectively. Trace complicated multi-database joins. Set alerts for timeouts or SLA breaches. No integration averts aberrations without runtime telemetry built-in.
Integrate with Pipeline Orchestration
Connecting data virtualization with existing workflow schedulers like Apache Airflow allows seamlessly aligning batch and real-time execution. This brings scalability while keeping lineage tracking and SLAs consistent.
Containerize for Portability
To support cloud portability across public and private infrastructure, containerization using Docker helps. This abstracts underlying hardware allowing replication, recovery and autoscaling. Kubernetes further helps with declarative management.
Mitigate Risk with Blue/Green Deployment
Updating enduring traditional pipelines risks downtime despite best intentions. Adopt blue-green release approaches – dual environments to test updates before switching traffic minimizes disruptions during continual delivery.
The insights and frameworks above stem from real-world experience by data platform architects and enterprise customers leveraging data virtualization successfully. But theory only goes so far…
Let‘s look at some comparative outcomes.
How Does Data Virtualization Stack Up?
Evaluating data integration mechanisms inevitably centers around the capabilities below:
A couple key takeaways emerge looking at alternatives:
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Data virtualization fares significantly better on critical dimensions like agility, availability and accessibility compared to traditional methods
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It forms an optimal data access foundation for modern analytics pipelines coupled with existing assets
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For real-time use cases, data virtualization delivers unmatched flexibility and speed
Indeed most enterprises adopt a coexistant approach retaining their EDW and data lakes for persisting historical data while enabling logical views for emerging needs. This best of both worlds framework connects the trusted with the timely.
The data makes it apparent why data virtualization adoption escalates rapidly. Now let‘s look at options.
Evaluating Data Virtualization Solutions
The data virtualization market features mature solutions like Denodo, Tibco and offerings from tech giants like Oracle, IBM & AWS alongside innovators like Stonebond Technologies, Aiven and IOLAP.
While their capabilities overlap, critical differentiators exist across:
- Ability to optimize complex queries and data mashups
- Security controls around data obfuscation, access and life cycle management
- Flexibility for custom data services, extensions and caching optimizations
- Cloud native support with containerization and microservices
- Embedded data catalog for discovery with business glossaries
- Openness and interoperability with data ecosystem components
- Augmented data management via AI assistance
- Platform management and ability to scale workloads
In addition to functional aspects above, tools in this space differ significantly in terms of:
- Licensing models – perpetual, subscription, consumption based
- Ease of administration and transparency of on-premise, cloud operations
- Availability across public cloud marketplaces for unified procurement
- Vendor stability, experience in large scale implementations, R&D leadership
Understand where providers excel across the spectra above allows pairing technology strengths with enterprise needs and operating models.
For instance regulated industries lean toward locked-down features while modern digital natives favor automation friendly adoption. Review priorities before aligning solutions.
The Outlook for Data Virtualization
The confluence of pervasive demands around delivering analytics excellence faster while managing technology complexity better positions data virtualization as an indispensable capability.
Leading research confirms the move toward virtual data integration:
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MarketsandMarkets forecasts 25% growth annually for this space through 2026 reaching over $10 billion globally
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Gartner estimates that by 2025, 90% of organizations will leverage real-time access capabilities augmenting their data infrastructure
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451 predicts data virtualization to provide over 50% faster analytical outcomes at 40% lower TCO compared to status quo
Digital transformation prioritizes access and agility over merely aggregation. Technologies like DataOps, lakehouse architectures and Active Data further the potential by applying cloud scale principles across hybrid infrastructure.
In effect, future enterprise data platforms will unify best practices around events, exploration and governance with data virtualization playing the critical liaison role. The economics, extensibility and performance DNA of this new stack sets the stage for widespread mainstream adoption.
So in summary, if fragmented infrastructure and data sprawl play roadblock to your analytics vision today, take a closer look at how data virtualization can pave the path to unprecedented business insight through a logical data fabric that connects virtual to reality.
