Data is the lifeblood of the digital economy. But making sense of exploding volumes of information siloed across disparate systems represents an existential challenge. Data virtualization serves as a strategic technology for breaking down data barriers holding back innovation.
This comprehensive guide explains everything you need to know about data virtualization – what it is, benefits, how it works, use cases, and recommendations for getting started.
Why Data Virtualization Matters
Here‘s the data dilemma every modern enterprise faces:
Valuable information assets constantly expanding in volume, velocity, and variety remain trapped in internal databases, cloud applications, analytical systems, unstructured files, and more. Important insights lie buried across disconnected islands inmates accessible to stakeholders that could be leveraged to drive better decision making.
Meanwhile, traditional data integration methods like hand coding custom connectors, complex ETL pipelines, or consolidating into massive centralized warehouses incur steep complexity, latency, overhead, and inflexibility bottlenecks. Emerging analytics use cases around everything from IoT to machine learning often cut across so many recent and evolving systems that they prove impractical to enable.
The resulting data rich but insight starved environment severely hampers an organization‘s ability to achieve strategic objectives around:
- Delivering personalized, omni channel customer experiences
- Optimizing pricing in competitive global markets
- Streamlining operations through data driven decisions
- Accelerating product innovation cycles
- Managing risk in dynamic regulatory environments
Data virtualization breaks down these barriers to data value creation by providing simplified, unified real-time access to vital information from across the enterprise – without the need to physically move and consolidate data first. This logical view empowers both IT and business teams to more easily leverage comprehensive, trusted datasets tailored to their use case requirements.
The benefits over traditional approaches allow far greater business agility, customer centricity, speed to insight, scalability, and cost efficiency fueling today‘s digital leaders. Now let‘s explore what exactly data virtualization entails under the covers…
Demystifying Data Virtualization Concepts
At a basic level, data virtualization inserts an abstraction later to decouple physical data from its consumption. This virtual intermediary essentially fools applications and end users into thinking incohesive sources scattered across environments appear as one seamless, consistent data resource.
In reality, sophisticated data virtualization platforms connect out to relevant systems in real time to query across them, handle security, deliver information back to requestors within required SLAs. But by hiding away technical complexity behind intuitive business terms and logic, consumers enjoy much simpler access without deep data environment expertise.
It helps to clarify some key concepts within data virtualization architecture:
| Concept | Definition |
|---|---|
| Abstraction Layer | Presents users with reusable business concepts and relationships as opposed to requiring database schemas and scripts |
| Connectors | Integrate new data sources through built-in and custom connectors like JDBC drivers |
| Federation | Bring together distributed data under a single query |
| Transformation | Applies rules to modify, aggregate, cleanse, etc. data from sources dynamically at query runtime |
| Unified semantic model | Defines single shared business vocabulary abstracted across sources aligned to usage |
| Security framework | Maintain data sensitivities applying row and column level security dynamically on cached result sets |
| In-memory caching | Stores reusable query results to significantly improve performance |
| Change data capture | Propagate underlying source data changes through trigger based or log–based change data capture |
| Lifecycle management | Tools for DevOps teams to streamline ongoing management after initial implementation |
While individual capabilities vary across solutions, these elements comprise the core building blocks of data virtualization platforms. Now let‘s walk through how this all comes together…
How Data Virtualization Architectures Function
Modern data virtualization solutions incorporate a range of components – connectivity, abstraction, processing, caching, and administration – seamlessly integrated to deliver simplified data access across environments.
Data virtualization platforms simplify access by abstracting away technical integration challenges
Stepping through the flow:
1. Connect – Prebuilt and configurable connectors extract data from relational, NoSQL, cloud applications, social media APIs, unstructured data stores and more
2. Model – Business users access data through an intuitive semantic model presenting information using reused domains, KPIs, metrics etc rather than database artifacts
3. Query – Users and applications issue SQL/MDX queries or search semantics against virtual objects
4. Optimize – Query planner evaluates distributed processing tradeoffs to determine optimal data federation/transformation logic
5. Retrieve – Platform accesses permitted sources on demand to retrieve responsive data
6. Transform – Data harmonization rules cleanse, shape, aggregate, enrich data from sources dynamically
7. Cache – Results sets cached in memory across user sessions improving performance
8. Secure – Row and column level security filters sensitive data aligned to policies
9. Render – Information presented back to requestors within required SLAs
By leveraging robust metadata management and optimizations, data virtualization removes significant integration burden delivering responsive unified data access using existing infrastructure.
Now let‘s look at how leading solutions…
