Introduction to Data Virtualisation

Data virtualization now reaches the plateau of productivity on Gartner's hype cycle, signaling mainstream adoption, rising data localisation, and critical roles for data integration architectures.

Data virtualization defines a distributed data management approach that provides a single unified access point to query across on-premise and cloud sources, creating virtual views without moving data.

Explore how data virtualization enables zero data movement, faster platform migration, real-time data access, and cross-cloud integration via a single virtual data fabric with centralized governance and security.

Explore the data virtualization reference architecture and how it unifies diverse data sources into a single access point, abstracts complexity, enables in-memory querying, and supports centralized governance for analytics.

Clarify common misconceptions of data virtualization: it is not ETL, not a data store, and not a database. It retrieves data on demand and can export results to visualization tools.

Explore practical deployment of data virtualisation across six core patterns, including data warehouse solution, cloud migration continuity, cross-cloud data access, unified customer views, data surface layers, and enterprise data marketplace.

Explore how data virtualization forms a logical data warehouse by federating queries across diverse sources through a single interface, enabling agile analytics with unified governance.

Use data virtualization to simplify cloud migration and multi-cloud access by providing a single discovery and access layer across on-premises and cloud data, minimizing downtime and enabling live migrations.

Achieve a 360-degree, single view of the customer by integrating data from diverse sources with data virtualization, enabling on-demand access and zero-latency insights.

Leverage data virtualization to create a common data abstraction layer reused across applications, enabling governance, security, and consistency while acting as an api gateway with rest and safe apis.

Explore how data virtualisation underpins an enterprise data marketplace, delivering real-time data through a single access point with secure, searchable data services for reporting tools and dashboards.

Leverage data virtualisation to unify siloed data sources and deliver real-time, governed insights via self-service analytics for users, with data lineage, authoritative sources, and pre calculated, approved reports and dashboards.

Discover how data virtualization matches physical data warehouses by reducing processing at the virtualization layer and limiting network transfer, through techniques like query rewriting, delegation, push processing, and caching.

Learn query optimization in data virtualization, from static rewrites and branch pruning to dynamic cost-based plan selection, with techniques like join reordering and aggregation push down.

Explore massive parallel processing (MPP) in data virtualization, streaming large data to clusters for parallel query execution, with automatic partial aggregation pushdown to boost cross-source query performance.

Explore techniques to boost virtualization performance: rewriting queries, massive parallel processing, caching, and summaries. See how query summaries pre-compute aggregations to speed cross-source queries.

Explore how data virtualization enables centralized governance, data quality, and secure delivery across on-premise and cloud sources.

Discover metadata management in a data virtualization tool, from importing sources and cataloging fields, keys, and profiling to automated documentation, business glossary creation, and lineage with impact analysis.

Explore data catalog capabilities within data visualization tools to search, browse, and preview data and metadata for self-service analytics and governance.

Explore the security capabilities of a data virtualization platform, enabling centralized security, data privacy and protection, and fine-grained access across regions.

Understand how data virtualization platforms secure access via northbound and southbound authentication. Learn how LDAP, Active Directory, single sign-on, and credential pass-through support robust authorization and role-based controls.

Protects data assets by implementing fine-grained, role-based access controls in a data virtualization platform, including row and column permissions, LDP server integration, and cell-level masking.

Discover how data virtualization enforces privacy with a unified access layer across on-prem and cloud systems, role-based controls, and GDPR-aligned masking techniques like dynamic and static masking.

Audit logs in a data virtualization platform track who accessed what and when, and what changes occurred. They help financial services and pharmaceuticals meet government security and auditing requirements.

Explore the market landscape of data virtualization by comparing standalone tools, data integration vendors with virtualization capabilities, and providers offering extendable data access to external files and tables.

Explore market leading standalone data virtualization solutions and their differentiators, including on-premise and cloud deployment, data lineage, rollback and recovery, and auto query optimization with a semantic layer.

Explore data integration tool vendors that offer data virtualization, focusing on IBM Cloud Pak for Data and Informatica PowerCenter, their drag-and-drop interfaces, governance features, and impact analysis.

Explore how major dbms vendors enable data virtualization to extend access across relational databases, cloud, and Hadoop, with federation and push-down query capabilities.

Data virtualization provides a unified access point across on premise and cloud data, creating virtual views. It abstracts complexity, optimizes performance with caching, and supports governance, security, and self-serve discovery.

Educate the business on data virtualization benefits and maintain open communication to advocate access, agility, and consistency. Establish scalable ownership, governance, and phased adoption throughout the data virtualization program.

Identify the problem and use cases for data virtualisation, map legacy systems and file-based sources, then apply Moscow-rated requirements to drive market scans, vendor demos, and a proof of concept.