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Tony Andris Founder, Partner, CTO

What is Data Virtualization?

Data Virtualization is an approach to unify data from multiple sources in a single layer so that applications, reporting tools, and end users can access the data without requiring details about the original source, location, and data structures.

The unified data layer is virtual. Unlike Extract Transform Load (ETL) processes, the majority of the data remains in place, and is queried on-demand directly from the source systems. This reduces mass data movement and brings the end user closer to the original data sources – minimizing the challenges and costs of multi-level data movement and transformation. Data Virtualization is faster and more flexible than traditional Data Integration (DI) – no mass data replication, no new data architecture, no mass data storage.

The unified data layer serves as an abstraction layer for creating data mashups across sources and formats. It reads and analyzes data from multiple sources including structured, unstructured, web, and big data; and makes the data available for integrated querying, reporting, and analytics. Data consumers don’t need a deep understanding of the complex structures and different formats of the underlying data sources. The mashed-up data is accessible through different interfaces including SQL, web services, data dumps/CSV, and pre-formatted reports.

WHAT ARE THE BENEFITS OF DATA VIRTUALIZATION?
  • Enables timely insights across disparate data sources
  • Delivers high-performance
  • Reduces data replication and storage
  • Increases speed and agility of integrated data access
  • Reduces development and support time
  • Hides complexity of underlying data structures
  • Enables multi-channel and multi-mode data access

Data virtualization use cases

The biggest value proposition of data virtualization is quick and efficient access to integrated data from multiple sources. Business users can then generate valuable insights by combining data across sources without having to embark on a major Data Integration project.

Data virtualization is very well suited for:

  • Agile Business Intelligence (BI)
  • Operational reporting, data visualization, and analytics
  • 360 views (customer, product, entity)
  • Real-time and point-in-time reporting and analytics
  • Integrated data services and Service Oriented Architecture (SOA)
  • Data discovery and concept validation (e.g., prior to major DI investment)

Examples of use cases:

  • Hospitality – Customer 360 understanding and experience management
  • Call Center Operations – Value added customer data and decision support
  • Multiple Industry Verticals – Data bridging across data warehouses and data lakes
  • Finance/Banking – Real-time fraud detection and warning
  • Health Care – Patient 360 views and data services across health information portals
  • Real Estate – Automated multi-site metrics and performance measures

DATA VIRTUALIZATION AND DATA WAREHOUSING

The enterprise landscape is filled with disparate data sources including multiple data warehouses, data marts, and/or data lakes. Data virtualization can efficiently bridge data across data warehouses, data marts, and data lakes without having to create a whole new integrated physical data platform. Existing data infrastructure can continue performing their core functions while the data virtualization layer just leverages the data from those sources. This aspect of data virtualization makes it complementary to all existing data sources and increases the availability and usage of enterprise data.

Hybrid data management and data platforms are here to stay and data virtualization is a valuable tool in the enterprise data management toolkit to enable quick and easy access to integrated data from multiple sources.

Data virtualization may also be considered as an alternative to ETL and data warehousing. Data virtualization is inherently aimed at producing quick and timely insights from multiple sources without having to embark on a major data project with extensive ETL and data storage. However, data virtualization may be extended and adapted to serve data warehousing requirements also. This will require an understanding of the data storage and history requirements along with planning and design to incorporate the right type of data virtualization, integration, and storage strategies, and infrastructure/performance optimizations (e.g., streaming, in-memory, hybrid storage).

Is data virtualization for you?

Do you have data sources of different technologies and data formats in different places?
Do you have data sources of different technologies and data formats in different places?
SOLUTION
dataWerks connects to any data source of any format and enables integrated querying
Do you need a high performance layer to query and mashup data from multiple sources?
Do you need a high performance layer to query and mashup data from multiple sources?
SOLUTION
dataWerks enables high performance querying and aggregation through its references
Do you want to respond faster to your business intelligence and analytics needs?
Do you want to respond faster to your business intelligence and analytics needs?
SOLUTION
dataWerks can be implemented in days instead of months, changes are also very quick
Do you want to reduce mass data replication and storage?
Do you want to reduce mass data replication and storage?
SOLUTION
dataWerks does not replicate data – it only references data needed for searching, filtering, and joining
Do you want real time insights using up to date data?
Do you want real time insights using up to date data?
SOLUTION
dataWerks stays connected to data sources picking up new data or changes as they occur
Do you want a scalable solution that adapts to increasing data volume and complexity?
Do you want a scalable solution that adapts to increasing data volume and complexity?
SOLUTION
dataWerks infrastructure is easily scalable and uses commodity hardware

SOLUTION

Avoid data replication. Instead, virtualize your entire data set from all data sources, regardless of structure or format. Access data at its source and when it is created to enable the use of the latest information from all applications. By consistently running queries against your entire data set within milliseconds, you enable your business to make optimal decisions and empower your staff to generate real time insights.

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