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To solve these challenges, according to Gartner "through 2022, 60% of all organizations will implement data virtualization as one key delivery style in their data integration architecture". It is clear that data virtualization has become a driving force for companies to implement agile, real-time and flexible enterprise data architecture.
In this session we will look at the data integration challenges solved by data virtualization, the main use cases and examine why this technology is growing so fastly. You will learn:
- What data virtualization really is
- How it differs from other enterprise data integration technologies
- Why data virtualization is finding enterprise-wide deployment inside some of the largest organizations
11. DataSources
1989
2008
1996
2000
1995
1992
Our Time
2012Reporting
DataConsumers
Data Science Data Market PlaceAI/ML
Data
Warehouse
Hadoop
Data Lake
DataVirtualization
Layer
SQL API
Metadata
Management
Unified Virtual Data Marts
Centralized Access Control
Universal Access
To All Data Sources
Data Management Evolution
Data Transformation and Combination
Applications Databases Files Real Time Web Logs Social Sensors Cloud
Analytics Data Monetization
12. Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs”. Henry Cook, Gartner April
2018
It’s Time to Rethink … It’s Time to go Virtual
13. Source: “Gartner Market Guide for data virtualization”
Data virtualization technology can be used to create
virtualized and integrated views of data in memory (rather
than executing data movement and physically storing
integrated views in a target data structure), and provides a
layer of abstraction above the physical implementation of
data.
14. Logical Data Warehouse Architecture
Reporting
Analytics
Data Science
Data Market Place
Data Monetization
AI/ML
iPaaS
Kafka
ETL
CDC
Sqoop
Flume
RawDataZoneStagingArea
CuratedDataZoneCoreDWHmodel
Data Warehouse
Data Lake
Data Virtualization Platform
Analytical Views
Data Science Views
λ Views
Real-Time Views
DWH Views
Hybrid Views
Cloud Views
UniversalCatalogofDataServices
CentralizedAccessControl
Logical Data Warehouse
16. Self-service data services
Centralized metadata, security &
governance
Data abstraction
Zero replication, zero relocation
Real-time information
Five Essential Capabilities of Data Virtualization
17. Abstracts access to disparate data sources.
Acts as a single virtual repository.
Abstracts data complexities like location,
format, protocols
…hides data complexity for ease of data access by business
Enterprise architects must revise their data architecture to
meet the demand for fast data.”
– Create a Road Map For A Real-time, Agile, Self-Service Data
Platform, Forrester Research
1. Data abstraction
18. …reduces development time and overall TCO
The Denodo Platform enables us to build and deliver data services,
to our internal and external consumers, within a day instead of the
1 – 2 weeks it would take with ETL.”
– Manager, DrillingInfo
Leaves the data at its source; extracts only what is
needed, on demand.
Diminishes the need for effort-intensive ETL
processes.
Eliminates unnecessary data redundancy.
2. Zero replication, zero relocation
19. Provisions data in real-time to consumers
Creates real-time logical views of data across many
data sources.
Supports transformations and quality functions
without the latency, redundancy, and rigidity of
legacy approaches
…enables timely decision-making
Data virtualization integrates disparate data sources in real time or near-real time
to meet demands for analytics and transactional data.”
– Create a Road Map For A Real-time, Agile, Self-Service Data Platform, Forrester
Research
3. Real-time information
20. Facilitates access to all data, both internal and external
Enables creation of universal semantic models reflecting
business taxonomy
Connects data silos to provide best available information to
drive business decisions
…enables information discovery and self-service
Impressively quick turn around time to "unlock“ data from additional siloes and
from legacy systems - Few vendors (if any) can compete with Denodo's support
of the Restful /OData standard - both to provide data (northbound) and to
access data from the sources (southbound).”
– Business Analyst, Swiss Re
4. Self-service data services
21. Abstracts data source security models and enables single-point
security and governance.
Extends single-point control across cloud and on-premises
architectures
Provides multiple forms of metadata (technical, business,
operational) to facilitate understanding of data.
…simplifies data security, privacy, audit
Our Denodo rollout was one of the easiest and most successful rollouts of critical
enterprise software I have seen. It was successful in handling our initial, security, use
case immediately, and has since shown a strong ability to cover additional use cases, in
particular acting as a Data Abstraction Layer via it's web service functionality.”
– Enterprise Architect, Asurion
5. Centralized metadata, security & governance
22. • Data Virtualization offers an alternative approach to data integration and provisioning
• Very agile, iterative approach – involve business stakeholders at beginning and deliver quickly
• Does not use expensive bulk data loads for data consolidation and centralization
• Data is delivered on-demand, in real-time
• Five key capabilities of a Data Virtualization Platform
• Data abstraction, Zero (minimal) replication, Real-time information, Self-service information,
Centralized metadata, security, and governance
• Wide range of use cases
• From Analytics and Big Data to Cloud to Governance and Data Services
22
Summary
25. 25
Key Takeaways
✓ Data Virtualization is a key technology when building a modern data
architecture
✓ It provides flexibility and agility and reduces the time to deliver data to the
business by up to 10x
✓ Data Virtualization hides the complexity of a constantly changing data
infrastructure from the users
✓ In doing so, it allows you to introduce new technologies, formats, protocols,
etc. without causing user disruption
27. 27
Next Steps
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