Watch full webinar here: https://bit.ly/3EEU2vK Uber, the world’s largest taxi company, own no fleet; AirBnb , the largest accommodation provider owns no real estate. The extraordinary way that companies is growing fast, globally and with little investment, was with thin layers on top of a complex system of others’ goods or services that owned the customer interface. In Digital transformation- Data Minimization sometimes very useful to deliver business value rapidly without physical data redundancy – specially for seamless data migration from OLTP, OLAP, Legacy platforms for quick data domain/Data product access for incremental value until the desired Architecture/data estate evolve. To achieve the same -Data virtualization logically allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted at source, or where it is physically located, and can provide a single customer view of the overall data. While implementing next-gen solution leveraging DV, it has certain set of key considerations and caveat with focused long term strategy, Target state Architecture and use case.

1. Logical Data Fabric: The Future of Data
Management and Analytics

2. #DenodoDataFest
Rapidly Enable Tangible Business Value
through Data Virtualization
Principal & Germany Data Engineering
Guild Head– AI & Analytics Practice,
Cognizant Technology Solutions
Supriyo Chakraborty

3. © 2021 Cognizant
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Relevance of Data Virtualization
Irrespective of tool, Self-service analytics initiatives are hindered by following key challenges at the data level
which Data Virtualization solutions can solve
Information is spread across disparate systems
Different data storage Layer
Time to Market
Lack of Governance
Untraceable data Lineage
Seamless Unification
Common Data access & Security Layer
Faster go to Market & incremental value
Single point of access enables Strong Governance
Data Lineage and Catalog

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Key considerations for Data Virtualization implementation
Before building a Data Virtualization solution, it is essential to understand what exactly are the source, consumption,
transformation and implementation considerations and how best the DV solution fits in the customer environment.
• Establish a single unified data-access point for all data: structured, unstructured, data that is spread across
the file system, and data from SQL/No-SQL databases. Build a bridge between big data sources and
relational database sources.
• Unify data security -Enable federated/heterogeneous joins of data residing in disparate locations/sources
• Encourage the agility of the development team by focusing on incremental value with quick access to data.
• Decouple applications and analytics from physical data structures, enabling data infrastructure changes while
minimizing impact to users.
• Accelerate data Delivery without the need of duplicate data physically and leverage data discovery
• Enable and accelerate system integration testing and user acceptance testing for migration projects
• Compare and reconcile data through the common semantic layer for hybrid cloud configurations
• Combine data-lake data with operational data to enable pseudo-real-time data feeds.
• Reduce data replication to enable data minimization and retain control over the entire data lifecycle.
• Re-deploy personnel from performing operational controls over data integration and monitoring extract,
transform, and load (ETL) processes improving digital efficiency.
• Increase Flexibility and self service allowing multiple consumption patterns once data has been provisioned
• Awareness and discovery of data through Data Catalog.

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Getting started..
“Every single cell in the human body replaces itself over a period of seven
years. That means there's not even the smallest part of you now that was part
of you seven years ago.” - Steven Hall
Before you begin, seek to understand all of the possible considerations
relating to data sources, consumption use case, Non-functional requirement,
data consumption, necessary transformations, and implementation, plan out
how the data virtualization solution will fit in with the existing environment,
and how it will add value in the Nirvana environment.

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