Govern and Protect Your End User Information

Govern and Protect Your End User
Information
Nageswar Cherukupalli
Vice President & Group Manager, Infosys
Clinton Cohagan
Chief Enterprise Data Architect, Lawrence Livermore National Lab

LLNL-PRES-933672
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory
under contract DE-AC52-07NA27344. Lawrence Livermore National Security, LLC
Data Governance & Protection with Data Virtualization
Denodo Fast Data Strategy Conference 2018
Clinton Cohagan
Chief Enterprise Information Architect, WTE Program
Group Lead Manager, ASQ Division
Computation Directorate
April 12, 2018

Data Governance with Data
Virtualization

5
Business can
now make
faster &
better
decisions
with all data
accessible by
any tool of
choice
Data Virtualization Reference Architecture
IT Flexible Source Architecture
Business
Flexible Tool
Choice
IT can now
be more
strategic &
move at a
more
intentional
pace without
affecting
business
agility

All NNSA Sites Required Sharing / Integrating Data
Services
NSC NNSS
Y-12
Pantex
SRS
LANL
SNL
LLNL
NSC NNSS
Y-12
Pantex
SRS
LANL
SNL
LLNL
NSC
Pantex
LLNL
vs

Tackle GDPR (General Data Protection Regulation) with Data
Virtualization

GDPR Principles and Impacts
Comes into effect May 2018
Aims to give control back to all individuals
within the European Union over their
personal data
Extends the scope of the EU data
protection law to all foreign companies
processing data of EU residents
Affects how companies collect, use and
transfer personal data
Requires Accountability
▪ GDPR requires controllers to show HOW
they comply with principles
Promotes Protection and Accuracy
▪ Personal information must be accurate and
able to be corrected on request
▪ On-line access (360 degree view)

Data Services can Provide 360 Views Across the Enterprise
LDAP
XML API
SQL Server
DB
Oracle DB

Catalog of Data Services
▪ Browse and Search
▪ Descriptions, Categories, and Tags
▪ Relationships and Data Lineage
Data Discovery & Self Service Access
▪ Search Content of Data Services
▪ Customize and Save Queries
▪ Export to Local File
Data Governance & Data Quality
▪ Certified Data Services
▪ Web-based and Secure
▪ Does not require SQL expertise
Denodo’s Dynamic Data Catalog Enhances Data Service Usability

Data Virtualization – Enabling
Cloud Adoption and Data
Governance
12

Cloud Adoption – Trends and Challenges
13
Cloud Adoption Trends
• More and more Enterprises are adopting Cloud platforms and
solutions due to many benefits that such solutions provide:
- Cost reduction
- Agility and Time to Market
- Elasticity and Scalability
- Drive Innovation
• New data stores and applications are being created or migrated to
the Cloud. Cloud native data stores (e.g. Azure SQL DW, AWS
Redshift, etc) are being leveraged. Customers are also adopting PaaS
and SaaS offerings and sometimes IaaS offerings from Cloud
providers such as AWS, Microsoft, Google, etc.
• Most customers are not adopting a big-bang approach to Cloud
adoption. Existing On-premise data assets would continue leading to
an hybrid ecosystem of data assets deployed On-premise and in the
Cloud.
Challenges
Cloud adoption presents new challenges:
• Integration across On-premise and Cloud data stores - Data might
need to be replicated in order to present a combined view.
• Real Time data access - Data from both On-premise and Cloud data
assets may be required by consuming applications/BI reports in real
time. Traditional ETL processes would introduce latency.
• Disruption to Business - During Cloud migrations (re-architecture, lift
and shift, etc.) existing business processes could get impacted
• Security and Governance - Data Governance is still of paramount
importance and a hybrid ecosystem makes it more complex to manage.
Metadata, lineage, quality and security of the data needs to be ensured.
• Compliance - It may not be feasible to move data from existing Data
Centers to the Cloud and vice-versa due to compliance requirements,
more so for Financial Services companies.

How Data Virtualization can help with Cloud Adoption and Data Governance
14
Denodo is helping solve and mitigate some of the key challenges that enterprises are facing with Cloud adoption and associated Data Governance
challenges
• Hybrid Data Hubs - Denodo can help create a hybrid data hub by combining On-premise and Cloud data stores. Deploy Denodo instance closer to the major data
sources whether On-premise or on the Cloud. Both Cloud native use cases and hybrid use cases can be supported by this solution pattern.
• Avoid Data Replication - With virtualization, data does not need to be replicated. Caching and MPP fabric capabilities can address any performance bottlenecks.
Avoiding data replication also results in many other add on benefits with faster time to market, avoiding data copies, reducing storage costs, etc.
• Real Time data access - There is no movement of the data, unlike a tradition ETL processes, enabling real time access to data.
• Avoid Disruption to Business Process during Cloud adoption Journey - Denodo can abstract and hide the complexity and changes to the data sources while
changes to the data store and data pipelines happen making the process less disruptive.
• Security and Governance - Denodo can provide single point of access and control while managing fine grained data access. This ensures better governance and
auditability in the consumption layer across various consumption patterns. It also enables lesser complexity by managing security in a single access layer as opposed
to managing security at multiple layers. PII data can be encrypted as well.
• Compliance - Using Data virtualization can address many compliance needs since data movement across data center and cloud can be avoided.
• Information discovery and Self Service - Denodo provides self-service capabilities wherein data consumers are able to search metadata , explore data relationship
and lineage , create their own views using a UI based interface which can subsequently be shared and published.

Denodo can help realize better Data Governance & Security with Cloud Adoption
15
• Centralized Security & Governance
- Data can be secured in the Virtualization layer, which can act as single point for securing and monitoring data
access.
- Abstraction of Security model. Denodo hides the complexity of managing security in the underlying data
sources (e.g. relation sources, Hadoop data lake, Files, SaaS)
- Flexibility to integrate with LDAP or Active Directory.
- Can support very fine grained controls based on RBAC models at row and column level. Masking and
encryption can also be done to as per the requirement.
• Better Auditing
- History of Data access/queries are stored and can be used for any auditing.
• Data Lineage, Impact Analysis
- Denodo stores metadata of sources ,transformations etc. implemented as part of virtualization layer and
uses this to provide data governance capabilities like Data lineage and Impact analysis.
- Denodo metadata is exposed through APIs and can be integrated with external tools.
- Denodo can also integrate with Reference Data management tools such as Collibra and IBM IGC, to further
strengthen enterprise wide data governance initiatives.

Denodo Cloud Data Virtualization – Solution Overview
16
SOAP/REST
Web Service0
Azure SQL DW/
RedShift
Data LakeSQL Server /
Oracle/Netezza/Teradata
Security &
Governance
Self Service
Connect Combine Publish
Monitoring&
Auditing
On – Prem. Cloud
Security &
Governance
Self Service
Connect Combine Publish
Monitoring&
Auditing
Reports Data Consumers
Apps
• Create a data virtualization layer across On-
premise and Cloud data sources with a bridge
between both.
• Suitable for use cases where data virtualization
already plays a key role in On-premise setup.
• As new data sources emerge in cloud, Denodo
instance in the cloud can be used for data
virtualization for cloud sources.
• This setup simplifies the integration and data
access across cloud and On-premise data
sources. Both instances see each other as
another JDBC source and data is aggregated and
processed at the right place, reducing data
movement over the network.
• For cloud native use cases, where all or majority
of sources are present in cloud, Denodo instance
in cloud would be sufficient.

It is amongst the leading financial holding company, has provided financing, leasing
and advisory services to small and middle market businesses for over a century
Client
• As a SIFI, the bank is subject to additional scrutiny and faces tougher stress-
testing expectations from regulators
• Acquisition of another bank necessitated multiple data integration initiatives.
• Ability to aggregate and identify risk exposures quickly and accurately at the
enterprise-wide level, across business lines and legal entities
• Accuracy, Integrity, Completeness, Timeliness and Adaptability
• Old habits die hard. Culture is a difficult thing to change.
Problem
• Company has to deal with its legacy systems and data, along with those of
acquisition of the new bank.
• Different technologies are used to host contracts, positions and balances for
different businesses and regions
• Supporting firm-wide risk analysis, CCAR and other comprehensive regulatory
reporting across regions/products requires a single comprehensive view of the
contracts, positions and balances across all lines of businesses / regions for
various products
• Applications are built and deployed in isolation
• Challenges with data integration due to data is incorrect, missing, uses the wrong
format, incomplete, and so on
• Missing of master data and Governance rules
• Show improved schedule compliance , Predictable project budgets, Enhanced
data consistency, lower project and operational costs
Challenges
One Source of the Truth: Authoritative sources of data by domains, Access all data via
common provisioning point
Build for Reuse, Leverage Investment: Build once, use many – CCAR, Liquidity, Risk,
Avoid point-to-point integration, simplify
Minimize Data Replication: Eliminate data redundancy and proliferation, Eliminate
redundant data reconciliation efforts.
Enable Effective Data Governance: Enforcing policies, standards and procedures,
Defining authoritative source of data, Efficient data lineage and metadata management,
Monitoring data quality, Empowering data stewards
Simplified Architecture, Agile Data Integration: Faster time-to-market delivery,
Incremental information delivery via standard APIs, Simplified architecture, data makes
minimal hops
ArchitectureConsideration
• Created an virtual Canonical logical layer integrating the desperate source systems for
data needs from New Business into Customer
• Underlying changes should not affect end user’s view of the data
• Early identification of Gaps from source system and requirement perspective saving
cost and time
• Involving Business from initial phase to avoid last minute surprises
• Analyzing data quality upfront and certifying
• Quickly consolidation from various systems to see the requirement feasibility and
quality issues
• Changes to source systems can be easily sustainable
• Regulation and policy change rules can be applied quickly and viewed for refinement
of compliance parameters throughout the organization
Solution
Case Study – How a Leading Financial Company is using Data Virtualization to address regulatory
aspects
17

Case Study - Logical view of Solution Architecture
DW
(SQL Server ,Oracle )
Data Mart
(SQL Server ,Oracle)
Source Systems & External Data
P
H
Y
S
I
C
A
L
L
A
Y
E
R
F
O
R
M
A
T
L
A
Y
E
R
B
U
S
I
N
E
S
S
L
A
Y
E
R
Caching/ Reference Data( SQL Server)
Data Quality
Source Systems
(SQL Server ,Oracle)
Files)
Meta Data
External Feeds
(DnB, Chatham)
Virtual Marts Reference Data
Virtual Views
Pre-Processed
Views
A
B
S
T
R
A
C
T
L
A
Y
E
R
canonical Models
RealTimeAPI’s
(WebServices)
Actimize
ReportingLayer
(OBIEE,QLikView,
SSRS)
FormattedDataOutput
Data
Down
streams
18
Denodo Data Virtualization
Denodo Data Virtualization

M O D E R A T E D B Y
19
Clinton Cohagan
Chief Enterprise Data Architect, Lawrence
Livermore National Lab
Nageswar Cherukupalli
Vice President & Group Manager, Infosys
Saptarshi Sengupta
Principal Product Marketing Manager
Governing and Securing Data with Data Virtualization

21
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