Denodo Partner Connect: A Review of the Top 5 Differentiated Use Cases for the Denodo Platform

Denodo Partner Connect:
Technical Webinar Series -
Top 5 Use Cases for Denodo

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Denodo Platform: ONE Logical Platform for All Your Data
Logically Integrate, Manage, and Deliver Distributed Data
ANY DATA
SOURCE
ANY DATA
CONSUMER
ANY PLATFORM ENVIRONMENT
On-Premises | Cloud | Multi-Cloud | Containerized
Data
Governance
Tools
BI Dashboard
Report and Tools
Data Science &
Machine Learning
Apps
Mobile &
Enterprise Apps
Microservices
Apps
DB, DW &
Data Lakes
Files
Cloud DB
& SaaS
Streaming
Data & IoT
Cube
Smart Query
Acceleration
AI/ML Recommendations
& Automation
Advanced Semantics
& Active Data
Catalog
Unified Security &
Governance
Logical Data
Abstraction
Real-Time Data
Integration

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Data Virtualization: The Foundation of a Logical Data Management Architecture
Agile Data
Integration
Logical Data Abstraction
Smart Query
Acceleration
Advanced
Semantics
Automation &
Recommendation
Unified Security & Governance
Data Catalog
AI/ML
Hybrid/Multi-Cloud
Data Integration
Self-Service BI
Data Governance
3600 View of
Entities
Data Fabric
Data Science
Enterprise
Data Services
Data Mesh
6 Key Capabilities of Logical Data Management Differentiated Use Cases
Data Virtualization capabilities offer an access and delivery layer that can serve as the foundation for the logical data fabric, which offers
significant automation functions in the data management space. These include automation of data recommendations, data quality, data
governance and policy, on top of the core integration functions of data virtualization.”
- Gartner Assessing the Relevance of Data Virtualization in Modern Data Architectures, June 2021

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What’s Denodo’s Recipe?
BENEFITS
Location, format, latency agnostic
▪ Ease of use
Consumers have a single location to
access any data.
▪ Centralized security and
governance
A single location for consistent
definition of policy.
▪ Agile data integration options
From full replication and
transformations, caching and real-time
federation options.
▪ Future-proof
Decoupling data from location and
schemas simplifies technology
evolution and infrastructure changes.
Embrace a distributed data landscape
Embrace the fact that data resides in multiple locations or
systems – on-prem, hybrid, multi-cloud. All data needs to
be managed with consistency
Use a logical approach to manage
it
Consumers access data through a centralized semantic
model, decoupled from data location and physical
schemas, that can enforce security and governance
requirements
4

5
Agile Data
Integration
Smart Query
Acceleration
Advanced
Semantics
Automation &
Recommendation
Data Catalog
AI/ML
Hybrid/Multi-Cloud
Data Integration
Self-Service BI
Data Governance
3600 View of
Entities
Data Fabric
Data Science
Enterprise
Data Services
Data Mesh

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• Complexity
• Duplication
• Increased costs
• Multiple security models
• Skill sets required
• Integration
Challenges of Hybrid/ Multi-Cloud Strategy
6

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Hybrid Cloud Architecture – Example with AWS
DATA FLOW
∙ Data from cloud and on-prem sources is loaded into
AWS relational and Hadoop-based stores for analytical
and operational processing.
∙ On premise data from applications, databases, files,
and other sources is virtualized by Denodo on-prem
engine providing unified and secure access point.
∙ Structured, semi-structured, and unstructured data
residing in AWS stores is combined with the data
coming from the cloud applications and on-prem data
sources delivering the real-time gateway for the end-
user consumption. Required virtual data marts are built
inside Denodo Platform for AWS.
∙ Data is consumed by Amazon QuickSight or any other
BI or analytical tools.
2
1
3
4

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DATA FLOW
• Connect your cloud data sources from Azure,
Amazon, and Google Cloud to Denodo Data
Virtualization platform.
• Connect to Snowflake cloud platform and onboard
relational data sets. Combine all types of your data
sitting in the other clouds with Snowflake Data
Warehouse using Denodo Data Virtualization,
creating one centralized data hub.
• Build Smart Summaries for the heavy analytical
queries and store them in Snowflake MPP platform
to achieve the best performance.
• Run real-time queries on virtual data from other
applications.
• Build operational reports and analytical dashboards
on top of Denodo Data Virtualization platform to
derive insights from the data, and use any BI tool to
serve thousands of end users.
1
2
4
5
3
Multi-Cloud Logical Architecture – Snowflake

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Multi-Cloud Integration with Logical Data Fabric

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About BHP
Company Profile and Background
• Anglo-Australian multinational mining, metals and petroleum dual-listed public company headquartered in Melbourne, Victoria,
Australia.
• BHP ranked as the world's largest mining company, based on market capitalization, and as Melbourne's third-largest company by
revenue,
• BHP has mining operations in Australia, North America, and South America, and petroleum operations in the U.S., Australia,
Trinidad and Tobago, UK, and Algeria.
• The company has four primary operational units
• Coal
• Copper
• Iron ore
• Petroleum
• No of Employee : 72,000
• Revenue : US$44.288 billion (2019)

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BHP – Globally Distributed Data and Users
Houston DC
Santiago DC
Perth DC Brisbane DC
AWS US
East
Escondida
Jansen London
Singapore
Kuala Lumpor
Shanghai
AWS
APAC

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BHP – Global Data Fabric
Houston DC
Santiago DC
Perth DC Brisbane DC
AWS US
East
AWS
APAC
Escondida
Jansen London
Singapore
Kuala Lumpor
Shanghai
Every Data Virtualization cluster is connected to local
data sources and is the access point for local consumer
apps such as BI and analytics tools. Each Data
Virtualization cluster has visibility of the datasets
available from all other clusters, and requests this data
from its peer cluster as required by end users

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BHP – Global and Logical Data Fabric
Every Data Virtualization cluster is connected to local
data sources, and is the access point for local
consumer apps such as BI and analytics tools. Each
Data Virtualization cluster has visibility of the datasets
available from all other clusters, and requests this data
from it's peer cluster as required by end users

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Agile Data
Integration
Smart Query
Acceleration
Advanced
Semantics
Automation &
Recommendation
Data Catalog
AI/ML
Hybrid/Multi-Cloud
Data Integration
Self-Service BI
Data Governance
3600 View of
Entities
Data Fabric
Data Science
Enterprise
Data Services
Data Mesh

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Data landscape is complex
• Data in multiple silos
• Every business data need is a
data integration project
• Inconsistencies
Avg. of 400 sources used for analytics
in big organizations
Source: IDG, 2021
DATA
SOURCE
DATA
CONSUMER
Data
Governance
Tools
DB, DW &
Data Lakes
Files
BI Dashboard
Report and Tools
Data Science &
Machine Learning
Apps
Mobile &
Enterprise Apps
Microservices
Apps
Cloud DB
& SaaS
Streaming
Data & IoT
Cube

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How Denodo’s Logical Data Management Layer Works
DISPARATE DATA SOURCES
DATA CONSUMERS
DATA CONSUMERS
Analytical Operational
Base/Raw views
Data Asset Inventory
Create Views with a
Common Schema
across all Sources
1
Standardized
views
Center of Excellence
Creates Ent. wide
Views used by all
Business Units
2
Customer Product Order
Each Layer of Views
provides more refined Single
Views of Truth
Business views
IT Semantic Layer
Business Units
Create Bus. Views
unique to their needs
3 Finance Distribution Sales

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Denodo Platform Enables Data Fabric Architecture
Business
Analyst Developer
Data
Scientist
DATA USERS
BI Tools DaaS
Data
Science
Tools
DATA APPS
UNIFIED DATA FABRIC
KEY BENEFITS:
✓ One DM Platform for all different data sources
✓ Open to plug new data sources easily
✓ Data replication minimized
✓ Data sharing/ Data preparation optimized
✓ Simplifying data provisioning & data changes
✓ Consistent business semantic across the enterprise
✓ Cost control over data storage & data computing
✓ Enabling new data consumption paradigms
✓ Full control and visibility over data consumption
✓ Easy to adapt to decentralized or multinational
organizations

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Use Case Overview
• The Challenge:
• A global automobile manufacturer with operations in over 170 countries has massive amounts of data
distributed in various sources and geographical regions around the world.
• Data stored in distinct geographical and network regions with no shared access among regions
• Providing secure access and sharing data amongst numerous key stakeholders
• The Solution
• Build a Logical Data Fabric for seamlessly integrating and providing secure access to global datasets,
powered by Denodo
• Primary Data Sources:
• AWS: Redshift, S3, Athena, Databricks
• Google Cloud: BigQuery, Snowflake
• Azure: Active Directory
• Consumption Tools
• Tableau, PowerBI, Alteryx

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Toyota Global Data Fabric Overview

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Agile Data
Integration
Smart Query
Acceleration
Advanced
Semantics
Automation &
Recommendation
Data Catalog
AI/ML
Hybrid/Multi-Cloud
Data Integration
Self-Service BI
Data Governance
3600 View of
Entities
Data Fabric
Data Science
Enterprise
Data Services
Data Mesh

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Common Challenges of Data Governance Initiatives
 Too many reports
 Duplicate reports
 Conflicting data
 Data Trust Issues
 Data extract hell
 Security nightmare NO STANDARDS OR GOVERNANCE
A TOWER OF BABEL

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Value Proposition
A Logical First Approach for Data Governance
Single Entry Point to
Enforce Security and
Governance Policies
Data on-premises and
off, combined through
the same governed
virtual layer
Single Source of
Truth / Canonical
Views
Who is Doing /
Accessing What,
When and How
Fewer copies of
personal data.
Lineage of copies is
available.

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Security: Tag and Attribute based policies in Denodo
▪ Denodo’s tags, available for tables and columns
▪ Definition of semantic, tag-based security policies
▪ Completely abstracted from specific tables
▪ Easier to manage and less error prone
▪ E.g mask the #SSN with *** for HR and Finance
▪ Allows for implementation of semantic security
rules across the data landscape, independent of
technologies underneath

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Governed Discoverability of Data
Only allowing
access to data you
have access to
Governed search of
Data available for
consumption

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Understand where data is coming from
Detailed and easy to read
information on data lineage
and transformations

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What data is consumed and by who?

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Case Study : Unified view into regulatory risk
Business Need
• Need a controlled data environment to support tougher
regulatory requirements.
• Information does not tie across all data silos.
• Need a smart data governance initiative to avoid garbage-
in-garbage-out problem.
Benefits
• Enables faster time-to-market and incremental information
delivery.
• Helps CIT realize value from data - successfully access all
data through provisioning point instead of through legacy
point-to-point integration.
• Minimize data replication and proliferation by eliminating
data redundancy.
Solution

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Agile Data
Integration
Smart Query
Acceleration
Advanced
Semantics
Automation &
Recommendation
Data Catalog
AI/ML
Hybrid/Multi-Cloud
Data Integration
Self-Service BI
Data Governance
3600 View of
Entities
Data Fabric
Data Science
Enterprise
Data Services
Data Mesh

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Challenges of Self-Service Initiatives
▪ Too many reports
▪ Duplicate reports
▪ Conflicting data
▪ Users don’t trust reports
▪ Data extract hell
NO STANDARDS OR GOVERNANCE
A TOWER OF BABEL

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Building a Data Marketplace
Cloud Storage
(S3, ADLS, GCS)
SaaS Applications
Big Data Application
Relational
Databases
JSON/XML Files
Flat Files
Data Warehouse
Excel Spreadsheets
Graph Database
Data Virtualization
Data Catalog

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Questions Answered by Data Marketplace
Can I find the data I need?
Is the data trusted?
Is the data sensitive?
Does it have business value?
What do others think about the data?
Is there any related data I should know about?
How frequently is it used?
Who owns the data?
Search, faceted search, browse
Lineage, trust rating or tag
Controlled access to sensitive fields
Reviews or ratings
User comments and feedback
Data asset relationships
Usage metrics
Data owner information
………………………………….
.……………………………………………
…………………………………………..
……………………………….
…………………
……
…………………………………..
……………………………………………

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Leading Global Bank – Pain Points
Supporting
Multiple Data
Access Tools
Changing
Technologies
Data Lifecycle
Management
Data
Discovery

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Leading Global Bank – Data Marketplace
Denodo Platform
Data Marketplace
Client & Account
Active Clients Client
Accounts
Party Summary
Positions & Holdings Securities & Pricing Market Data Hub Index & Benchmark
Systems of Record Data Lake Data Warehouse
with Business Semantic Layer
Virtual Data Lake

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Leading Global Bank – Data Marketplace
DENODO DATA CATALOG
→ Categorization
→ Tagging
→ Models
→ Descriptions
→ Data Lineage
→ Metadata Search
→ Query Builder

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Agile Data
Integration
Smart Query
Acceleration
Advanced
Semantics
Automation &
Recommendation
Data Catalog
AI/ML
Hybrid/Multi-Cloud
Data Integration
Self-Service BI
Data Governance
3600 View of
Entities
Data Fabric
Data Science
Enterprise
Data Services
Data Mesh

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• A s3600 view, also known as propensity modeling is an aggregated, consistent and holistic representation of
the data held by an organization about the customers / citizens / patients / vehicles etc. that can be viewed in one
place, such as a single page. The advantage to an organization of attaining this unified view comes from the ability it
gives to analyze past behavior in order to better target and personalize future interactions
• Where representations of a customer are held in more than one data set, achieving a single 360 view can be difficult
because identity must be traceable between the records held in those systems
Customer / Patient / Citizen 360
3600 View of …

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Single 360 View - AWS
DATA FLOW
 On premise data from applications, databases, files,
and other sources is virtualized by Denodo on-
premise engine providing unified and secure access
point.
 Structured, semi-structured, and unstructured data
residing in AWS stores (2) is combined with the data
coming from the cloud applications (3) on-prem data
sources (4) and real-time interactional information
from IVR systems, web-logs, social networks, and
video feeds (5).
 Virtual 360 data views are exposed via API interface
for the real-time dashboarding and alerting.
 Also, virtual data views are built and provided for the
historical reporting and analytics.
2
1
3
4
5
6
7

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Reference Architecture
Customer / Patient / Citizen 360
Business Views
Matching and Merging
Base Views
Security
Data
Governance
Query
Optimization
Real Time 360 View
Dashboards
Reporting and
Analytics
DATA SOURCES
DATA CONSUMERS
ODBC, JDBC
Rest, OData, GraphQL
DATA VIRTUALIZATION
Abstraction Layer
Unified Virtual Views
DISPARATE DATA
Any Format
At Any Speed

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Customer 360 in Real Time
• Started with 1,900 call center agents
• Currently ~3,000 agents
• Integrate 37 backend systems to
deliver customer data to agent
desktop
• System grows as # agents increases
• Currently 32 cores in Production
• 4M+ transactions per day, 1,800
concurrent users

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• Jazztel uses Data Virtualization to deliver customer data from 37 operational and analytical
systems to a call center agent desktop
▪ Agent has all customer data available when talking to customer
▪ Goal to solve customer problem or answer questions in a single call without transfers or call-back
• Agents spend less time finding customer data as all available through single application
▪ No ‘swivel chair’ integration using multiple different apps
• Time per customer – Average Handle Time (AHT) – reduced by 10%
▪ With 3,000 call center agents – represented a saving of 300 FTE = €10M per year
Note: First Call Resolution Rate (FCR) increased to over 90% and reduced ‘back office’ tasks by 50%.
Customer satisfaction increase to over 95%.
Jazztel – Single View of Customer Benefits

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Positive Business Outcomes
SMART DATA DISCOVERY
Data Catalog enables everyone to find and use business-friendly
views of the data they need to make more informed decisions.
FASTER TIME-TO-REVENUE
Self-service data frees the data teams to focus on the provisioning
and managing of the organization’s data, not satisfying ad-hoc
requests.
IMPROVED SECURITY AND DATA GOVERNANCE
Decouple data security policy from the underlying data
repositories and enforce access controls from the centralized data
access layer.
REDUCED OPERATION COSTS
Simplifying data access and controls improves agility and lowers
operation costs.
Sicredi is building a unified view
of enterprise data spread
across 116 credit unions. The
Denodo Platform’s logical data
layer has enabled faster data
delivery, self-service analytics,
data governance, and is one of
the key enabler for data
democratization at Sicredi”
- Senior Data Architect - Sicredi
Using the Denodo Platform,
GetSmarter achieved major
efficiency gains by getting its
data warehouse ready for
production in about 2 weeks,
which otherwise would have
taken about 6 months.
- GetSmarter

Thanks!
www.denodo.com info@denodo.com
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Denodo Partner Connect: A Review of the Top 5 Differentiated Use Cases for the Denodo Platform

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Denodo Partner Connect: A Review of the Top 5 Differentiated Use Cases for the Denodo Platform