Watch full webinar here: https://bit.ly/3lSwLyU
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es un componente clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de la información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos forma parte de las herramientas estratégica para implementar y optimizar el gobierno de datos. Esta tecnología permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
Le invitamos a participar en este webinar para aprender:
- Cómo acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Cómo activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
4. Agenda
1. Current Challenges in Data Management
2. Data Virtualization and the Logical Data Warehouse
3. Data Virtualization: What Analysts Say
4. Case Studies
5. Q&A
5. Current Challenges in Data Management
1. Faster & more complex demands for decision making
▪ Provide useful information for decision making at all organization levels
▪ New users with advanced analytical skills and needs: e.g. data scientists
▪ Solution? Self Service Initiatives lead by business users, etc. → Either too complex (direct
access) or too costly (specific data marts) , Governance and consistency problems
2. Regulations, enterprise-wide governance & data security
▪ Tens of new regulations worldwide: tax, finance, privacy, HR, environmental, etc.
▪ Ensure consistency in semantics of delivered data and data quality
▪ Enforce security policies
▪ Solution? Data Governance tools. Separate, static system for documentation→ get out of sync
easily, don’t enforce policies & don’t deliver data to users
3. Complexity of DM infrastructure: IT cost reduction
▪ Huge data growth, operation costs → IT is looking for cheaper and more flexible solutions
▪ Solution? Cloud, Data Lakes → Increase integration complexity in the short term. E.g. Gartner
says “83% of Data Lakes projects have failed”
6. 6
What is the Problem ?
Lack of Agility:
• No unified infrastructure (multiple data sources and
analysis / visualization tools)
• Integrating, transforming and combining data is slow with
traditional methods
Agility vs Governance:
• Inconsistent reports / Single Source of Truth
• Compliance with company glossaries and policies
• How to enforce consistent security, data quality and
governance policies across multiple systems
• Too much replicated data
7. 7
Do Data Governance Tools Solve the Problem ?
DG Tools allow:
• Informing about data assets and their level of quality
• Defining unified glossaries and terminology
• Defining data quality and data governance policies, and
managing/tracking changes
Disconnected from the data delivery process
• Do not ensure delivered data conforms to glossaries
• Do not enforce security, data quality and governance
policies in the data delivery process
• The problem of how to enforce these policies across multiple
data sources and consumption tools remain
9. 9
Denodo proprietary and confidential. DO NOT DISTRIBUTE
Gartner: Unified Data Integration, Delivery and Governance
Denodo
10. 10
Denodo’s Logical Data Fabric Links: Business Interface to Data
1. Single Access Point to all Data
at any location
2. Semantic Layer – Expose Data
in Business-Friendly form,
adapted to the needs of each
consumer
3. Up to 80% reduction in
integration costs, in terms of
resources and technology data
4. Consume data with any tool
and access technology (SQL,
REST, GraphQL, OData,…)
5. Single entry point to apply
security and governance
policies
6. Abstraction: change vendor /
location / processing engine
without affecting data
consumers
11. 11
Data Virtualization: Logical Data Delivery for the Business
Development
Lifecycle
Monitoring & Audit
Governance
Security
Development Tools
/ SDK
Scheduler
Cache
Optimiser
JDBC/ODBC/ADO.Net REST / GraphQL / OData
U
LoB
View
Mart
View
J
Application
Layer
Business
Layer
Unified View Unified View
Unified View
Unified View
A
J
J
Derived View Derived View
J
J
S
Transformation
& Cleansing
Data
Source
Layer
Base
View
Base
View
Base
View
Base
View
Base
View
Base
View
Base
View
Abstraction
12. 12
Data Virtualization for Data Governance
Single Entry Point
for Enforcing
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.
13. Query Execution and Performance
Source
Abstraction
Virtual
Modelling
Business
Delivery
Query Optimizer
Security & Governance
Query Engine
Delegate processing to data sources
▪ Transparently switch workloads according to cost or performance
Most advanced execution engine for distributed scenarios
▪ Unique techniques automatically rewrite user queries to maximize
pushdown
▪ Leverage MPP capabilities to deal with large data volumes
Advancing Caching / Acceleration Mechanisms
▪ Selectively materialize subsets of the data for protecting data
sources and query acceleration
▪ Machine Learning for automatically proposing selective data
materializations for query acceleration
14. 14
Source: Gartner 2018 Data Virtualization Market Guide
In 2020, organizations utilizing data virtualization will spend 45% less
on building and managing data integration processes.”
Through 2022, 60% of enterprises will implement some form of data
virtualization as one enterprise production option for data integration.
Source: Gartner 2018 Data Virtualization Market Guide
16. 16
Gartner Gives DV its Highest Maturity Rating
“Data Virtualization
can be deployed
with low risk and
effort to achieve
maximum value.”
17. 17
Source: Gartner Magic Quadrant for Data Integration, August 2018
Denodo continues to expand its leadership and mind share in data
virtualization, reaching almost 95% of Gartner client inquiries on the subject.”
Denodo grew at an impressive rate in 2018 and 2019... its leadership in
the Data Virtualization market is enabling its growth
Source: Gartner Market Share Analysis: Data Integration Worldwide, 2018 (published August 2019)
and 2019 (published April 2020)
18. 18
Customer Satisfaction
Why Customers Choose Denodo
▪ Gartner Peer Insights Customer’s Choice
Award (January 2021)
▪ Both in 2019 and 2020, the only vendor
where 100% of reviewers would
recommend Denodo
▪ 125+ verified reviews with overall score of
4.7 out of 5
19. 19
Spectrum Health (Michigan)
Regional Healthcare System (Hospitals,
Physicians and Plans)
• 170 service sites, including hospitals, urgent care
centers, primary care physician offices, community
clinics, rehabilitation, outpatient facilities and elderly
care.
• Revenue $6.9 billion with 39,000 employees and
volunteers
• Health plan with 1 million members
Primary Challenges
• Integrating multiple analytical data sources quickly
• Reconciling provider data from multiple sources
accurately (business impact)
20. 20
Spectrum Health 1st Project – COVID-19 Dashboard
COMPONENTS:
Tableau, Denodo, Oracle and SQL Server,
10+ other sources
TEAM:
1 Tableau developer, 2 Denodo
developers, 1 Denodo admin
DEVELOPMENT TIME:
• 2 days - Prototype
• 2 weeks – Production*server available
CHALLENGES:
• Very short timeframe
• No formal Data Fabric training
• Understanding performance
optimization (queries from hours to
less than a minute)
“Overall, I felt the team did an amazing job
and the platform did help us deliver value
much quicker than we would have been able
to going the traditional ETL route. It would
have take us at least 6 weeks.”
- Senior Information Architect
24. About BHP
We are a leading global resources company
▪ Our purpose is to bring people and resources together
to build a better world.
▪ Our strategy is to have the best capabilities, best
commodities and best assets, to create long-term value
and high returns.
▪ At BHP, we have a unique perspective on the
extraordinary potential of natural resources to provide
the essential building blocks of progress.
▪ We are among the world’s top producers of major
commodities, including iron ore, metallurgical coal and
copper. We also have substantial interests in oil and gas.
▪ We have a global presence with operations and offices
across Australia, Asia, the UK, Canada, the USA and
Central and South America.
24
Data Virtualization Platform - September 2020
25. Multi-Location Hybrid Data Fabric
25
Problems:
• Repeated engineering effort
• Long lead-times
• Project-centric repositories: duplicate
data everywhere
Brisbane
Perth
Santiago
Houston
Cloud
Tenancy
Data
Lake
Data
Mart
Data
Mart
Analytics
Analytics
Analytics
Data Virtualization Platform - September 2020
26. Reference architecture
26
Data Source
✓ Application data stores
✓ SaaS / Cloud Applications
✓ Application interfaces
✓ Manual data sources
Data Fabric Consumers
✓ Enterprise &
Regional Data
Stores
Self Service Data Catalogue
Query
Optimisation
Query
Development
Data
Federation
Data
Discovery
Abstraction / Semantic Layer
Security Layer
Kerberos Delegation + Encryption in Transit + Extensive Auditing
Secure
Faster
Connect to data stores or direct to source Get access to the right data, fast.
Self service
Flexible protocols
✓ Analytics
✓ Self Service
✓ Business Intelligence
✓ Transactional Applications
✓ Bring your own tool
BHP Data Fabric - September 2020
27. Multi-Location Hybrid Data Fabric
27
Problems:
• Repeated engineering effort
• Long lead-times
• Project-centric repositories: duplicate
data everywhere
Brisbane
Perth
Santiago
Houston
Cloud
Tenancy
Data
Lake
Data
Mart
Data
Mart
Analytics
Analytics
Analytics
Data Virtualization Platform - September 2020
28. Enabling Agile Analytics
and Data Governance with
Data Virtualization
Demostración de producto
Juan González
Líder Técnico
March 2021
29. Revisión del modelo conceptual
29
Data Virtualization Platform - September 2020
30. Revisión del modelo conceptual
30
Data Virtualization Platform - September 2020