Watch full webinar here: https://bit.ly/3omhmGw
A key aspect for achieving digital transformation is the data. Government organizations are well aware of the challenges that amount from increasing volume and velocity of data generation from multiple sources, such as enterprise applications, social media, integrated systems and sensory data. This webinar will discover how to utilize big data in the government sector and how to overcome data challenges to accelerate digital transformation.
Key points:
- Digital transformation in the government sector.
- The importance of data in digital transformation.
- The challenges of big data.
- The concept of data virtualization.
Exploiting Big Data to Accelerate Digital Transformation: Government Sector Applications (Middle East)
1. 1
Exploiting Big Data to Accelerate Digital Transformation: Government Sector Applications
2. Speakers
Dr. Hani Alturkostani
Digital Transformation and IT
Consultant, Government
Sector
Dr. Alexey Sidorov
Data Management Director
and Chief Evangelist, Denodo
Shaden Aloliwi
Data Virtualization Architect,
ExperTech
Lama Abunayyan
Customer Relations Specialist,
ExperTech
34. 34
Martin Fowler, Chief Scientist ThoughtWorks
We are gearing up for a shift to polyglot persistence - where
any decent sized enterprise will have a variety of different
data storage technologies for different kinds of data.
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While IT keeps trying to get all data
to a single repository, that data has
grown across systems, transcending
data warehouses, data lakes, cloud,
and, more recently, to the edge.
37. 37
Zhamak Dehghani, “Data Mesh Principles and Logical Architecture”
While this centralized model can work
for organizations that have a simpler
domain with smaller number of diverse
consumption cases, it fails for
enterprises with rich domains, a large
number of sources and a diverse set of
consumers.
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§ Operational Data
§ Heavy Structured
§ Schema on Write
§ Monolithic
§ ETL
§ Expensive Storage
§ Analytical Data
§ Less Structured
§ Schema on Read
§ Monolithic
§ ELT
§ Inexpensive Storage
§ Any Data
§ Any Structure
§ Schema on Demand
§ Distributed
§ Virtualization
§ No Storage
40. 40
Should Your Application Consider Data Mesh Connectivity? May 2020
… data mesh allows data sources to remain distributed
and controlled by different organizations, but accessible
to a centralized application”
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§ 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
§ It allows you to introduce new technologies, formats,
protocols, etc. without causing user disruption
§ Key Takeaways
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CONNECT, INTROSPECT & GOVERN ANY DATA SOURCE
WITH ZERO DATA REPLICATION
COMBINE & INTEGRATE INTO BUSINESS DATA VIEWS
CONSUME & SECURE DATA VIEWS IN MULTIPLE
FORMATS
What is Data Virtualization?
The core technology to enable modern data integration & data management
Sales
HR
Executive
Marketing Apps/API
Data Science
AI/ML
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Data Virtualization – A Data Fabric Layer
Consume
in business applications
Combine
related data into views
Connect
to disparate data sources
2
3
1
DATA CONSUMERS
DISPARATE DATA SOURCES
Enterprise Applications, Reporting, BI, Portals, ESB, Mobile, Web, Users
Databases & Warehouses, Cloud/Saas Applications, Big Data, NoSQL, Web, XML, Excel, PDF, Word...
Analytical Operational
Less Structured
More Structured
CONNECT COMBINE PUBLISH
Multiple Protocols,
Formats
Query, Search,
Browse
Request/Reply,
Event Driven
Secure
Delivery
SQL,
MDX
Web
Services
Big Data
APIs
Web Automation
and Indexing
CONNECT COMBINE CONSUME
Share, Deliver,
Publish, Govern,
Collaborate
Discover, Transform,
Prepare, Improve
Quality, Integrate
Normalized views of
disparate data
“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, Dec 16,
2015
51. 51
Data Virtualization: Unified Data Integration and Delivery
• Data Abstraction: decoupling
applications/data usage from data
sources
• Data Integration without replication
or relocation of physical data
• Easy Access to Any Data, high
performant and real-time/ right-
time
• Data Catalog for self-service data
services and easy discovery
• Unified metadata, security &
governance across all data assets
• Data Migration eliminating
disruption to Line of Business
applications and reducing risk and
cost
A logical data layer – a “data fabric” – that provides high-performant, real-time, and secure access to
integrated business views of disparate data across the enterprise
52. 52
Denodo Platform Architecture, How Does it Work?
How it works
Development
Lifecycle
Monitoring & Audit
Governance
Security
Development Tools
/ SDK
Scheduler
Cache
Optimiser
JDBC/ODBC/ADO.Net SOAP / REST WS
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
53. 53
Data Virtualization Connects the Users to the Data That They Need
1. Data virtualization allows you to connect to (almost) any data source
2. You can combine and transform that data into the format needed by the
consumer
3. The data can be exposed to the consumers in a format and interface that is
usable by them
• Typically consumers use the tools that they already use – they don’t have to learn new tools
and skills to access the data
4. All of this can be done without copying or moving the data
• The data stays in the original sources (databases, applications, files, etc.) and is retrieved, in
real-time, on demand
Cliffs Notes version (TL;DR)
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Customer 360 in Real Time
§ Higher customer loyalty and retention
due to reduction in service response
time from 6 minutes to 2 minutes, a
66% reduction.
§ Increased scalability of their data
infrastructure systems to support
business growth.
§ Greater revenue opportunities from
upselling and cross-selling.
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Enterprise Wide Data Access Layer
§ Intel accelerated the time of its
average services deployments from
180 hours to 8 hours.
§ Time-to-market (TTM) for data
delivery to business users has been
reduced by 90%
§ Post-merger data integration has
been greatly accelerated.