Accelerate Migration to the Cloud using Data Virtualization (APAC)

DATA VIRTUALIZATION
APAC WEBINAR SERIES
Sessions Covering Key Data
Integration Challenges Solved
with Data Virtualization

Accelerate Cloud Migration with Data Virtualization
Chris Day
Director, APAC Sales Engineering, Denodo
Sushant Kumar
Product Marketing Manager, Denodo

Agenda
1. Challenges with Cloud Migration
2. Migration and Cloud Architecture
3. Cloud Use Cases, Migration and Case Studies
4. Product Demo
5. Q&A
6. Next Steps

4
Migrating workloads to Cloud ?
It’s Not “If” or “When”, rather How best to Optimize the journey !

5
Understanding Cloud Migration
1. Move data or applications or both
from on- premises to Public cloud
2. Move partial workloads to the
cloud, with some still on premises
– Hybrid Cloud
3. Migration of on-prem apps /data to
multiple public clouds (use case
driven) - Multi-Cloud
4. Build an on-premises cloud
computing platform – Private
Cloud

6
Where’s my Data ? Cloud Native, SaaS applications & More
Type Of
Data
Sample Being used for
Machine
generate
d data
• Clickstream web server logs
• IVR logs, App Server logs
• DBMS logs
• On-line behaviour analysis
• Cyber security
• Consumer IoT (Sensor data)
• Industrial IoT (Sensor data)
• Location, temperature,
movement, vibration,
pressure
• Product usage behaviour
• Product or equipment performance
Human
generate
d data
• Social network data
• Inbound email
• Competitor news feeds
• Documents
• Voice interaction data
• Unstructured text , sentiment analysis
Traditiona
l
structure
d data
• Master data
• Transaction data
• Customer, product, employee,
supplier, site,…..
• Orders, shipments, returns, payments,
adjustments..
External data • Open government data
• Weather data
• Structured data
• Semi-structured data e.g., JSON, XML, AVRO,
• Sales impact, distribution impact

7
Manage / Integrate Data Across One Or More Clouds And On-
Premises Systems
ERPOpsCRM
data
integration
DW
data
integration
DW
On-Premise
OLTP systems
Cloud
OLTP systems

8
But there are Challenges with Cloud Adoption/Migration
• Silos remain (Cost and Interoperability).
Lack of strategy.
• Security concerns in the Cloud (GDPR …)
• Performance bottlenecks (data
across regions, infrastructure)
• Business downtime (complexity of
migration, apps/data sources)
• Learning new skills and resources.

9
Migration Patterns to the Cloud – 5 R’s
Cloud Migration Pattern

10
IT: Flexible Source Architecture
Business:
Flexible Tool
Choice
IT can now
move at
slower
speedw/o
affecting
business
Business can
now make
faster & more
sophisticated
decisions as
all data
accessible by
any tool of
choice
Data Driven Agile Reference Architecture using Data
Virtualization (Connect + Combine + Consume)

11
Key Use Cases in the Cloud using Data Virtualization
ONE Analytics & BI in the Cloud
TWO
Modernizing Applications – Extending Data
Science
THREE
Hybrid Data Fabric & Data Lakes in the
Cloud
FOUR
Data Marketplace/ Archive & Compliance
Reporting
Multi-Cloud & SaaS Data Integration

12
• Legacy Data Integration a.k.a. ETL
• Cloud Data Integration
• Data Virtualization
• Modernizing Data Warehouse in the
Cloud
• SaaS Application integration in the
Cloud
• Accelerate Data Science – using
governed and right data sets
Solutions to alleviate Cloud Migration/Data Sprawl

13
Amazon RDS,
Aurora
US East
Availability
Zone
EMEA
Availability
Zone
On-prem
data center
Multi-Location Architecture – The role it plays!

14
Another scenario where the Hybrid
Data Hub is useful is during
migration periods
• Databases and applications can
be gradually migrated to the
cloud
• The DV layer absorbs the
changes
• Migration is transparent for end
users
Active
Directory
Data CenterCloud
Hybrid Data Fabric – Migration to Cloud

15
Architecture:
• Location of data sources
• Scaling – Auto / Clustering
• Load balancing / High
Availability
Sizing:
• Data volume (size)
• Concurrency (queries)
• Infrastructure choices
• Cloud Burst workloads
Performance:
• Query pushdown
• Caching
• Networking (VPC)
Data Sources:
• SaaS applications (SFDC,
ServiceNow)
• Special connectors for AWS
Redshift, Snowflake, Spark
SQL, Azure SQL DW)
• REST and Odata connectors
Best Practices – Data Virtualization in the Cloud

17
Data Virtualization Streamlines the Data Infrastructure at AXA XL
THE CHALLENGE:
The data management architecture of AXA XL is extremely complex, with
multiple operational source systems.
BUSINESS NEEDS:
• Multiple stakeholders from different business groups used their own BI tools to
access data. This, in turn, lead to latencies in data delivery as well as
inconsistencies between different data sets, creating multiple versions of the
truth.
• There was also a lack of data access control, with no way to trace who accessed
what data, or when.

18
Data Virtualization in the Cloud

19
• The Denodo Platform acts as the single point of entry to all the different systems, eliminating the need for each
consuming application to connect to the sources individually, and in the process making the data architecture
nimble.
• Business teams are now able to publish more reports, from more sources, more often, without having to move any
data.
• The Denodo Platform’s data governance and access control framework enables the IT team to implement role-
based data access, which makes it easy for AXA XL to comply with local and international data privacy laws such as
GDPR.
• The consolidated view of data helps improve the representation of business terminology. This allows for one single
definition for all of AXA XL’s metrics, providing a higher level of consistency in data and also ensures a single
version of truth across data sets.
• Data virtualization enabled the AXA XL data management team to perform multiple proofs-of-concepts in the
cloud, to test new products and services, much more quickly than other data integration techniques.
Data Virtualization Benefits for Axa

20
Paco Hernandez, CoE Lead, Semantic and Data Modeling, AXA XL
Denodo in the cloud brings us the
flexibility to do a lot more with a lot less

Product Demonstration
Chris Day
Director, APAC Sales Engineering, Denodo

22
The Scenario – Moving Data to the Cloud
Modernizing the Data Warehouse in the Cloud
Tooffload the warehouse we store historical
sales data in a Hadoop cluster
Denodo providing data virtualization,
governance and security
Now you need to update all your reports, dashboards, applications etc.
On Premise Cloud

23
Example of the Change Needed
What’s the impact of a
marketing campaign over
time?
 Historical sales data offloaded to
Hadoop cluster for cheaper
storage
 Current sales data – Oracle ->
Redshift
 Store data – Oracle -> Aurora
Sources
Combine,
Transform
&
Integrate
Consume
Base View
Source
Abstraction
join
group by state
union
Hist. Sales
211M
Current Sales
68M
Store

25
Key Takeaways
• Migrating workloads to cloud can be easy if you plan ahead
andpick the right tools.
• Cloud has become an integral part of the data journey.
Spend more time on deriving value with data
virtualization, rather then re- arranging or replicating data.
• Denodo Platform can alleviate complex, heterogenous,
multi-cloud landscape to solve you data integration needs
and provide insightsin real-time

VIRTUAL
November 24-25, 2020 | 9:00am SGT | 12:00pm AEDT
The Agile Data Management and Analytics
Conference
Advancing Cloud, Analytics & Data Science with Logical Data Fabric
REGISTER NOW
https://denodo.link/2Id87c8

Thanks!
www.denodo.com info@denodo.com
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Unless otherwise specified, no part of this PDF file may be reproduced or utilized in any for or by any means, electronic or mechanical, including photocopying and microfilm,
without prior the written authorization from Denodo Technologies.

Accelerate Migration to the Cloud using Data Virtualization (APAC)

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