Watch full webinar here: https://bit.ly/3aIofv9 The best of breed big data fabrics should deliver actionable insights to the business users with minimal effort, provide end-to-end security to the entire enterprise data platform and provide real-time data integration, while delivering self-service data platform to business users. While big data initiatives have become necessary for any business to generate actionable insights, big data fabric has become a necessity for any successful big data initiative. The best of breed big data fabrics should deliver actionable insights to the business users with minimal effort, provide end-to-end security to the entire enterprise data platform and provide real-time data integration, while delivering self-service data platform to business users. Attend this session to learn how big data fabric enabled by data virtualization: - Provides lightning fast self-service data access to business users - Centralizes data security, governance and data privacy - Fulfills the promise of data lakes to provide actionable insights

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

2. Data Virtualization enabled Data Fabric:
Operationalize the data lake
Chris Day
Director, APAC Sales Engineering, Denodo
Sushant Kumar
Product Marketing Manager, Denodo

3. Agenda
1. The Reality of Data Lakes
2. Big Data Fabric
3. Customer Case Study – Logitech
4. Demo
5. Q&A
6. Next Steps

4. 4
A Modern Data Ecosystem
4

5. 5
The Promise of the Data Lake
The promise of a data lake is a
place that you can store data in its
raw form, unencumbered by
validation, mastering, or quality
processes, so as to allow
consumers to choose what data is
of value to them with a quick time
to market.

6. 6
…Data lakes lack semantic consistency and
governed metadata. Meeting the needs of
wider audiences require curated repositories
with governance, semantic consistency and
access controls.”

7. 7
Data Lake (System ofInsight)
Information Management andGovernance
Data Lakes and Big Data Fabric
1
Multiple repositories, organized based on
source and usage; hosted on appropriate
data platform for workload
1
Catalog of data, ownership, meaning and
permitted usage
2
No direct access to repositories3
Curation of all data to define meaning and
classifications
4
Business-led information governance and
management
5
Active monitoring and management of data6
Data-centric security7
Access to raw data to developnew
production analytics
8
Moderated, view-based self-service access to
data and analytics for Line of Business
9
2
3
4 5
6
7
8
9

8. 8
Forrester’s Big Data Fabric

9. 9
Data Virtualization
Big Data Fabric and Data Virtualization

10. 10
Data Virtualization Reference Architecture
Security
Governance and Metadata management
DataWarehouse
Unstructured Data
RDBMS
Excel
FlatFiles
XML
Email
Sensors (IIoT)
SocialMedia
Storage
RDBMS
Big Data Lakes
noSQL
IMDG
Structured Data Data Ingestion
Real Time/Streaming
Wearables
RFID
Compute
Batch
CDC
Data Access
Metadata
Enrichment
Search and Index
Data Virtualization
Data Services
Data Insight
DataMining
Dashboards
Data Discovery
andSelf-Service
Reporting
DATA
VIRTUALIZATION

11. 11
Data Virtualization Reference Architecture
Security
Governance and Metadata management
DataWarehouse
Unstructured Data
RDBMS
Excel
FlatFiles
XML
Email
Sensors (IIoT)
SocialMedia
Storage
RDBMS
Big Data Lakes
noSQL
IMDG
Structured Data Data Ingestion
Real Time/Streaming
Wearables
RFID
Compute
Batch
CDC
Data Access
Metadata
Enrichment
Search and Index
Data Virtualization
Data Services
Data Insight
DataMining
Dashboards
Data Discovery
andSelf-Service
Reporting
DATA
VIRTUALIZATION

12. 12
Customer Case Study – Logitech
• Logitech also needed more agile (and cost effective) analytics platform for
customer analytics
• Moved analytics to AWS – utilized many different storage and compute capabilities
on AWS
• Redshift, Snowflake for EDW, RDS for relational data, S3 for data ingestion
• Spark, EMR, NLP for analytics
• Used Data Virtualization as data access layer
• Users could access data irrespective of storage or processing capability
• Data Virtualization Layer provided data security/access permissions
• Abstracted security from different data sources
• Data governance also provided by Data Virtualization Layer
• Data lineage, metadata management, data catalog and discovery, etc.

13. 13
Customer Case Study – Logitech

14. 14
Customer Case Study – Logitech

15. 15
Customer Case Study – Logitech

16. 16
Summary
• Big Data initiatives can add deep analytical insight to help
organizations find new opportunities or provide more efficient
services
• When defining your Big Data initiative, think about how users will
find and access the data
• A Big Data Fabric is key to making this easy
• And making the initiative successful
• Data Virtualization is the right technology for an agile and flexible
Big Data Fabric

17. Product Demonstration
Director, APAC Sales Engineering, Denodo
Chris Day

18. Q&A

19. 19
Next Steps
Access Denodo Platform in the Cloud!
Take a Test Drive today!
https://bit.ly/2AouQLQ
GET STARTED TODAY

20. 20
Next Steps

21. Next session
How Data Virtualization adds value to your Data
Science stack
Sushant Kumar
Product Marketing Manager, Denodo
Chris Day
Director, APAC Sales Engineeing, Denodo

22. Thanks!
www.denodo.com info@denodo.com
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