Watch this Fast Data Strategy session with speakers: Maria Thonn, Enterprise BI Development Manager, T-Mobile & Jonathan Wisgerhof, Smart Data Architect, Kadenza: https://goo.gl/J1qiLj Your company, like most of your peers, is undoubtedly data-aware and data-driven. However, unless you embrace a modern architecture like data virtualization to deliver actionable insights from your enterprise data, the worth of your enterprise data will diminish to a fraction of its potential. Attend this session to learn how data virtualization: • Provides a common semantic layer for business intelligence (BI) and analytical applications • Enables a more agile, flexible logical data warehouse • Acts as a single virtual catalog for all enterprise data sources including data lakes

1. #FastDataStrategy

2. Next-gen Analytics:
Going Beyond Data Warehouse
Jonathan Wisgerhof
Smart Data Architect, Kadenza
Maria Thonn
Enterprise BI Development Manager, T-Mobile

3. T-Mobile Confidential
Big Data Analytics
in a Hybrid Environment

4. Slide / 4
Data Virtualization (DV)
T-Mobile Confidential
Speed
•Data is available to the Business faster than the traditional method, as data is
accessed in the original source without replication.
•Provides self-service BI capability by enabling users to find the data they need in a
secure way.
Agility
•Enables users to adjust models and create ad hoc views to meet the changing
needs of the Business without the need to physically rebuild the data.
•A common semantic model enables consistent results across all business units.
Lower Cost
•Addresses company financial needs to reduce IT spend, as % of business requires
1/6th the resources to implement versus traditional methods.
•Lower cost and increased time to value.
•Saves hardware cost associated with data replication and consolidation efforts.
Why Data Virtualization?
EDS Vision
“To transform data delivery into a single
point of data access through virtual data
layer that provide a consistent, connected
and secured data view, in agile on
demand self-service data delivery to serve
ever changing business needs.”

5. Slide / 5
What Problem Are We Solving?
T-Mobile Confidential
Many disparate data sources at T-Mobile can
require direct connection, replication, new
development, moving data through various
layers, apply rules, and etc.
Users are dependent upon Development for
new views, updates to queries, report creation,
and etc.
History resides only on the source server
making it difficult or impossible to retrieve and
combine for advanced analytics.
Data Virtualization with Denodo enables
connection to any data source (web, semi-
structured, and unstructured) in any format,
type, or disparate location.
Data Virtualization provides self-service to the
user community by enabling them to perform
day-to-day activities that they are currently
dependent upon others to fulfill.
Data Virtualization with Denodo enables ease of
retrieval by providing functionality to both
combine data from disparate sources and
translate from the disparate sources (VQL).

6. 6
▪ Provide a Common Data Layer
▪ Logical Data Lake saves on data storage
▪ Data integration cross data sources instead of just within single data source
▪ Unified or federated data cross data sources
▪ Provides metadata governance
▪ Unified security at common data layer
▪ Decouple your data apps and reporting data
▪ Near Real Time Virtual Data Marts
▪ Virtual Sandboxes and Prototyping
▪ Data Warehouse Offloading
▪ Self-service tool for Business analysis and reporting
▪ Reduce development time and cost
▪ Limit data redundancy and data storage requirement
T-Mobile Confidential
Benefits of Data Virtualization

7. 7
T-Mobile Confidential
EDS Architecture (Platform)
Datalake
Hadoop
Data Warehouse
Teradata
IngestionData Sources
SAP BW
TD
Legacy BI Data
Legacy ETL
Processes
SAP
Ericsson
Auxdb
EIP
Others
Visualization
Tableau
PowerBI
BO
Audit, Balance & Controls (ABC)
DMF
(Batch)
BEAM
Virtualization
Denodo
Collaboration
MyInsight
SOX/
non-SOX
Share Point
SAS
Others
AWS
Redshift
TM1
Informatic
a
Others
Others
Azure
Netezza
SOX
Non-SOX
SSRS
gateway

8. 8
▪ Big data use cases and data lake usage is on the rise at T-Mobile
▪ Our Hadoop implementation is both on-prem and in the cloud
▪ Advanced analytics with both on-prem and cloud data
▪ Performance cannot be compromised
▪ In-Memory Parallel Processing can make it happen
▪ Business users from various LOBs searching for enterprise data
▪ Data naming needs to be meaningful for business users
▪ Self-service information search, discovery and analytics is of utmost importance
▪ Denodo 7.0 Dynamic Data Catalog is the answer
T-Mobile Confidential
Denodo 7.0 and T-Mobile

10. Next-Gen Analytics:
Going Beyond Data Warehouse
Jonathan Wisgerhof
Smart Data Architect at Kadenza
Denodo Fast Data Strategy
Virtual Summit 2018

11. Next-Gen Analytics: Going Beyond Data Warehouse
• ​Common semantic layer for BI & analytics
• An agile, flexible Logical Data Warehouse
• Single Virtual Catalog for all enterprise data sources
Agenda

12. EXPLOSIVE
GROWTH OF
DATA

13. What we do
Consultancy
Management &
Maintenance Deployment
Roadmap
Proof of concept
Quick scan

14. Data Virtualization
Agile
Productivity
Pace
Insight
Time
Money
What does it bring?

15. Agile BI
Data virtualization use case areas
360° view
Big Data & Cloud integration
Data Services
Use case areas

16. ✓​Common semantic layer for BI & analytics
• An agile, flexible Logical Data Warehouse
• Single Virtual Catalog for all enterprise data sources​

17. Business need
Reliable and structured
Exploring data
1. Flexible handling of known and new
data, for example using other tools
2. Central control over definitions, logic,
authorization, source access
Play with data
Data usage differs

18. Central and managed
New data can be added quickly. Users have the
same version of the truth.
Freedom locally
Users have the freedom to use the available
data with their own tooling.
How? ... with data virtualisation!
Service BI and analytics with one platform

19. Data Virtualization architecture
• Connectivity
• Authorization
• Traceability
• Auditability
• Smart optimizer
• One semantic layer
• Central logic
Governed reports Freedom in usage Play with data

20. • ​Common semantic layer for BI & analytics
✓An agile, flexible Logical Data Warehouse
• Single Virtual Catalog for all enterprise data sources​

21. Logical Data Warehouse
Logical Integration
Historical Integration
Management
Datamart
ReportingCONNECT INTEGRATE PUBLISH
Analysis model
Services
Datasets
API’s
Connect
Logical information architecture
Deze basisarchitectuur van het Logical Data
Warehouse bevat een aantal standaard componenten
waarvan de meeste optioneel zijn. De vraag, situatie en
omgeving dicteert of een component relevant is en wordt
ingezet. Als een component niet nu, maar pas later
relevant wordt kan deze later alsnog worden opgenomen.
Een logische structuur betekent dat de objecten virtueel
worden uitgevoerd, tenzij er noodzaak is om ze fysiek te
maken. In een logische laag wordt een object dus fysiek
opgeslagen (in cache) bij een van de volgende situaties:
Een technische beperking (vanuit het LDW kan technisch
geen live-verbinding met de bron worden gemaakt); een
functionele beperking (Een live-verbinding is bijvoorbeeld
niet mogelijk omdat de bron niet altijd beschikbaar is);
performance (Het virtuele object is te traag).
In de LDW architectuur betekent dit dat de connectie-, de
logische integratie- en de datamartlaag door middel van
views worden geïmplementeerd en mogelijk onder
invloed van de genoemde factoren worden omgevormd
tot fysieke tabellen.
Logical Data Warehouse
Logical Integration
Historical Integration
Management
Datamart
ReportingCONNECT INTEGRATE PUBLISH
46
5
7
3
Analysis model
2
1
Services
Datasets
API’s
Connect

22. • ​Common semantic layer for BI & analytics
• An agile, flexible Logical Data Warehouse
✓Single Virtual Catalog for all enterprise data sources

23. Data Catalog
• Catalog of views and web services
- Browse and search for existing views and services
- See descriptions, relationships and data lineage
• Preview and find data
- Quick look at data
- Search based on content
• Consume
- Propose new standard business / canonical views
- Customize existing views for particular needs
Single virtual catalog for source and BI products

24. Create a data marketplace
Data Catalog combined with data services & products
Data as service
• Webservice API
• Freedom for data customer
• Integrate to data lakes
• Usage insight, regulated access
Data as product
• Tailored product
• Central logic
• Flexibility in output format
• Data to give away

25. Thanks!
Jonathan Wisgerhof
Smart Data Architect at Kadenza

26. M O D E R A T E D B Y
27
Maria Thonn
Enterprise BI Development Manager, T-Mobile
Jonathan Wisgerhof
Smart Data Architect, Kadenza
Saptarshi Sengupta
Principal Product Marketing Manager
Next-gen Analytics: Going Beyond Data Warehouse

27. Q&A

28. 29
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29. Thank you!
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