To watch full webinar, follow this link: https://goo.gl/3s9hRG The tide is changing for analytics architectures. Traditional approaches, from the data warehouse to the data lake, implicitly assume that all relevant data can be stored in a single, centralized repository. But this approach is slow and expensive, and sometimes not even feasible, because some data sources are too big to be replicated, and data is often too distributed such as those found in cloud data sources to make a “full centralization” strategy successful. Attend this webinar to learn: • Why Logical architectures are the best option when integrating Big Data. • How Denodo’s parallel in-memory capabilities with dynamic query optimization redefine analytics architectures. • How IT can meet business demands for data much faster with Data Virtualization. Agenda: • Challenges with traditional approaches for analytics architectures. • Overview of Denodo's parallel in-memory capabilities. • Product Demo of parallel in-memory capabilities accelerating analytics performance. • Q&A. To watch all webinars in Denodo's Packed Lunch Webinar Series, follow this link: https://goo.gl/4xL9wM

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

2. Parallel In-Memory Processing and
Data Virtualization Redefine Analytics
Architectures
Alberto Pan
CTO, Denodo

3. 3
Logical Architectures for Analytics

4. 4
Logical Architectures: Data Virtualization

5. The Role of Denodo: Data Combination
Connect to any type of data source
Combine data from several systems
Publish it to the desired format
…zero-code development of complex data combinations and transformations
The Denodo Platform enables us to build and
deliver data services, to our internal and
external consumers, within a day instead of
the 1 – 2 weeks it would take with ETL.”
– Manager, DrillingInfo

6. The Role of Denodo: Abstraction
Abstracts access to disparate data
sources.
Acts as a single virtual repository.
Abstracts data complexities like
location, format, protocols
…hides data complexity for ease of data access by business
Enterprise architects must revise their data
architecture to meet the demand for fast data.”
– Create a Road Map For A Real-time, Agile, Self-
Service Data Platform, Forrester Research

7. The Role of Denodo: Logical Views for Self-Service
Enables creation of semantic models reflecting
business taxonomy
Create multiple logical views over the same
physical data, adapted to each type of user / Line
of Business
…enables information discovery and self-service
Impressively quick turn around time to "unlock“
data from additional siloes and from legacy
systems - Few vendors (if any) can compete with
Denodo's support of the Restful/Odata standard -
both to provide data (northbound) and to access
data from the sources (southbound).”
– Business Analyst, Swiss Re

8. Centralized metadata, security & governance
Abstracts data source security models and enables
single-point security and governance.
Extends single-point control across cloud and on-
premises architectures
Provides multiple forms of metadata (technical,
business, operational) to facilitate understanding of
data.
…simplifies data security, privacy, audit
Our Denodo rollout was one of the easiest and most successful
rollouts of critical enterprise software I have seen. It was
successful in handling our initial, security, use case
immediately, and has since shown a strong ability to cover
additional use cases, in particular acting as a Data Abstraction
Layer via it's web service functionality.”
– Enterprise Architect, Asurion

9. The Role of Denodo: Advanced Optimization
Optimizer specifically designed for logical
architectures
Minimizes data movement and maximizes
local processing at the origin systems
Cost-based automatic optimization
decisions
Optimize distributed queries across large repositories
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

10. 10
Query Optimization: Example (1)
Naive Strategy (BI Tools, BDI Tools, Simple
federation engines):
join
union
group by
Customers (3M)
Sales previous years
(3B)Sales this year
(290M)
290M rows
300M rows
(sales previous
year)
3M rows
593M rows through
the network
Obtain Total Sales By Customer Country in the Last Two Years

11. 11
Query Optimization: Example (2)
Denodo Strategy
join
union
group by
Customers (3M)
Sales previous years
(3B)Sales this year
(290M)
3M rows (sales by
customer this year)
3M rows (sales
by customer
previous year)
3M rows
9 M rows through the
network
Obtain Total Sales By Customer Country in the Last Two Years
group by
customer
group by
customer

12. Query Optimization: Example (and 3)
union
group by
3M rows
(sales by customer
this year)
3M rows
(sales by
customer
previous year)
3M rows
(customers)
Aggregation
pushdowngroup by
customer
group by
customer
join
Integrated
MPP
processing
System Execution Time
Optimization
Technique
No Rewriting 20 min None
Denodo 6 51 sec Aggregation push-down
Denodo 7 24 sec
Aggregation push-down
+ MPP integration

13. Demo
Logical Data Warehouse Scenario
1. Create Data Sources and Import Source Tables in Denodo
• DW: Sales current year
• Hadoop: Sales previous years
• Oracle: Customer data
2. Create logical sales view
3. Run example reports and see execution trace
13

14. Demo
14

15. Q&A

16. Next steps
Download Denodo Express:
www.denodoexpress.com
Access Denodo Platform on AWS:
www.denodo.com/en/denodo-platform/denodo-
platform-for-aws

17. 17

18. Thank you!
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