Which technologies increase real-time data delivery? Database and other technologies are taking on support for in-memory fabric to further boost the speed of data delivery in this impatient world. Watch live presentation here: https://goo.gl/opVEPj In this presentation, you will learn: • How data virtualization uses in-memory fabric to further accelerate the delivery of data to business users. • How data virtualization provides cost savings and agility gains. • How a logical data warehouse architecture can minimize data replication.

1. Accelerating Analytics Architectures with Parallel In-
memory Processing and Data Virtualization
Alberto Pan
CTO, Denodo

2. Agenda1. Logical Architectures for Analytics
2. Performance: Accelerating Data Delivery
3. Demo

3. 3
Logical Architectures for Analytics

4. 4
Logical Architectures: Data Virtualization

5. 5
The Role of Denodo
Combine and publish data from any type of data source with a few clicks
Abstracts application from changes in IT infrastructure
Exposes different logical views adapted to each type of user / LOB
Single entry point to apply security and governance policies
Advanced distributed query optimization
…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. 6
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

7. 7
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

8. 8
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 13 sec
Aggregation push-down + MPP
integration

9. Demo

10. Q&A

11. Thank you!
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