This document provides an agenda and summaries for an educational seminar on self-service BI, logical data warehouses, and data lakes held in December 2016. The agenda includes presentations on customer use cases using these technologies, architectural patterns and performance considerations, demonstrations, and a panel discussion. One presentation provides details on how a company called Vizient is using a logical data warehouse approach powered by data virtualization to enable self-service BI across distributed data sets and integrate data from mergers and acquisitions. Key challenges addressed include user security, data timeliness for reporting, and supporting multiple related projects on the same data.

1. Educational Seminar:
Self-service BI, Logical Data Warehouse and Data Lakes
December 2016

2. Speakers
Chuck DeVries
VP, Enterprise Architecture
Vizient
Ravi Shankar
CMO
Denodo
Chris Walters
Sr. Solutions Consultant
Denodo
Charles Yorek
VP, Business Analytics
iOLAP

3. Agenda1.Customer Use Case: Powering Self-Service BI with Logical
Data Warehouse and Operationalizing Logical Data Lakes
2.Logical Data Lakes/ Warehouse: Architectural Patterns and
Performance Considerations
3.Demo: Building Logical Data Lakes/ Warehouse using Data
Virtualization
4.Best Practices: Big Data Virtualization Deployment and Management
5.Panel: Self-Service BI, Logical Data Warehouse, Data Lakes

4. Powering Self Service BI with Logical Data
Warehouses and Operationalizing Data
Lakes
Chuck DeVries
December 2016

5. AGENDA
- Who is Vizient
- Self Service BI on distributed data sets
- Modern Data Architecture

6. Vizient Presentation │ Date │ Confidential Information6
Who is Vizient?
• Combination of VHA, University HealthSystem
Consortium, Novation, MedAssets Spend and
Clinical Resource Management and Sg2
• Experts with the purchasing power, insights
and connections that accelerate performance
for members

7. Vizient Presentation │ Date │ Confidential Information7
Purpose, mission, strategic aspirations
Purpose
To ensure our members
deliver exceptional, cost-
effective care
Mission
To connect members
with the knowledge,
solutions and expertise
that accelerate
performance
Strategic Aspirations
• Become an
indispensable partner
to health care
organizations
• Become a leader
in health care
innovation
• Accelerate our
growth rate

8. Vizient Presentation │ Date │ Confidential Information8
Vizient members span the care continuum
Vizient serves thousands of health care organizations
across the nation, from independent, community-based
organizations to large, integrated systems including
• Acute care hospitals
• Academic medical centers
• Non-acute community health care providers
• Pediatric facilities

9. Vizient Presentation │ Date │ Confidential Information9
Member-owned, member-driven
MEMBERSHIP BENEFITS
• Harness powerful insights
• Accelerate performance
• Achieve scale and efficiency
• Make innovative connections
• Be more agile
• Build knowledge
• Gain advocates on important policy issues
We measure our success by our members’ success. We fuel
powerful connections that help members focus on what they
do best: deliver exceptional, cost-effective care.

10. We deliver brilliant, data-driven resources and
insights — from benchmarking and predictive analytics
to cost-savings — to where they’re needed most.
Empowering brilliant connections

11. Vizient Presentation │ Date │ Confidential Information11
Unmatched insight and expertise
9 out of 10
of the U.S. News & World Report Best
Hospitals 2014-2015 Honor Roll
utilized our contracts and services.
~$100B
Vizient represents approximately
$100 billion in annual purchasing
volume — the largest in the
industry.
200+
Vizient member hospitals have
achieved remarkable
improvements in quality and
patient safety through our
Hospital Engagement Network.
More
than 1/3
Vizient provides services
to more than one-third of the
nation’s hospitals.
Information is inclusive of MedAssets Spend and Clinical Resource
Management segment, including Sg2.

12. Vizient Presentation │ Date │ Confidential Information12

13. Examples of powering self
service discovery with a
Logical Data Warehouse
approach
Vizient Presentation │ Date │ Confidential Information13

14. Financial Data Mart
Primary Use Case: Unify disparate accounting and finance data marts
across various legacy organizations into a logical data warehouse
Secondary Use Cases
• Provide a unified source for key BI initiatives like the GPO Dashboard
• Support reporting needs as legacy systems are migrated or replaced during
integration of Vizient and L-MDAS (dbVision, etc.)
• Provide a final resting place for archived legacy sources like Solomon, Epicor,
etc.
Vizient Presentation │ Date │ Confidential Information14
VHA
MedAssets
UHC

15. Financial Data Mart
Architectural Approach
• Denodo was selected as the data platform in
order to utilize the following features of the
software:
–Data Virtualization allows sources in various mediums and
locations to be integrated without physically moving the data
–Data Abstraction allows data to be represented consistently within
the datamart while data sources are moved or replaced behind
the scenes
–Data Integration allows for a single seamless view to be created
across a subject area (e.g. “Supplier Sales”) with varied data
transformation rules for each data source within the subject area
(PRS, dbVision) allowing a logical data warehouse to be created
without the need to instantiate a physical on
Vizient Presentation │ Date │ Confidential Information15

16. GPO Dashboard
Primary Use Case: Provide a consolidated view of supplier sales data
across all customers of legacy Vizient & Med Assets organizations.
Architectural Approach
• Financial Datamart (on Denodo) for data source
• Denodo TDE Exporter Tool for daily data extracts to Tableau:
– Report Data
– Report User Security
• Tableau for report development and distribution
Vizient Presentation │ Date │ Confidential Information16
Over 400 active users across 6
departments

17. GPO Dashboard
Key Challenges
• Balance between data timeliness and report performance
– Tableau reports performed best utilizing the TDE format
(cached/extracted dataset) as opposed to a live connection
– This meant that the report caches required daily refreshes, and data
extraction had to be appropriately tuned
– Denodo features such as dataset statistics and indexing greatly
contributed to this performance tuning
• Provisioning user security at cell level
– The requirement for some internal report users to be restricted to the
members/customers to which they are assigned meant that a new
report security approach was needed
– Reliance on TDEs for report data necessitated the integration of security
in the reporting layer
– Tableau’s “data blending” feature allows user security to be specified
within a separate dataset
– This also supports reuse of the security view across logical data
warehouse views
Vizient Presentation │ Date │ Confidential Information17

18. Contract Sales Actualizer Dashboard
Primary Use Case: Integrate Member Spend and Supplier Sales
data from all Vizient organizations to identify opportunities for
increasing contract utilization
Other Use Cases:
• Maintain consistency (Single Source Of Truth) with GPO
dashboard regarding:
– Supplier Sales Data
– Dimension Data
– User Security
Architectural Approach
• Data source utilizes Denodo to reuse overlapping datasets (sales,
dimensions, security) while allowing separate virtualized views to
be created for new datasets (member spend) which can be also be
reused by future projects via a logical data warehouse
• Reporting components match approach used by GPO Dashboard
Vizient Presentation │ Date │ Confidential Information18

19. Contract Sales Actualizer Dashboard
Key Challenges
• Successful integration of Exadata RDM as a data source for Denodo
– Approach utilizes the strength of Exadata RDBMS for aggregating
large quantities of data quickly
– Denodo to integrate the data with similar legacy SQL Server data
sources to create a comprehensive view of Vizient member spend
• Scalability/Configuration Management
– Advances were made to support parallel development of this
project and continued efforts on GPO dashboard
– Compartmentalization features within Denodo allow for code
changes in each project to be version controlled and assessed for
dependencies
– Process guidelines are being authored to allow for multiple
development efforts on the same datasets
Vizient Presentation │ Date │ Confidential Information19

20. Modern Data Architecture
Vizient Presentation │ Date │ Confidential Information20

21. Virtual
warehouse
Modern Data Architecture
Vizient Presentation │ Date │ Confidential Information21
Open
Data
Purchase
Data
RDBMS
Rules
RDBMS
ODS
Data
warehouse

22. Our central focus is helping members
apply data and insights in new ways to
achieve sustainable results. Our
success is ultimately defined by the
success of our members in serving their
patients and communities.
Curt Nonomaque, President and CEO, Vizient

23. Logical Data Lakes/ Warehouse:
Architectural Patterns and Performance Considerations
Ravi Shankar, CMO
December 2016

24. Agenda1.The Logical Data Warehouse
2.Architectural Patterns
3.Performance Considerations
4.Customer Success Studies

25. Logical Data Warehouse
Description:
 A semantic layer on top of the data warehouse that keeps the business data
definition.
 Allows the integration of multiple data sources including enterprise systems,
the data warehouse, additional processing nodes (analytical appliances, Big
Data, …), Web, Cloud and unstructured data.
 Publishes data to multiple applications and reporting tools.
27

26. Logical Data Warehouse
Description:
 “The Logical Data Warehouse (LDW) is a new data management architecture for
analytics combining the strengths of traditional repository warehouses with
alternative data management and access strategy. The LDW will form a new
best practice by the end of 2015.”
 “The LDW is an evolution and augmentation of DW practices, not a replacement”
 “A repository-only style DW contains a single ontology/taxonomy, whereas in the
LDW a semantic layer can contain many combination of use cases, many
business definitions of the same information”
 “The LDW permits an IT organization to make a large number of datasets
available for analysis via query tools and applications.”
28
Gartner Definition
Gartner Hype Cycle for Enterprise Information Management, 2012

27. 29
Data Virtualization as the Data Integration Layer
Data Virtualization as Data
Integration/Semantic Layer
Data Virtualization
EDW ODS
• Move data integration and semantic layer to
independent Data Virtualization platform
• Purpose built for supporting data access
across multiple heterogeneous data sources
• Separate layer provides semantic models for
underlying data
• Physical to logical mapping
• Enforces common and consistent security
and governance policies
• Gartner’s recommended approach

28. Logical Data Warehouse
30
EDW Hadoop
Cluster
Sales
HDFS
Files
Document
Collections
NoSQL
Database
ERP
Database Excel

29. What about the Logical Data Lake?
A Data Lake will not have a star or snowflake schema, but rather a more
heterogeneous collection of views with raw data from heterogeneous
sources
The virtual layer will act as a common umbrella under which these
different sources are presented to the end user as a single system
However, from the virtualization perspective, a Virtual Data Lake shares
many technical aspects with a LDW and most of these contents also
apply to a Logical Data Lake

30. Architectural Patterns
For a Logical Data Warehouse

31. 33
Common Patterns for a Logical Data Warehouse
1. The Virtual Data Mart
2. DW + MDM
3. DW + Cloud
4. DW + DW
5. DW historical offloading

32. 34
1. Virtual Data Marts
Business friendly models defined on top of one or multiple systems,
often “flavored” for a particular division
Motivation
 Hide complexity of star schemas for business users
 Simplify model for a particular vertical
 Reuse semantic models and security across multiple reporting engines
Typical queries
 Simple projections, filters and aggregations on top of curated “fat tables” that
merge data from facts and many dimensions
Simplified semantic models for business users

33. 35
1. Virtual Data Marts
Time Dimension Fact table
(sales)
Product
Retailer
Dimension
Sales
EDW Others
Product
Prod. Details

34. 36
2. DW + MDM
Slim dimensions with extended information maintained in an external
MDM system
Motivation
 Keep a single copy of golden records in the MDM that can be reused across
systems and managed in a single place
Typical queries
 Join a large fact table (DW) with several MDM dimensions, aggregations on
top
Example
 Revenue by customer, projecting the address from the MDM

35. 37
2. DW + MDM dimensions
Time Dimension Fact table
(sales) Product Dimension
Retailer
Dimension
EDW MDM

36. 38
3. DW + Cloud dimensional data
Fresh data from cloud systems (e.g. SFDC) is mixed with the EDW, usually
on the dimensions. DW is sometimes also in the cloud.
Motivation
 Take advantage of “fresh” data coming straight from SaaS systems
 Avoid local replication of cloud systems
Typical queries
 Dimensions are joined with cloud data to filter based on some external attribute
not available (or not current) in the EDW
Example
 Report on current revenue on accounts where the potential for an expansion is
higher than 80%

37. 39
3. DW + Cloud dimensional data
Time Dimension Fact table
(sales) Product Dimension
Customer
Dimension
CRM
SFDC
Customer
EDW

38. 40
4. Multiple DW integration
Motivation
 Merges and acquisitions
 Different DWs by department
 Transition to new EDW Deployments (migration to Spark, Redshift, etc.)
Typical queries
 Joins across fact tables in different DW with aggregations before or after the JOIN
Example
 Get customers with a purchases higher than 100 USD that do not have a fidelity
card (purchases and fidelity card data in different DW)
Use of multiple DW as if it was only one

39. 41
4. Multiple DW integration
Time
Dimensi
on
Sales fact
Product
Dimension
Region
Finance EDW
City
Marketing EDW
Customer Fidelity factsProduct
Dimension
*Real Examples: Nationwide POC, IBM tests
Store

40. 42
5. DW Historical Partitioning
Only the most current data (e.g. last year) is in the EDW. Historical data is
offloaded to a Hadoop cluster
Motivations
 Reduce storage cost
 Transparently use the two datasets as if they were all together
Typical queries
 Facts are defined as a partitioned UNION based on date
 Queries join the “virtual fact” with dimensions and aggregate on top
Example
 Queries on current date only need to go to the DW, but longer timespans need to merge
with Hadoop
Horizontal partitioning

41. 43
5. DW Historical offloading
Horizontal partitioning
Time Dimension Fact table
(sales) Product Dimension
Retailer
Dimension
Current Sales Historical Sales
EDW

42. Performance Considerations
In a Logical Data Warehouse

43. 45
It is a common assumption that a virtualized solution will
be much slower than a persisted approach via ETL:
1. There is a large amount of data moved through the
network for each query
2. Network transfer is slow
But is this really true?

44. 46
Denodo has done extensive testing using queries from the standard benchmarking test
TPC-DS* and the following scenario
Compares the performance of a federated approach in Denodo with an MPP system where
all the data has been replicated via ETL
Customer Dim.
2 M rows
Sales Facts
290 M rows
Items Dim.
400 K rows
* TPC-DS is the de-facto industry standard benchmark for measuring the performance of
decision support solutions including, but not limited to, Big Data systems.
vs.
Sales Facts
290 M rows
Items Dim.
400 K rows
Customer Dim.
2 M rows
Performance Comparison
Logical Data Warehouse vs. Physical Data Warehouse

45. 47
Performance Comparison
Query Description
Returned
Rows
Time Netezza
Time Denodo
(Federated Oracle,
Netezza & SQL Server)
Optimization Technique
(automatically selected)
Total sales by customer 1,99 M 20.9 sec. 21.4 sec. Full aggregation push-down
Total sales by customer and
year between 2000 and 2004
5,51 M 52.3 sec. 59.0 sec Full aggregation push-down
Total sales by item brand 31,35 K 4.7 sec. 5.0 sec. Partial aggregation push-down
Total sales by item where
sale price less than current
list price
17,05 K 3.5 sec. 5.2 sec On the fly data movement
Logical Data Warehouse vs. Physical Data Warehouse

46. 48
Performance and optimizations in Denodo
Focused on 3 core concepts
Dynamic Multi-Source Query Execution Plans
Leverages processing power & architecture of data sources
Dynamic to support ad hoc queries
Uses statistics for cost-based query plans
Selective Materialization
Intelligent Caching of only the most relevant and often used
information
Optimized Resource Management
Smart allocation of resources to handle high concurrency
Throttling to control and mitigate source impact
Resource plans based on rules

47. 49
Performance and optimizations in Denodo
Comparing optimizations in DV vs ETL
Although Data Virtualization is a data integration platform,
architecturally speaking it is more similar to a RDBMs
Uses relational logic
Metadata is equivalent to that of a database
Enables ad hoc querying
Key difference between ETL engines and DV:
ETL engines are optimized for static bulk movements
Fixed data flows
Data virtualization is optimized for queries
Dynamic execution plan per query
Therefore, the performance architecture presented here
resembles that of a RDBMS

48. Success Stories
Customer Case Studies

49. Autodesk Overview
• Founded 1982 (NASDAQ: ASDK)
• Annual revenues (FY 2015) $2.5B
 Over 8,800 employees
• 3D modeling and animation software
 Flagship product is AutoCAD
• Market sectors:
 Architecture, Engineering, and Construction
 Manufacturing
 Media and Entertainment
 Recently started 3D Printing offerings
51

50. Business Drivers for Change
• Software consumption model is changing
 Perpetual licenses to subscriptions
 User want more flexibility in how they use software
• Autodesk needed to transition to subscription pricing
 2016 – some products will be subscription only
• Lifetime revenue higher with subscriptions
 Over 3-5 years, subscriptions = more revenues
• Changing a licensing model is disruptive
52

51. Technology Challenges
• Current ‘traditional’ BI/EDW architecture not
designed for data streams from online apps
 Weblogs, Clickstreams, Cloud/Desktop apps, etc.
• Existing infrastructure can’t simply ‘go away’
 Regulatory reporting (e.g. SEC)
 Existing ‘perpetual’ customers
• ‘Subscription’ infrastructure work in parallel
 Extend and enhance existing systems
 With single access point to all data
• Solution – ‘Logical Data Warehouse’
53

52. Logical Data Warehouse at Autodesk
54

53. Logical Data Warehouse at Autodesk
Traditional BI/Reporting
55

54. Logical Data Warehouse at Autodesk
‘New Data’ Ingestion
56

55. Logical Data Warehouse at Autodesk
Reporting on Combined Data
57

56. 58
Problem Solution Results
Case Study Autodesk Successfully Changes Their
Revenue Model and Transforms Business
 Autodesk was changing their business
revenue model from a conventional
perpetual license model to
subscription-based license model.
 Inability to deliver high quality data in
a timely manner to business
stakeholders.
 Evolution from traditional operational
data warehouse to contemporary
logical data warehouse deemed
necessary for faster speed.
 General purpose platform to deliver
data through logical data warehouse.
 Denodo Abstraction Layer helps live
invoicing with SAP.
 Data virtualization enabled a culture
of “see before you build”.
 Successfully transitioned to
subscription-based licensing.
 For the first time, Autodesk can do
single point security enforcement and
have uniform data environment for
access.
Autodesk, Inc. is an American multinational software corporation that makes software for the
architecture, engineering, construction, manufacturing, media, and entertainment industries.

57. Demo

58. BIG DATA VIRTUALIZATION
DEPLOYMENT AND
MANAGEMENT
Best Practices

59. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 61

60. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 62
“Good work building ETL jobs this
year”
- No CEO Ever…

61. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 63
SO WHY DO WE STILL BUILD THEM?

62. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 64
BUSINESS VALUE IS KING

63. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 65
BUSINESS VALUE IS KING

64. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 66
BIGGER SURE ISN’T EASIER
• SKILLS
• EASY IN/HARD OUT
• ALL DATA SOURCES AREN’T EQUAL

65. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 67
VIRTUALIZATION BRIDGES THE SKILLS
GAP

66. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 68
VIRTUALIZATION PROVIDES EASE OF USE
How the data goes in… How it gets back out…

67. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 69
SOMEBODY BOUGHT SOMETHING BACK
IN THE DAY
• WE HAVE TO DEAL WITH
LEGACY
• HOMOGENEITY ISN’T
REALISTIC
• ALL DATA SOURCES
AREN’T EQUAL

68. IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL 70
WHAT NOW?
• POC USING DENODO
EXPRESS OR AWS
• IOLAP CAN HELP BUILD A
ROADMAP

69. Founded in 2000
 16 years Delivering Success
Headquartered in Frisco, Texas
 National Customer Base
 Extended Workforce
U.S. Company with Offshore Capabilities
 60 consultants in the U.S. (full-time, salaried)
 50 consultants in Europe (Offshore – BIDC)
IOLAP, INC. - PROPRIETARY AND CONFIDENTIAL
IOLAP OVERVIEW
Focused solely on big data, data strategy, advanced analytics, and reporting
71
Onsite
Near Shore
Offshore

70. Speakers
Chuck DeVries
VP, Enterprise Architecture
Vizient
Ravi Shankar
CMO
Denodo
Chris Walters
Sr. Solutions Consultant
Denodo
Charles Yorek
VP, Business Analytics
iOLAP

71. Next Steps
Attend the webinar “Realizing the Promise
of Data Lakes” on December 15
Register at: www.denodo.com
Access Denodo on AWS
Visit: www.denodo.com/en/denodo-platform/denodo-platform-for-aws
Download Denodo Express
The free way to Data Virtualization!
Download from: www.denodo.com

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