Denodo DataFest 2017: Integrating Big Data and Streaming Data with Enterprise Data
Denodo DataFest 2016: Accelerating Self-Service BI with Logical Data Warehouse
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Transcript of Denodo DataFest 2016: Accelerating Self-Service BI with Logical Data Warehouse
O C T O B E R 1 8 , 2 0 1 6 S A N F R A N C I S C O B A Y A R E A , C A
#DenodoDataFest
RAPID, AGILE DATA STRATEGIESFor Accelerating Analytics, Cloud, and Big Data Initiatives.
Accelerating Self Service BI via a Logical Data Warehouse
Mark Blanchette
VP, Business Technology and Data ManagementOctober 2016
Agenda:
1. Introduction
2. Seacoast Use Case
3. Logical Data Warehouse Components
4. Accelerating Self-Service BI
5. Summary & Q&A
3
About Seacoast Bank [NASDAQ: SBCF]Attractive Geography; Deep local Roots; Benefiting from Investments in Digital Transformation and Commercial Loan Platform, and Strategic Acquisitions
Seacoast Use Case
Background• Historically, centralized reporting
• Majority of the data came from the “core”; hosted data warehouse environment
• Reporting, mostly batch, with little interaction with the live data. Output excel, .pdf files
Business Drivers• Business self-service: “Democratization of the data”
• Unification of information assets
• Enablement and speed to onboard of 3rd party and on-premisedata with the hosted environment
• Lessen Seacoast’s dependency on external vendors for maintaining our information
Project Approach
Socialize
•Discuss with Executives
•Obtain financial support
Selection
•POC with Vendors
Engineer
•Design Solution
•Model Data
Build
•Infrastructure
•Logical views
•Report templates
Pilot
• Business report users
•Verify data
Deploy
•Deploy solution to production
What is a Logical Data Warehouse (LDW)?
According to denodo:
A logical data warehouse is a data system that follows the ideas of a traditional Enterprise Data Warehouse (star or snowflake schemas) and includes , in addition to one (or more) core DWs, data from external sources.
LDW provides a single view of all the data, without having to transport it from its original silo, thus easing consumption of the information.
8Presentation Title Here 8
Data Virtualization
Repository Management
Metadata Management
Taxonomy/ontology
Performance Monitoring
LDW From a Component Perspective
Service Level Agreements
Solution Overview
• Virtualize data using Denodo and presented it as datamarts to reporting
tools
• Physically move/cache data, when needed, for performance reasons
• Utilize SAS Visual Analytics for Enterprise Reporting, interactive reports,
and analytical reporting
Hosted Data Warehouse
Application Data
Store (ADS)
Dimensional
Data
Warehouse
(DDW)
Data Virtualization “NetFlix Streaming”
Physically Move Data “DVD”
Data Sources Transport & Storage Layer
Data Marts -
Physical
(MySql)
Virtualized
ViewsVisual Tools
Operational
Reports
Ad-Hoc
Queries
Presentation Layer
Web
ServicesM
eta
-Data
Other sources of Data
Loan
Origination
Systems
Other
Application
Data
Data
Defin
itions
Mobile
Visuals
Reporting
Ad Hoc Tools
LDW Architecture Metadata Mgmt.
Data Virtualization
Integration Self Service BI
Taxonomy/ontology, performamce SLA’s
Repository Mgmt.
Datamart Creation
• Leveraged the dimensional model (star and snow-flaked schema) from the hosted environment
• Created 9 datamarts covering the main business subject areas of the bank
• Created over 30 derived (virtual) views segmented into the business datamarts
• Build once , deploy everywhere
• Views loaded into SAS Visual Analytics, where they are available in the in-memory model for reporting and analytics
Derived (Virtual) Views
Enterprise Data Dictionary• Created a custom web based application for managing
business terms• Linked with views to provide business friendly names in SAS Visual
Analytics
• Used for both development and self-service BI
• Terms can be custom tagged, classified by security classification, assigned to a data owner.
Metadata Linkage• Created a process to link data dictionary term names to
derived views. Business “friendly” terms
• As data stewards, update, add terms, they can be integrated as part of the end user interface
• Terms on reports can be easily searched in the dictionary
Physical Column Names Business Term Names
Self Service BI
• Sources of data can be consolidated in the logical data warehouse and be made easily available to reporting engine
• Webinar was developed to guide business users through the basics
• Easy to use web based reporting and analytics platform – SAS Visual Analytics
• Departmental datamarts that are optimized for reporting
• Report templates, that replace one or more static reports, yet deliver dynamic content
• Reduce and eventually eliminate ad-hoc type service requests
Report Example
Logical Data Warehouse Benefits
50%
Less time vs traditional data warehouse approaches
3 Hrs. VS 3 Days
Sourcing data for BI vs traditional ETL methods
Data from different technologies/sources can be easily combined
At least for Seacoast, a LDW will help free up resources to work on other Enterprise projects.
Additional Benefits of Denodo VDP
• Real time web services• Utilized web services during the project to expose data to custom
applications.
• Data governance • Data lineage, impact analysis
• ITPilot : • Capture data from websites for including into derived views.
Summary
• Using a Logical Data Warehouse (LDW) can significantly shorten the time to source/model data vs traditional approaches.
• With a LDW, IT can quickly deliver datamarts and views of the data that are optimized for reporting.
• Lines of business, analysts and data scientists can focus on decision making rather than how to source the data.
• Creating reports that allow for dynamically selecting the data, will allow for higher degree of reusability and focus on just the data needed.
Panel
M O D E R A T E D B Y :
Mark Blanchette
VP Data Warehouse, Seacoast Bank
Praveen Saluja
Director of BI, Fastaff
Chandra Siv
General Manager and Head – Data & Analytics Solutions, Mindtree
Lakshmi Randall
Head of Product Marketing, Denodo