This document contains slides from a presentation by Gurcan Orhan about data warehousing and business intelligence. It discusses the history of data warehousing and defines key concepts. It also provides an overview of Oracle cloud offerings for data management, integration, applications, platform services, and infrastructure. Additionally, it covers topics like data marts, big data, project management methodologies, and emerging trends in data warehousing.
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Is Data Warehouse Dying?
1. Gürcan ORHAN
Enterprise Data Warehouse
Architect
http://gurcanorhan.wordpress.com
@gurcan_orhan
http://tr.linkedin.com/in/gurcanorhan
2.
3. 30 May 17 #OTNEMEATour
What is the relation
between B. B. King,
Chevy and DWH?
4. 30 May 17 #OTNEMEATour
WHO’S THAT GUY TALKING ?
+20 years of IT experience.
+14 years of DWH experience.
+10 years of Oracle Data Integrator experience.
+8 years of Oracle Warehouse Builder experience.
Sybase Power Designer, ERwin Data Modeler, SDDM
OBIEE, Cognos, Microstrategy, Business Objects, Qlikview, Tableau
IBM Data Stage, SAP Data Services, Informatica, etc…
Oracle Excellence Awards - Technologist of the Year 2011 :
Enterprise Architect
DWH & BI Chair : TROUG (Turkish Oracle User Group)
Published Customer Snapshot for NODI @Oracle.com
Published videos about ODI @Oracle.com
Published OTN Podcasts about
“Data Warehousing and ODI”
“ODI and the Evolution of Data Integration”
Lots of “2MTT”s
Articles in OTech Magazine, SearchSoftwareQuality.com
Annual panelist for ODTUG “Ask the Experts Panel : ODI”
Presenter in OOW since 2010 (7 times in a row ⭐ )
Presenter in many OUG conferences in globe
Presenter in various universities in Turkey
5. Ekol Germany
Warehousing
Solutions
begin with the
Kardelen Facility
1996 2003 2010 2012 2014 2016
201520132011200820021990
Acquire STS Int.
Transport
Ekol Bosnia
Ekol France
Ekol Greece
Ekol Ukraine
Ekol Spain
Ekol Bulgaria
Ekol Czech Rep.
Ekol Iran
Ekol PolandEkol Italy
Ekol Romania
Ekol HungaryAcquire
Unok/Unatsan
Rainbow
Replaced by
Quadro
(software)
Intermodal
operations Ro-Ro
operations
Established
Ekol Milestones
8. 30 May 17 #OTNEMEATour
History of Data Warehouse starts with;
• EIS (Enterprise Information Systems)
• DSS (Decision Support Systems)
• Data Warehousing and Business Intelligence (DWH / BI)
... and the story continues with DWH 2.0 and Cloud!
A data warehouse is a subject-oriented, integrated, time-variant and
non-volatile collection of data in support of management's decision making
process. (Bill Inmon - 1990)
Business Intelligence is processing of converting data into knowledge. (Gartner
Group)
DATA
INFORMATION
KNOWLEDGE
DECISION
HISTORY OF DWH
subject-oriented integrated time-variant
non-volatile
12. 30 May 17 #OTNEMEATour
Relatively
a little bit
longer...
IMPLEMENTATION TIMELINE
13. 30 May 17 #OTNEMEATour
... but ...
ALTERNATIVE REQUIREMENT
14. 30 May 17 #OTNEMEATour
COMPONENTS OF A DWH
I told you, I will cover
every one of you… 🤗
15. 30 May 17 #OTNEMEATour
ORACLE CLOUD OFFERINGS
APPLICATIONS
Human Capital Management Supply Chain Management Enterprise Performance Mgmt
Global Human Resources Inventory Management Enterprise Planning
Talent Management Logistics Planning and Budgeting
Workforce Rewards Manufacturing Enterprise Performance Reporting
Workforce Management Order Management Account Reconciliation
Work Life Solutions Procurement Financial Consolidation and Close
HCM Cloud for Midsize Product Lifecycle Management Profitability and Cost Management
Enterprise Resource Planning Product Master Data Management Tax Reporting
Financials Supply Chain Planning Customer Experience
Revenue Management Industry Solutions Marketing
Accounting Hub Reporting Communications Sales
Project Financial Management Financial Services Service
Project Management Consumer Goods Configure, Price, and Quote (CPQ)
Procurement High Tech and Manufacturing Commerce
Risk Management Higher Education Loyalty
ERP Cloud for Midsize Hospitality Engagement (Sales and Service)
Internet of Things Apps Utilities Customer Data Management
IoT Asset Monitoring Data Sales Performance Management
IoT Production Monitoring Data as a Service (DaaS) CX Cloud for Midsize
IoT Fleet Monitoring ID Graph SaaS Analytics
IoT Connected Worker Data Cloud Verticals HCM Analytics
Deployment Options Social CRM Analytics
for Financial Services Social Network ERP Analytics
for Retail Services Social Marketing SCM Analytics
for Public Sector Social Engagement and Monitoring Adaptive Intelligent Apps
for Department of Defense
PLATFORM
Data Management Integration
Database Backup Data Integrator
Database Integration
MySQL SOA
NoSQL Database GoldenGate
Big Data Internet of Things
Big Data - Compute Edition API Platform
Event Hub Process
Application Development Management
Java Application Performance Monitoring
Mobile Infrastructure Monitoring
Messaging Log Analytics
Application Container Cloud (Java SE & Node) Orchestration
Developer IT Analytics
Application Builder Business Analytics
API Catalog Analytics Cloud
Content and Experience Business Intelligence
Content and Experience Big Data Discovery
WebCenter Portal Cloud Big Data Preparation
DIVA Cloud Data Visualization
Security Essbase
CASB
Identity
Security Monitoring and Analytics
Configuration and Compliance
INFRASTRUCTURE
Infrastructure
Compute
Storage
Network
Container
Ravello
Cloud Machine
Bare Metal Cloud Services
Architecture
Compute Service
Networking Service
Storage Services
Governance Services
Database Cloud Service
Load Balancing Service
Everything you need to set up a
DWH is already in the Cloud…
* as of 12.05.2017
16. 30 May 17 #OTNEMEATour
ALTERNATIVES (ODM APPROACH)
Did I say “bullshit”.
OMG !!!
17. 30 May 17 #OTNEMEATour
Datamart is;
• Subject oriented
• Integrated with related data
• Usually fed by a single source system (what about history !!!)
PROPERTY DATA WAREHOUSE DATAMART
Content Enterprise Department Based
Subject Many Single and
Business Unit oriented
Data Source Multiple Single
Implementation Month – Year Month
Can be dependent or independent.
• Dependent DM : Fed from DWH, easy ETL, part of enterprise plan
• Independent DM : Discrete, high operational cost, fed from external sources,
prepared especially for analytical requirements
WHAT IS A DATAMART?
18. 30 May 17 #OTNEMEATour
PROPERTY OPERATIONAL
SYSTEM
DATA WAREHOUSE DATA MART
Response
Time
Milliseconds / Seconds Seconds / Minutes Seconds / Minutes
Operation DML (Data
Manipulation
Language)
Prioritizing Read Only Read Only
Data Nature 30 – 60 days Temporal Snapshot Temporal Snapshot
Data
Organization
Application Subjective, Temporal Subjective, Temporal
Data Volume Small / Medium Big / Very Big Medium / Big
Data Source Operational, Internal Operational, Internal,
External
Operational, Internal,
External
Activities Process Based Analysis Based Analysis Based
DWH & DM & OLTP
19. 30 May 17 #OTNEMEATour
PROJECT MANAGEMENT METHODOLOGY
Anybody having a
gun? 🔫
Shout now.
20. 30 May 17 #OTNEMEATour
BIG DATA
“V”s are good.
21. 30 May 17 #OTNEMEATour
BIG DATA MATURITY MODEL
28. 30 May 17 #OTNEMEATour
long
live
Data Warehouse...
EVENTUALLY…
29. 30 May 17 #OTNEMEATour
http://gurcanorhan.wordpress.com
@gurcan_orhan
http://tr.linkedin.com/in/gurcanorhan
Notes de l'éditeur
IBM’s 5MB hard disk drive in 1956
B.B. King started playing guitar at 1947 (hitchhiked to Memphis, Tennessse), recorded total of 622 studio albums, live albums, Singles, EPs and Compilation albums.
Chevrolet Bel Air started production in 1957 and ended in 1981.
What is the relation between B.B. King, Chevy and DWH?
Subject-Oriented: A data warehouse can be used to analyze a particular subject area. For example, "sales" can be a particular subject.
Integrated: A data warehouse integrates data from multiple data sources. For example, source A and source B may have different ways of identifying a product, but in a data warehouse, there will be only a single way of identifying a product.
Time-Variant: Historical data is kept in a data warehouse. For example, one can retrieve data from 3 months, 6 months, 12 months, or even older data from a data warehouse. This contrasts with a transactions system, where often only the most recent data is kept. For example, a transaction system may hold the most recent address of a customer, where a data warehouse can hold all addresses associated with a customer.
Non-Volatile: Once data is in the data warehouse, it will not change. So, historical data in a data warehouse should never be altered.
Data has a life : it born, lives, dies.
Relatively a little bit longer…
… but ...
Cover everybody
I told you, I will covert every one of you…
Everything you need to set up a DWH is already in the Cloud…
BULLSHIT at the end of slide.
Did I say “bullshit”. OMG !!!
DWH inside DWH, give the example of Vodafone Turkey.
OLTP : Operational Legacy Transactional Processing
Anybody having a gun? 🔫
Shout now.
“V”s are good.
This is my take for Big Data…
Who’s saying that ETL is retiring?
How come I can remember the thing I mentioned 30 minutes before. #Proud
Next slide will be awesome.! #KillingMachine
At last he is finishing and I can go to my more focused session. What was that schedule again?
Please don’t check you phones.