safe harbor statement - ncb · oracle multitenant high consolidation density, transparent to...
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Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Safe Harbor Statement
The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.
Oracle Confidential – Internal 2
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
The IOT Platform
Alaa Fahmy Core Technology Product Leader - ECEMEA
3
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Continuous Oracle Database Innovations Preserving customer’s investment though each new Computing Era
Stored Procedures Partitioning
Parallel Query Unstructured Data
Resource Management Real Application Clusters
Data Guard XML
IOT Big Data SQL Multitenant In-Memory
JSON
Big Data & IOT Internet Client-Server
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Spatial & Graph
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
What is Spatial Data?
• Business data that contains or describes location
– Geographic features (roads, rivers, parks, etc.)
– Assets (pipe lines, cables, transformers,
– Sales data (sales territory, customer registration, etc.)
– Street and postal address (customers, stores, factories, etc.)
• Anything connected to a physical location
• Almost every database contains some form of business data that can be leveraged using spatial technologies
• Location is a “universal key” linking entities which would otherwise be unrelated
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Open and Interoperable – Broad Partner Support
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Location Intelligence
• Reveals spatial relationships, trends, clusters and patterns undetectable with traditional BI.
• Detect links, patterns and trends depending on the spatial context.
BI provides the
WHO, WHAT & WHEN
Spatial provides the
WHERE
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
BI and Maps: A Natural Fit A picture worth a thousand words
• Maps are a natural choice for representing spatially-related data
• Help understand many phenomena and their relationships
Map courtesy StrangeMaps, Wikipedia (John Snow)
More bars (red) or grocery
stores (brown) per 10,000 people
Cholera incidents and possibly contaminated well
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Data acquisition in 3D
• Large volumes of point data acquired by sensors
– LIDAR (Light Detection and Ranging)
– Seismic sensors
• Millions of points to model a scene
• New data type to efficiently manage this data
– SDO_PC: can handle billions of points
• Triangulated Irregular Network: create surfaces from point sets
– SDO_TIN: scalable storage
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
View in Google Earth
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Survey & Land Registration Bureau Manama - Bahrain
• National 3D-enabled data model
―Full architectural model for Manama
• Underground Utilities
―Cables, water, gas & sewage pipes
• Under sea formation
―Used mainly for drilling
• City Planning
• Solar Potential Calculations
• Noise Distribution
• Heat Requirement
• Public Safety
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Advanced Analytics
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
•Automatically sifting through large amounts of data to find previously hidden patterns, discover valuable new insights and make predictions through:
– Identify most important factor (Attribute Importance)
–Predict customer behavior (Classification)
–Predict or estimate a value (Regression)
–Find profiles of targeted people or items (Decision Trees)
–Segment a population (Clustering)
–Find fraudulent or “rare events” (Anomaly Detection)
–Determine co-occurring items in a “baskets” (Associations)
What is Data Mining?
A1 A2 A3 A4 A5 A6 A7
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal/Restricted/Highly Restricted 15
A Real Fraud Example
• My credit card statement—Can you see the fraud?
• May 22 1:14 PM FOOD Monaco Café $127.38 • May 22 7:32 PM Drinks Mineral Water $28.00 • … • June 14 2:05 PM MISC Mobil Mart $75.00 • June 14 2:06 PM MISC Mobil Mart $75.00 • June 15 11:48 AM MISC Mobil Mart $75.00 • June 15 11:49 AM MISC Mobil Mart $75.00 • May 28 6:31 PM WINE Acton Shop $31.00 • May 29 8:39 PM FOOD Crossroads $128.14 • June 16 11:48 AM MISC Mobil Mart $75.00 • June 16 11:49 AM MISC Mobil Mart $75.00
Monaco? Gas Station?
Pairs of $75?
All same $75 amount?
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Oracle Advanced Analytics Use Cases
Oracle Confidential – Internal/Restricted/Highly Restricted 16
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Fast Data
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Business Drivers
• Applicable to applications with multiple geographies
• Run the business on a map
• Analyze historical data and predict future events
• Get real-time alerts
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
% believe their organization is losing
revenue as a result of not being
able to fully leverage information 67% 89%
executives who say drawing
intelligence from data is top
priority
executives who would grade
themselves C or lower in
preparedness
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
of executives say
too much critical
information is
delivered too late
Source: Aberdeen Group – January 2012, survey of 247 executives - Data Management for BI – Big Data, Bigger Insight, Superior Performance
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Obstacles to Faster Data – Latency Gap While Ensuring Accuracy, Efficiency, and Scale
Business event
Action Time
Bu
sin
ess
Val
ue
Data captured
Analysis completed Action taken
Source: Richard Hackethorn’s Component’s of Action Time
Fragmented event entities
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Futuristic Prediction on Fast Data Introducing Oracle Advanced Online Data Mining
Export model
Rebuild model Score events
Continuous
Event Flow
Predict when price of next event with > 80% using Oracle Data Mining model
Oracle Advanced
Analytics Option
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Managing Fast Data on Devices
• Embed Fast Data into smart devices and gateways
• Process data in real-time on the device
• Only deliver relevant information to data center thereby minimizing data congestion
• Streamline Fast data across entire M2M value chain - from device to data center
Embedded Event Processing (Internet of Things : IOT)
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Journey to Database as a Service
Silos
Complex
Standardized
Simple
Consolidated
Efficient
Cloud
Agile
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Co
nso
lidat
ion
Den
sity
Database Consolidation on Clouds Traditional consolidation methods
25
Share Servers Share Servers & OS Share Servers, OS, & Database
Virtual Machines Dedicated Databases Schema Consolidation
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Co
nso
lidat
ion
Den
sity
Oracle Multitenant High consolidation density, transparent to existing applications
26
Share Servers Share Servers & OS Share Servers, OS, & Database
Virtual Machines Dedicated Databases Pluggable Databases
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
AP OE GL
New Multitenant Architecture Memory and processes required at container level only
27
System Resources
GL OE AP
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
GL OE AP
Oracle Database Architecture More efficient utilization of system resources
28
System Resources
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Oracle Multitenant on SuperCluster T5-8 Consolidation Tests of PDBs vs. non-CDBs
0
500
1000
1500
2000
SIDBs PDBs
Memory Footprint per Database (not including Buffer Cache)
8x reduction in memory footprint
MB
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
SIDBs PDBs
Performance (Total Throughput) 252 PDBs vs. non-CDBs
80% higher aggregate
throughput
tps
0
50
100
150
200
250
300
SIDBs PDBs
Number of supported Databases (same Throughput per Database)
50% more databases
consolidated
databases
cores
0
50000
100000
150000
200000
250000
300000
350000
400000
SIDBs PDBs
Storage IOPS required to support 252 Databases
3x reduction in storage IOPS
0
50
100
150
200
SIDBs PDBs
Number of Cores required to support 252 Databases
64 fewer cores needed
IOPS
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Multitenancy implemented by the Database, not the Application
30
Multitenant for Software as a Service
Customer 1 Customer 2 Customer 3 Customer 4 Customer 5 Customer 6 Customer 7
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
✔ SILVER
BRONZE
GOLD
✔
✔
Multitenant for Database as a Service Different service levels for different requirements
31
RAC, Data Guard
RAC
Backups Test and Development
Production
Mission Critical
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Oracle Multitenant New Oracle Database architecture for the Cloud
Virtualize the database into PDBs
– Applications run unchanged
Lower OPEX
– Manage many as one • Patch, upgrade, backup, standby
– Granular control when appropriate
– Easy to provision, move, clone
Lower CAPEX
– More databases per server
– Shared memory and background processes
32
ERP CRM
DW
Complementary to VMs
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Development in the Cloud
Database as a Service on the Cloud
33
Development and deployment agility
Development On Premise
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Database as a Service Easy Migration to the Cloud
34
On Premises Traditional Deployment
or Private Cloud
Oracle Cloud
Deploy Anywhere
Same Architecture
Same Standards
Same Products
Transparently move workloads between on-premises and public cloud
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Heat Map
Smart Compression
Automated Tiering
In Database Archiving
Network Compression
Automatic Data Optimization Optimize data storage based on usage
Public 35
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
0101101110101010010100100100001000 1010101101001011010011100001010010
Archive Data
01110000101000101101110
10101001010010010000100
01010101101001011
010101001010010010001
Storage Strategy Usage based data compression and tiering
Hot Data
3X
Advanced Row Compression
Warm Data
1010
101011
101010
011010
111000
010100
010110
1110
101010
010100
100100
001000
101010
110100
1011
010011
100001
010010
010100
001001
000010
00
10
1010
110100
10
10X
Columnar Query Compression 1
00
00
101001
001010
010101
101110
00010
101010101
1101
01
0011
01
0111
00
0010
10
00
1011
0111
0
101010010
1001
00
1000
01
0001
01
0101
10
10
0101
1010
0
11100001010
0100
1010
0001
0010
0001
00
0101
0
1010101011
1010
1001
1010
1110
0001
01000
101
1011
15X
Columnar Archive Compression
01110101010010 10000100010101 01011100001010
10101010111010100110101110
00010100010110111010101001
01001001000010001010101101
00101101001110000101001001
01000010010000100010101011
10011010
10100101001001000010001
11
10
0101
001001
010010
101101
110110
10
101010101
1101
0100
1101
0111
0000
1011
10
10110
01
Public 36
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Automatic Data Optimization Simplifying the life cycle of data
• An in-memory heat map tracks block and segment access
– Data is periodically written to disk
– Information is accessible by views or stored procedures
• Uses can attach policies to tables to compress or tier data based on access to data
– Tables or Partitions can be moved between compression levels whilst data is still being accessed
Po licy 1
Public 37
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Automatic Data Optimization Add compression and tiering policies to tables
Oldest Data Most Recent Data
Po licy 1
Po licy 2
Compress Partitions with row compression if they haven’t been modified in 30 days
Compress Partitions with columnar compression if they haven’t been modified in 180 days
Public 38
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Automatic Data Optimization A heat map tracks the activity of segments and blocks
Oldest Data Most Recent Data
Po licy 1
Po licy 2
Public 39
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Automatic Data Optimization Policies are automatically applied to tables
Oldest Data Most Recent Data
Po licy 1
Po licy 2
Public 40
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Automatic Data Optimization Policies are automatically applied to tables
Oldest Data Most Recent Data
Po licy 1
Po licy 2
Public 41
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Automatic Data Optimization Policies are automatically applied to tables
Oldest Data Most Recent Data
Po licy 1
Po licy 2
Public 42
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Automatic Data Optimization Reduce storage footprint, read compressed data faster
Oldest Data Most Recent Data
Po licy 1
Po licy 2
Public 43
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Automatic Data Optimization Automatically tier data to lower cost storage
Oldest Data Most Recent Data
Po licy 1
Po licy 2
Po licy 3
If the tablespace is nearly full compress the oldest partition with archive compression and move it to Tier 2 Storage
Public 44
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Automatic Data Optimization Compress data over it’s lifecycle
Least Active Data Most Active Data
No Compression Advanced Row Compression
Hybrid Columnar Query
Compression
Hybrid Columnar Archive
Compression
3X Compression
OLTP
10X Compression
Reporting
15X Compression
Compliance
Public 45
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Maximum Availability Architecture
46
Protecting from common causes of planned and unplanned downtime
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Data Guard Far Sync
Data Guard Fast Sync
Global Data Services
Active Data Guard Reporting Enhancements
Flex ASM
Rolling Upgrade using Data Guard
Application Continuity
High Availability
GoldenGate Integrated Capture
Public 47
Oracle Database 12c enhancements
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Comprehensive Defense in Depth
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Soc. Sec. # 115-69-3428
DOB 11/06/71
PIN 5623
Policy enforced redaction of sensitive data
Redacting Sensitive Data Mask Application Data Dynamically
Call Center Operator
Payroll Processing
Call Centers
Decision Support Systems
Systems with PII, PHI, PCI data
Public 49
Oracle Database In-Memory Option
Powering the Real-Time
Enterprise Available July 2014
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Until Now Must Choose One Format and Suffer Tradeoffs
Optimizing Transaction and Query Performance Row Format Databases versus Column Format Databases
Row
Transactions run faster on row format
– Insert or query a sales order – Fast processing few rows, many columns
Column
Analytics run faster on column format
– Example : Report on sales totals by region – Fast accessing few columns, many rows
SALES
SALES
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Breakthrough: Dual Format In-Memory Database
• BOTH row and column in-memory formats for same table
• Simultaneously active and transactionally consistent
• Analytics & reporting use new in-memory Column format
• OLTP uses proven row format
52
Memory Memory
SALES SALES
Row Format
Column Format
“Now we can run time-sensitive analytical queries directly
against our OLTP database. This is something we wouldn’t
have dreamt of earlier.”
Arup Nanda Enterprise Architect
Starwood Hotels and Resorts
PeopleSoft In-Memory Financial Analyzer
1300x Faster
From 4.3 Hours to 11.5 Secs
• 290M Ledger Lines
• 250 Business Units
• 7 Step Analysis, Pivot, Drill
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Oracle Database In-Memory Customer Experience
55
Schneider Electric analysis of up to 2 billion General Ledger entries
0
20
40
60
80
100
2B 300K 30K
S
e
c
o
n
d
s
Analytics Query Results
Row Format Column Format
• Analytic queries 7-128x faster
• OLTP transactions 5-9x faster
• 76% storage savings
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Oracle In-Memory: Simple to Implement
1. Configure Memory Capacity
inmemory_size = XXX GB
2. Configure tables or partitions to be in memory
alter table | partition … inmemory;
3. Drop analytic indexes to speed up OLTP
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Oracle In-Memory Requires Zero Application Changes
Full Functionality - No restrictions on SQL
Easy to Implement - No migration of data
Fully Compatible - All existing applications run unchanged
Fully Multitenant - Oracle In-Memory is Cloud Ready
Uniquely Achieves All In-Memory Benefits With No Application Changes
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Customer Initiatives
Database as a Service
Cloud Big Data
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
The Power of Oracle SQL For a wide variety of ‘Big Data’ types
59
• Structured data
– Numeric, string, date, …
– Row and column formats
• Unstructured data – LOBs
– Text
– XML
– JSON
– Spatial
– Graph
– Multimedia
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Data Warehouses
Business Analytics
Evolution of Big Data Analytics in the Enterprise
60
Transactional Applications
Operational Reporting
Social Media
Internet of Things
73°
Big Data Platform
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Data Analytics Challenge Separate silos of information to analyze
61
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Data Analytics Challenge Separate data access interfaces
62
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
SQL is Critical
“….the complexity of dealing with a non-ACID data store in every part of our business logic would be too great, and there was simply no way our business could function without SQL queries.”
Google, VLDB 2013
“[Facebook] started in the Hadoop world. We are now bringing in relational to enhance that. ... [we] realized that using the wrong technology for certain kinds of problems can be difficult.”
Ken Rudin, Facebook, TDWI 2013
63
http://tdwi.org/articles/2013/05/06/facebooks-relational-platform.aspx https://www.linkedin.com/groups/Find-out-why-Google-decided-4434815.S.273792742
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Data Analytics Challenge No comprehensive SQL interface across Oracle, Hadoop and NoSQL
64
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
What customers want Rich, comprehensive SQL access to all enterprise data
65
NoSQL
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Oracle Big Data Management System Unify All Query
66
NoSQL
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Engineered for Clouds and Big Data
Big Data
Database as a Service
Cloud