safe harbor statement - ncb · oracle multitenant high consolidation density, transparent to...

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


The IOT Platform

Alaa Fahmy Core Technology Product Leader - ECEMEA

3


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


Spatial & Graph


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


Open and Interoperable – Broad Partner Support


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


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


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


View in Google Earth


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


Advanced Analytics


•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?


Oracle Advanced Analytics Use Cases

Oracle Confidential – Internal/Restricted/Highly Restricted 16


Fast Data


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


% 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


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


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


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


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)


Journey to Database as a Service

Silos

Complex

Standardized

Simple

Consolidated

Efficient

Cloud

Agile


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


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


AP OE GL

New Multitenant Architecture Memory and processes required at container level only

27

System Resources

GL OE AP


GL OE AP

Oracle Database Architecture More efficient utilization of system resources

28

System Resources


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


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


✔ 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


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


Development in the Cloud

Database as a Service on the Cloud

33

Development and deployment agility

Development On Premise


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


Heat Map

Smart Compression

Automated Tiering

In Database Archiving

Network Compression

Automatic Data Optimization Optimize data storage based on usage

Public 35


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


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


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


Automatic Data Optimization A heat map tracks the activity of segments and blocks


Po licy 1

Po licy 2

Public 39


Automatic Data Optimization Policies are automatically applied to tables


Po licy 1

Po licy 2

Public 40




Po licy 1

Po licy 2

Public 41




Po licy 1

Po licy 2

Public 42


Automatic Data Optimization Reduce storage footprint, read compressed data faster


Po licy 1

Po licy 2

Public 43


Automatic Data Optimization Automatically tier data to lower cost storage


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


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


Maximum Availability Architecture

46

Protecting from common causes of planned and unplanned downtime


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


Comprehensive Defense in Depth


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


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


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


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


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


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


Customer Initiatives

Database as a Service

Cloud Big Data


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


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


Data Analytics Challenge Separate silos of information to analyze

61


Data Analytics Challenge Separate data access interfaces

62


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


Data Analytics Challenge No comprehensive SQL interface across Oracle, Hadoop and NoSQL

64


What customers want Rich, comprehensive SQL access to all enterprise data

65

NoSQL


Oracle Big Data Management System Unify All Query

66

NoSQL


Engineered for Clouds and Big Data

Big Data

Database as a Service

Cloud

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