Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 1 Oracle Database 12c Sergey Stetsenko Principal Sales Consultant.

Copyright © 2013, Oracle and/or its affiliates. All rights reserved.1

Oracle Database 12cSergey StetsenkoPrincipal Sales Consultant


Major Database Focus Areas

ENGINEERED SYSTEMS

BIG DATA

SOCIAL

BLOG

SMARTMETER

101100101001001001101010101011100101010100100101

CLOUD COMPUTING


Private Database Cloud ArchitecturesUsing Oracle Database 11g

Dedicated Databases

share servers and OS

Virtual Machines

share servers

Schema Consolidation

share servers, OS and database

Increasing Consolidation


Private Database Cloud ArchitecturesUsing Oracle Database 12c

Dedicated Databases

share servers and OS

Virtual Machines

share servers

Pluggable Databases

share servers, OS and database

Increasing Consolidation


Isolation and multitenancy

Fast provisioning and cloning

Secure and highly available

No application changes

Manage many as one

Greater resource utilization

Performant and scalable

Lower IT costs

Consolidating Databases on Clouds Key requirements…

12.1

12.1

12.1


Consolidating Databases

v


Database Consolidation Challenges

Reduce the number of separate

databases

Reduce IT costs by consolidating more

servers

Keep database applications isolated

and unchanged


Oracle Database ArchitectureRequires memory, processes and database files

System Resources

ERPMemory

BackgroundProcesses

DatabaseFiles

CRMMemory

BackgroundProcesses

DatabaseFiles

DWMemory

BackgroundProcesses

DatabaseFiles


Container Database

New Multitenant ArchitectureMemory and processes required at container level only

System Resources

ERPMemory

BackgroundProcesses

DatabaseFiles

CRMMemory

BackgroundProcesses

DatabaseFiles

DWMemory

BackgroundProcesses

DatabaseFiles


New Multitenant ArchitectureMemory and processes required at container level only

System Resources

Container Database


Consolidating DatabasesStep1: Upgrade databases in-place

10.2

ERP

11.1

CRM

11.2

DW

12.1

ERP

12.1

CRM

12.1

DW

Container Database

Upgrade in PlaceContainer Database


Consolidating DatabasesStep2: Plug-in upgraded databases

Container Database

12.1

ERP

12.1

CRM

12.1

DW


OLTP benchmark comparison

Only 3GB of memory vs. 20GB memory used for 50 databases

Pluggable databases scaled to over 250 while separate database instances maxed at 50

Pluggable vs Separate DatabasesHighly Efficient: 6x Less H/W Resource, 5x more Scalable


Managing Shared ResourcesResource management for consolidated databases

Container DatabaseHigh Priority

Medium Priority

Low Priority

DW

CRM

ERP


Simplified PatchingApply changes once, all pluggable databases updated

Upgrade in-place

Container Database

DW12.1

CRM

12.1

ERP

12.1

12.X

12.X

12.X


Simplified UpgradesFlexible choice when patching & upgrading databases

Original Container Database (12.1) Upgraded Container Database (12.X)

DW12.1

CRM

12.1

ERP

12.1DW

12.X

CRM

12.X


Manage Many Databases as OneBackup databases as one, recover at pluggable database Level

DW12.1

CRM

12.1

ERP

12.1

One Backup

Point-in-time recoveryat pluggable database level

Container Database


Production Container Database

Manage Many Databases as OneOne standby database covers all pluggable databases

HCM12.1

DW12.1

CRM

12.1

ERP12.1

Standby Container Database

HCM12.1

DW12.1

CRM

12.1

ERP12.1


GOLD

SILVER

BRONZE

RAC, Data Guard, Daily Incrementals

Data Guard, Daily Incrementals

Weekly Full Backups

Managing Database Service Level TiersChange tiers as databases become more mission critical


Production Container Database Development Container Database

Creating Databases for Test and DevelopmentFast, flexible copy and snapshot of pluggable databases

DW12.1

CRM

12.1

ERP

12.1

ERP Dev

CopyERP Dev

CopyERP Dev

Copy


Multitenant Architecture for SaaSEach customer’s data in private pluggable database

Container Database

CUST 7

CUST 6

CUST 5

CUST 4

CUST 3

CUST 2

CUST 1


Isolation and multitenancy

Fast provisioning and cloning

Secure and highly available

No application changes

Manage many as one

Greater resource utilization

Performant and scalable

Lower IT costs

New Multitenant ArchitectureIdeal for consolidating databases on Clouds

12.1

12.1

12.1


Lowering Storage Costsv


Managing Storage Challenges

Compress data, without impacting

performance

Manage more data without incurring additional cost

Tier and compress data based on usage


Automatic Data Optimization

An in memory heat map tracks access to segments and blocks

– 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

Simplifying the life cycle of data


Automatic Data OptimizationAdd compression and tiering policies to tables

Copyright © 2012, Oracle and/or its affiliates. All rights reserved. Confidential – Oracle Restricted26

Partitioned By Time

Oldest Data Most Recent Data

Compress Partitions with Row Compression if they haven’t been modified in 30 days

Compress Partitions with Hybrid Columnar Compression if they haven’t been modified in 180 days


Automatic Data OptimizationA heat map tracks the activity of segments and blocks


Partitioned By Time



Automatic Data OptimizationPolicies are automatically applied to tables


Partitioned By Time





Partitioned By Time





Partitioned By Time



Automatic Data OptimizationReduce storage footprint, read compressed data faster


Partitioned By Time



Automatic Data OptimizationAutomatically tier data to lower cost storage


Partitioned By Time


Tier 1 Storage

Tier 2 Storage

If the tablespace is nearly full compress the oldest partition with Archive Hybrid Columar Compression and move it to Tier 2 Storage


Redacting Datav


Oracle Database Security SolutionsDefense-in-Depth for Maximum Security

Activity Monitoring

Database Firewall

Auditing and Reporting

DETECTIVE

Redaction and Masking

Multi-Factor Authorization

Encryption

PREVENTIVE ADMINISTRATIVE

Data Discovery and Classification

Vulnerability Scanning

Database Lifecycle Management


Redacting Data Challenges

Redact data in applications,

queries and reports

Secure sensitive personal

information

Avoid changing applications,

queries and reports


Soc. Sec. #115-69-3428

DOB11/06/71

PIN5623

Policy enforced redaction of sensitive data

Redacting Sensitive DataMask Application Data Dynamically

Call Center Operator

Payroll Processing


Data Warehousing & BIv


Pattern MatchingSimplified Analysis of Big Data

Select * from Employees MATCH_RECOGNIZE (…PATTERN(X+ Z{2})…)

Scalable discovery of business event sequences

– Clickstream logs: sessionization, search behaviour

– Financial transactions: fraud detection, double bottom (“W”) stock analysis

– Telco: dropped calls

– Medical sensors: automated medical observations and detections

Patterns are defined using regular expressions

Asc

endi

ng O

rder


Streaming Result Sets

32K Varchar

Identity Data Type

“On The Fly” Translations

Callout Framework

Fetch First (Top “N”) Queries

Application Development and MigrationsMaking Migrations to Oracle Easy

TSQL:select top 10 * from table1

PL/SQL:OPEN v_cursor FOR SELECT * FROM table1 FETCH FIRST 10 ROWS ONLY; DBMS_SQL.RETURN_RESULT(v_cursor);


Intercepted SQL

jdbc

jdbc

Oracle Migration : Sybase to OracleUse SQL Translation Framework to intercept SQL and translate it

HR

12.1

TEST

12.1

DEV

12.1

Container Database

SQL Translation Framework

Alternative SQL SQL Developer

Sybase Application


Security & Compliance

Big Data

Consolidation

Data Optimization

Application Development

High Availability

In-Memory

Performance & Scalability

Data Warehousing

Plug into the Cloud


Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 1 Oracle Database 12c Sergey Stetsenko Principal Sales Consultant.

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