Post on 02-Jan-2016
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Oracle Database 12cSergey StetsenkoPrincipal Sales Consultant
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Major Database Focus Areas
ENGINEERED SYSTEMS
BIG DATA
SOCIAL
BLOG
SMARTMETER
101100101001001001101010101011100101010100100101
CLOUD COMPUTING
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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
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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
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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
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Consolidating Databases
v
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Database Consolidation Challenges
Reduce the number of separate
databases
Reduce IT costs by consolidating more
servers
Keep database applications isolated
and unchanged
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Oracle Database ArchitectureRequires memory, processes and database files
System Resources
ERPMemory
BackgroundProcesses
DatabaseFiles
CRMMemory
BackgroundProcesses
DatabaseFiles
DWMemory
BackgroundProcesses
DatabaseFiles
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Container Database
New Multitenant ArchitectureMemory and processes required at container level only
System Resources
ERPMemory
BackgroundProcesses
DatabaseFiles
CRMMemory
BackgroundProcesses
DatabaseFiles
DWMemory
BackgroundProcesses
DatabaseFiles
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New Multitenant ArchitectureMemory and processes required at container level only
System Resources
Container Database
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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
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Consolidating DatabasesStep2: Plug-in upgraded databases
Container Database
12.1
ERP
12.1
CRM
12.1
DW
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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
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Managing Shared ResourcesResource management for consolidated databases
Container DatabaseHigh Priority
Medium Priority
Low Priority
DW
CRM
ERP
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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
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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
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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
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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
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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
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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
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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
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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
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Lowering Storage Costsv
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Managing Storage Challenges
Compress data, without impacting
performance
Manage more data without incurring additional cost
Tier and compress data based on usage
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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
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Automatic Data OptimizationAdd compression and tiering policies to tables
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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
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Automatic Data OptimizationA heat map tracks the activity of segments and blocks
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Partitioned By Time
Oldest Data Most Recent Data
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Automatic Data OptimizationPolicies are automatically applied to tables
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Partitioned By Time
Oldest Data Most Recent Data
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Automatic Data OptimizationPolicies are automatically applied to tables
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Partitioned By Time
Oldest Data Most Recent Data
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Automatic Data OptimizationPolicies are automatically applied to tables
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Partitioned By Time
Oldest Data Most Recent Data
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Automatic Data OptimizationReduce storage footprint, read compressed data faster
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Partitioned By Time
Oldest Data Most Recent Data
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Automatic Data OptimizationAutomatically tier data to lower cost storage
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Partitioned By Time
Oldest Data Most Recent Data
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
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Redacting Datav
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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
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Redacting Data Challenges
Redact data in applications,
queries and reports
Secure sensitive personal
information
Avoid changing applications,
queries and reports
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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
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Data Warehousing & BIv
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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
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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);
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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
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Security & Compliance
Big Data
Consolidation
Data Optimization
Application Development
High Availability
In-Memory
Performance & Scalability
Data Warehousing
Plug into the Cloud
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