SlideShare a Scribd company logo
1 of 54
© 2013 AgreeYa Solutions. All rights reserved.1
© 2013, AgreeYa Solutions. All rights reserved.
www.agreeya.com
Netezza Overview
Netezza Architecture
Netezza Performance Tuning
Netezza Admin
April 10, 2013 – BHAWANI NANDAN PRASAD – BI Practice Head
SMP – IIM Calcutta, MBA – Stratford University USA, B.E. (IT)
© 2013 AgreeYa Solutions. All rights reserved.2
Agenda
• Netezza Architecture
• Netezza Connectivity
• NZSQL
• Data Types in Netezza
• Metadata Tables
• Types of Joins in Netezza
• Data Loading and Unloading in Netezza
• Data Distribution in Netezza
• Transactions in Netezza
• GROOM/Reclaim Process in Netezza
• Zone Maps in Netezza
• GENERATE STATISTICS in Netezza
© 2013 AgreeYa Solutions. All rights reserved.3
© 2013, AgreeYa Solutions. All rights reserved.
www.agreeya.com
Netezza Architecture
© 2013 AgreeYa Solutions. All rights reserved.4
Netezza Architecture
© 2013 AgreeYa Solutions. All rights reserved.5
Data Stream Processing in Netezza
© 2013 AgreeYa Solutions. All rights reserved.6
Netezza Connectivity
© 2013 AgreeYa Solutions. All rights reserved.7
NZSQL
• Utility to interact with Netezza database
• Useful to writing multi-liner queries, executing those for analysis or
reporting purpose
• Setting environment is a pre-requisite before starting on nzsql
• Logging into nzsql opens the pg.log file and start capturing all
activities performed by user on corresponding DB
© 2013 AgreeYa Solutions. All rights reserved.8
Data Types in Netezza
DATATYPE DESCRIPTION SIZE
BOOL boolean, 'true'/'false' 1
BPCHAR char(length), blank-padded string, fixed storage length VAR
CHAR single character 1
DATE ANSI SQL date 4
FLOAT4 single-precision floating point number, 4-byte storage 4
FLOAT8 double-precision floating point number, 8-byte storage 8
INT1 -128 to 127, 1-byte storage 1
INT2 -32 thousand to 32 thousand, 2-byte storage 2
INT4 -2 billion to 2 billion integer, 4-byte storage 4
INT8 ~18 digit integer, 8-byte storage 8
INTERVAL @ <number> <units>, time interval 12
NCHAR nchar VAR
NUMERIC numeric(precision, decimal), arbitrary precision number 19
NVARCHAR nvarchar VAR
TIME hh:mm:ss, ANSI SQL time 8
TIMESTAMP date and time 8
TIMETZ hh:mm:ss, ANSI SQL time 12
VARCHAR varchar(length), non-blank-padded string, variable storage length VAR
© 2013 AgreeYa Solutions. All rights reserved.9
Metadata Tables in Netezza
• Like any other database, Netezza also provides metadata tables and
views which provides information about objects
• Some of the frequently required MD tables are:
System Table Name Usage
_V_OBJECTS Used to display information related to different objects like tables, views,
external tables, synonyms and more
_V_TABLES Used to display information related to different tables present in Netezza
_V_VIEW Used to display information related to different views present in Netezza
_V_RELATION_COLUMN Used to display information related to different columns present in Netezza
tables
© 2013 AgreeYa Solutions. All rights reserved.10
Types of Joins in Netezza
• Netezza internally processes joins in following order:
– Hash Join (in memory)
– Hash Join (in disk)
– Sort Merge Join
– Nested Loop Join
– Cross Join
• Netezza has three main types of joins available:
– Co-located Join
– Re-distribution of data
– Broadcasting of data
© 2013 AgreeYa Solutions. All rights reserved.11
Data Loading and Unloading in Netezza
• NZLOAD (only loading)
• EXTERNAL TABLES (both loading and unloading)
• CTAS (CREATE TABLE AS) (both loading and unloading)
• Nzsql with –o option (only unloading)
© 2013 AgreeYa Solutions. All rights reserved.12
Data Distribution in Netezza
• Key factor in shooting performance to great extent
• Backbone of MPP architecture
• Can be leverage using DISTRIBUTE ON clause after CRAETE TABLE
statement
• Of three types:
– DISTRIBUTE ON (column name);
– DISTRIBUTE ON RANDOM;
– No DISTRIBUTE specification
– Very useful while loading data into tables and fetching data from table
© 2013 AgreeYa Solutions. All rights reserved.13
Selecting a distribution key
• Columns with many distinct values
• Column or columns based on selection set
• As few columns as possible
• Data distributed on same key
• DO NOT use Boolean keys
• Checking distribution of data in table
© 2013 AgreeYa Solutions. All rights reserved.14
Collocated Join
© 2013 AgreeYa Solutions. All rights reserved.15
Single Redistribute
© 2013 AgreeYa Solutions. All rights reserved.16
Double Redistribute
© 2013 AgreeYa Solutions. All rights reserved.17
Broadcast
© 2013 AgreeYa Solutions. All rights reserved.18
Transactions in Netezza
• Three basic columns to carry out transaction in Netezza
– Createxid
– Deletexid
– Rowid
• Values in these columns keep on changing with every transaction
• These are hidden columns with every table in Netezza
• Also used to track deleted records in many cases
© 2013 AgreeYa Solutions. All rights reserved.19
Transactions in Netezza contd..
© 2013 AgreeYa Solutions. All rights reserved.20
Aborted Transaction in Netezza
© 2013 AgreeYa Solutions. All rights reserved.21
Locking, Concurrency and Isolation
• Netezza implements serializable transaction isolation for highest level
of consistency
• Multi-versioning and Serialization dependency checking
• User cannot explicitly lock a table in Netezza
• UPDATE clause works differently in Netezza
© 2013 AgreeYa Solutions. All rights reserved.22
GROOM/Reclaim in Netezza
• Logically deleted records reside in memory in Netezza in following
cases:
– INSERT
– UPDATE
– Failed INSERT or aborted nzload operation
– Failed UPDATE operation
• Logically deleted records in Netezza causes:
– Occupancy of extra disk space
– Requires extra time for full table scan
© 2013 AgreeYa Solutions. All rights reserved.23
GROOM/Reclaim contd..
• GROOM/ RECLAIM process recovers this unused disk space in
Netezza
• GROOM command support operations for:
– Single table
– All tables in one database
– All tables in all database
• Benefits of GROOM:
– Permits shared access to target table
– Can be interrupted without leaving target table locked
– Refreshed materialized views created on base table
• Syntax:
© 2013 AgreeYa Solutions. All rights reserved.24
Zone Maps in Netezza
• Zone Maps are similar to indexes in any other DB
• Created on integer, date and timestamp fields
• Created and refreshed automatically when:
• GENERATE STATSTICS
• NZLOAD
• INSERT or UPDATE
• GROOM Operation
© 2013 AgreeYa Solutions. All rights reserved.25
GENERATE STATISTICS in Netezza
• Netezza optimizer relies on GENERATE STATISTICS to gather
statistics about tables
• GENERATE STATISTICS collects statistics about each table
columns:
– Minimum and maximum values on character data
– Maximum and average length on varchar
– NULL Counts
– Updates the system catalog
• GENERATE STATISTICS can be collected at three levels:
– Database Level
– Table level
– Column Level
• Can also be collected using Nzadmin tool
© 2013 AgreeYa Solutions. All rights reserved.26
GENERATE STATISTICS contd..
• Netezza system generates two basic statistics, table row count and
min-max values for character columns while doing:
– INSERT
– UPDATE
– CTAS (GENERATE STATISTICS is automatically created is row count >=
10k)
– Nzload
– GROOM
– TRUNCATE TABLE
• It is important to generate statistics for:
© 2013 AgreeYa Solutions. All rights reserved.27
SPU Failover Activity
 Disk timing : It shows the SPU showing the slow
performance
 Step 1) Pause the system
• nzsql>> nzsystem pause
 Step 2) Confirm that the system is paused
• nzsql>> nzstate
 Step 3) Failover the SPU
• nzsql>> nzspu failover -id <SPU ID>
 Step 4) Resume the system
• nzsql>> nzsystem resume
© 2013 AgreeYa Solutions. All rights reserved.28
Genstats Command
 To generate statistics on any database table(s) for
which the statistics
• are not currently 100% "up-to-date".
 The optimizer uses statistics to guide its decisions on
how best to execute a query. The more reliable and
up-to-date the statistics are,more accurate
optimizer's decisions are likely to be.
© 2013 AgreeYa Solutions. All rights reserved.29
Backup & Restore
 Types of Back up :
 Full Back up
 Differential backup
 Incremental Differential backup
 Cumulative Differential backup
 Elaborative Example
•
© 2013 AgreeYa Solutions. All rights reserved.30
Back up Command
 Backup command / scripts is used for backing up tables from NPS.
 Backup command / nz_backup script must be run locally (on the NPS host being backed
up).
 These command/scripts processes a single table, multiple tables, or an entire database.
 The data format that is used can be either
 ascii -- which is very portable.
 binary-- which is Netezza's compressed/internal format, which is
much faster, and results in significantly smaller backup sets.
 gzip -- ascii, which is gzip'ed on the NPS host.
 The data is written to (or read from) disk files or named pipes.
 If pipes are used, another application is used to produce the data.
 These scripts just concern themselves with the DATA itself. When backing up
• a table, the DDL is not included.
© 2013 AgreeYa Solutions. All rights reserved.31
Back up Command Examples
 Full backup:
• /nz/kit/bin/nzbackup -db CIDB_PRD -dir
/back_folder
• nohup nzbackup -db CIDB_PRD -u admin -dir
/back_folder
 Differential backup:
 /nz/kit/bin/nzbackup -db CIDB_PRD -u admin -dir
/back_folder -differential -v
• nohup nzbackup -db CIDB_PRD -u admin -dir
/back_folder -schema-only
© 2013 AgreeYa Solutions. All rights reserved.32
Restore Command
 Restore command / scripts is used to restore tables to NPS.
 Restore command / nz_restore script must be run locally (on the NPS host being restored ).
 These command/scripts processes a single table, multiple tables, or an entire database.
 The data format that is used can be either
 ascii -- which is very portable.
 binary-- which is Netezza's compressed/internal format, which is
much faster, and results in significantly smaller backup sets.
 gzip -- ascii, which is gzip'ed on the NPS host.
 The data is written to (or read from) disk files or named pipes.
 If pipes are used, another application is used to produce the data.
 These scripts just concern themselves with the DATA itself. When backing up
• a table, the DDL is not included.
© 2013 AgreeYa Solutions. All rights reserved.33
Restore Command
 Syntax : nzrestore [-db database] [-dir directory]
• [-connector name] [-connectorArgs] [-schema only]
» [-users] [-v] [-rev] [-h] [-increment] [-mode]
• [-backupset ID] [-lockdb]
 Here,
 -dir specifies the backup root directory when using the file system connector
 -connector specifies the connector type either File System, Veritas or Tivoli
• NOTE : If -connector is omitted defaults to File System connector
 -connectorArgs specifies:
• - DATASTORE_SERVER and DATASTORE_POLICY when using the Veritas
connector
• - The TSM password when using Tivoli connector
• - may optionally be specified as environment variables
 -If incremental is omitted, defaults to full backup
 -mode specifies REST /NEXT mode .
 -lockdb specifies locking of database during restore [ TRUE/FALSE]
© 2013 AgreeYa Solutions. All rights reserved.34
Tape Back up Command
 Similar to Backup Command.
 Syntax is also similar
 Command only Differs in the destination location which is “Tape” instead of
any file location as that of normal backup .
 Example :
 nzbackup -v -db EDW_STANDBY -dir
/migration/TF12_EDW_STANDBY_Tape_Backup/tape1
/migration/TF12_EDW_STANDBY_Tape_Backup/tape2
/migration/TF12_EDW_STANDBY_Tape_Backup/tape3
/migration/TF12_EDW_STANDBY_Tape_Backup/tape4 -streams 4
© 2013 AgreeYa Solutions. All rights reserved.35
Netezza Performance Server
© 2013 AgreeYa Solutions. All rights reserved.36
Defaults in Netezza
 Default Users
 nz (Linux OS user )
 admin ( NPS database super-user with full access to all
system functions and objects )
 root ( Linux root user )
 System Defaults
 system database
 public group
 5480 – ODBC port
 Note :
 By default user created is added to the public group
 User can’t be deleted from public group
 group, user & database share a common namespace.so group name, user name and
database names must be unique.
© 2013 AgreeYa Solutions. All rights reserved.37
Managing Users
 By default user have access to only system views allowing then to retrieve a list of used
database objects.
 Sql for Creating User :
 CREATE USER user_name WITH PASWORD ‘string’ [options]
 Sql for altering User credentials/privileges
 ALTER USER user_name WITH [options]
 Sql for deleting User :
 DROP USER user_name
 Note : Here options can be :
 Row limit, Group name, Validity, Session Time out, Query Time out, Default priority, Maximum
priority, Resource group
 Nzsql command to list user
• SYSTEM(ADMIN) => du
 Nzsql command to list user’s permission
• SYSTEM(ADMIN) => dpu
© 2013 AgreeYa Solutions. All rights reserved.38
Managing Groups
 By default group created is public group.
 By default user is added in public group.
 Sql for Creating Group :
 CREATE GROUP group_name WITH PASWORD ‘string’ [options]
 Sql for altering group credentials/privileges
 ALTER GROUP group_name [ADD|OWNER|RENAME|WITH]
 Sql for deleting User :
 DROP USER use_name
 Note : Here options can be :
 Row limit, Session Time out, Query Time out, Default priority, Maximum priority, Resource limit, user
names
 Nzsql command to list user
• SYSTEM(ADMIN) => dg
 Nzsql command to list user’s permission
• SYSTEM(ADMIN) => dpg
© 2013 AgreeYa Solutions. All rights reserved.39
Permissions
 Types of Permission :
 Object Permissions [ 11 nos. ]:
• List, Select
• Insert, Delete, Update
• Alter, Drop, Truncate
• Lock, Abort, Load, Genstat
 Admin Permissions [ 13 nos. ]:
• Database, Temporary Table, External Table , System Table, view
• User, Group
• Create, Backup, Restore, Reclaim
• Hardware, system
 Scope of Permission :
 Applicable only to Object Permissions :
 Two classes
• Local Scope : Applicable when logged into particular database
• Global Scope : Applicable when logged into system database
 By default Admin permissions are Global in Nature
© 2013 AgreeYa Solutions. All rights reserved.40
Object Permissions
 Object Permissions Granted in the system database are inherited by all other databases
• i.e. they have global scope
 Object Permissions Granted within database are local to the databases
• i.e. they have local scope
 Object Permissions are additive in nature
• i.e. Effectively all permission the of an object
• = User Permissions + Group Permissions + Public Permission
 Sql for Granting Object Permission :
 GRANT object_permission On object TO {PUBLIC | GROUP group_name | user_name } [ WITH GRANT
OPTION ]
 Sql for Revoking Object Permission :
 REVOKE object_permission On object TO {PUBLIC | GROUP group_name | user_name }
© 2013 AgreeYa Solutions. All rights reserved.41
Admin Permissions
 Admin Permissions are Global in scope
 Sql for Granting Admin Permission :
 GRANT admin_permission TO {PUBLIC | GROUP group_name | user_name } [
WITH GRANT OPTION ]
 Sql for Revoking Admin Permission :
 REVOKE admin_permission TO {PUBLIC | GROUP group_name | user_name }
 Nzsql command to list user’s permission
 SYSTEM(ADMIN) => dpu
 Nzsql command to list group’s permission
 SYSTEM(ADMIN) => dpg
© 2013 AgreeYa Solutions. All rights reserved.42
Listing All Permissions to User/Group
© 2013 AgreeYa Solutions. All rights reserved.43
Viewing The Distribution & Skew
 In CLI
 on linux prompt
• $ nz_skew utility ( on Linux Prompt )
 on nzsql prompt
• nzsql => SELECT datasliceid, COUNT(datasliceid) AS "ROWS"
FROM MB_STU_PRE
• GROUP BY datasliceid
• ORDER BY "ROWS";
 In GUI
– In nzAdmin –> Tools –> Table Skews
 NOTE :
 For changing the distribution key Create Table table_name AS ( select clause ) is used with Distribution
Key
 If distribution clause is not specified in the CTAS, parent table distribution key column is used as
distribution by default.
 The default threshold to display skew of table 100 MB.
© 2013 AgreeYa Solutions. All rights reserved.44
Log Files in Netezza
 All the log file in Netezza are in the directory:
 /nz/kit/log/
 Various log created are
 Alcapp, alcloader, waitForAlcapp
 backupsvr, bnrmgr, restoresvr
 bootsvr, dbos
 Clientmgr , eventmgr, sessionmgr, sysmgr
 fcommrtx
 gencErrors, hostStatsGen
 Loadmgr , nzloadTmpLogs
 Plans , planshist, postgres
 sendMail
 ssgdba
 startupsvr, statsSvr
© 2013 AgreeYa Solutions. All rights reserved.45
Priority
 Priority are
 Job Priority
 Session Priority
 Priority values are defined for a user, a group, or as the system default
 Sys determines value of priority to use when the user connects to the host
– and executes SQL commands
 Two more are there- SYSTEM CRITICAL (highest) and SYSTEM BACKGOUND (lowest), which are not visible to
user.
 The possible priorities are critical, high, normal, low, or none.
 The default priority for groups, and the system is none.
 If priorities are not set, user sessions run at normal priority.
© 2013 AgreeYa Solutions. All rights reserved.46
Priority (Contd…)
 The syntax to set system priority is:
 SET SYSTEM DEFAULT
– [SESSIONTIMEOUT | ROWSETLIMIT | QUERYTIMEOUT ] TO [number |
UNLIMITED ]
– [DEFPRIORITY | MAXPRIORITY ] to [CRITICAL | HIGH | NORMAL | LOW |
NONE]
 The syntax to create group and set default priority is :
 SHOW SYSTEM DEFAULT MAXPRIORITY;
 SHOW SYSTEM DEFAULT DEFPRIORITY;
 The syntax to create group and set default priority is :
 CREATE GROUP group_name WITH DEFPRIORITY TO HIGH;
 The syntax to create group and set default priority is :
 CREATE USER user_name WITH DEFPRIORITY TO CRITICAL;
© 2013 AgreeYa Solutions. All rights reserved.47
Priority (Contd…)
 The syntax to change the priority of a session
 ALTER SESSION [<session_id>] SET PRIORITY TO
<priority> ;
 Example :
– nzsql=> ALTER SESSION 21664 SET PRIORITY TO HIGH;
 The syntax to change priority of a session using nzsession
 nzsession priority -high -u nz -pw password -id 21664;
© 2013 AgreeYa Solutions. All rights reserved.48
Migration
 Based on the activities migration activities are classified as :
 Data Migration
 Environment migration :
 Code Migration
© 2013 AgreeYa Solutions. All rights reserved.49
Data Migration
 Data Migration : It is done on two different ways :
 nz_migrate utility –
 Syntax : nz_migrate -shost <name/IP> -thost <name/IP>
» -sdb <dbname> -tdb <dbname>
» [optional args]
 This script must be invoked from the 'source' machine.
 Optionally, this script can automatically create the target database and objects
via the options
• -CreateTargetTable
• -CreateTargetDatabase
 Through means of external table –
 Database/table is converted to external table from the source
 External table is converted back to database/table to the target
© 2013 AgreeYa Solutions. All rights reserved.50
Environment Migration
 Environment Migration :
 Objects created by developer in personal database EDW_UT is moved to
development EDW_SIT database
 All the Objects present in development database EDW_SIT is moved to
Production database EDW_PROD
 This is done through customized script
“export/home/nz/psm/scripts/dlc/object.in.work.prod.bash “
 NOTE :
– There is also a script
“export/home/nz/psm/scripts/dlc/object.in.work.create.bash”
which
– promotes objects from personal database EDW_UT to EDW_SIT
for testing purpose
© 2013 AgreeYa Solutions. All rights reserved.51
Code Migration
 Code migration :
 This is a manually method where ddl of the objects like
table,view etc are obtained using nz_ddl_table, nz_ddl_view
and through this ddl’s objects are created.
 The privileges (ACL) of the object is obtained through
nz_get_acl utility, is used to reproduce the ACL on the newly
created object .
 This is done through customized script .
© 2013 AgreeYa Solutions. All rights reserved.52
Events and Alerts
By default there are total 40 events for which alert is
being raised. They are :1. CPUcoresOK_em_NzCS
2. CPUcoresReduced_em_NzCS
3. HostNoLongerOnline
4. HostNotOnline
5. MemFlt_rc_NzCS
6. NzDAC_QDR_fault_em_NzCS
7. Regen_em_NzCS
8. RunAwayQuery_TF12
9. RunAway_rc_NzCS
10. RunAway_rc_monitor
11. SystemOnline
12. coreRequest_em_NzCS
13. dFPGA_em_NzCS
14. dFPGA_em_NzCS_r
15. diskFull_8x_em_NzCS
16. diskFull_90_em_NzCS
17. diskFull_95_em_NzCS
18. histCapture_em_NzCS
19. histLoad_em_NzCS
20. hwFlt_FanOrPwr_em_NzCS
© 2013 AgreeYa Solutions. All rights reserved.53
Monitoring & Gathering Scripts
 Monitoring on the system is done through customized/ system scripts which
executes daily in following ways :
 Hourly performance data of each server is generated and mailed
 Consolidated reports on various daily dba activites ( like backup, restore,
genstat, reclaim etc..) for all the servers is generated and mailed
 Complete health report is generated by nz_health and mailed
 Report of all the log activities is generated by and mailed
 SPU performance is checked by disk_timing script on every 8 hrs.
© 2013 AgreeYa Solutions. All rights reserved.54
www.agreeya.com
54
Thank You
BHAWANI NANDAN PRASAD
BI & Analytics Practice Head
Bhawani.prasad@agreeya.net
+91 9717570222

More Related Content

What's hot

Using R on Netezza
Using R on NetezzaUsing R on Netezza
Using R on NetezzaAjay Ohri
 
Managing user Online Training in IBM Netezza DBA Development by www.etraining...
Managing user Online Training in IBM Netezza DBA Development by www.etraining...Managing user Online Training in IBM Netezza DBA Development by www.etraining...
Managing user Online Training in IBM Netezza DBA Development by www.etraining...Ravikumar Nandigam
 
Oracle to Netezza Migration Casestudy
Oracle to Netezza Migration CasestudyOracle to Netezza Migration Casestudy
Oracle to Netezza Migration CasestudyAsis Mohanty
 
Netezza vs teradata
Netezza vs teradataNetezza vs teradata
Netezza vs teradataAsis Mohanty
 
Using Netezza Query Plan to Improve Performace
Using Netezza Query Plan to Improve PerformaceUsing Netezza Query Plan to Improve Performace
Using Netezza Query Plan to Improve PerformaceBiju Nair
 
NENUG Apr14 Talk - data modeling for netezza
NENUG Apr14 Talk - data modeling for netezzaNENUG Apr14 Talk - data modeling for netezza
NENUG Apr14 Talk - data modeling for netezzaBiju Nair
 
Netezza Online Training by www.etraining.guru in India
Netezza Online Training by www.etraining.guru in IndiaNetezza Online Training by www.etraining.guru in India
Netezza Online Training by www.etraining.guru in IndiaRavikumar Nandigam
 
Netezza fundamentals-for-developers
Netezza fundamentals-for-developersNetezza fundamentals-for-developers
Netezza fundamentals-for-developersTariq H. Khan
 
Netezza Architecture and Administration
Netezza Architecture and AdministrationNetezza Architecture and Administration
Netezza Architecture and AdministrationBraja Krishna Das
 
The IBM Netezza Data Warehouse Appliance
The IBM Netezza Data Warehouse ApplianceThe IBM Netezza Data Warehouse Appliance
The IBM Netezza Data Warehouse ApplianceIBM Sverige
 
The IBM Netezza datawarehouse appliance
The IBM Netezza datawarehouse applianceThe IBM Netezza datawarehouse appliance
The IBM Netezza datawarehouse applianceIBM Danmark
 
Ibm pure data system for analytics n200x
Ibm pure data system for analytics n200xIbm pure data system for analytics n200x
Ibm pure data system for analytics n200xIBM Sverige
 
White Paper: Backup and Recovery of the EMC Greenplum Data Computing Applian...
 White Paper: Backup and Recovery of the EMC Greenplum Data Computing Applian... White Paper: Backup and Recovery of the EMC Greenplum Data Computing Applian...
White Paper: Backup and Recovery of the EMC Greenplum Data Computing Applian...EMC
 
Netezza vs Teradata vs Exadata
Netezza vs Teradata vs ExadataNetezza vs Teradata vs Exadata
Netezza vs Teradata vs ExadataAsis Mohanty
 

What's hot (20)

Course content (netezza dba)
Course content (netezza dba)Course content (netezza dba)
Course content (netezza dba)
 
Using R on Netezza
Using R on NetezzaUsing R on Netezza
Using R on Netezza
 
Managing user Online Training in IBM Netezza DBA Development by www.etraining...
Managing user Online Training in IBM Netezza DBA Development by www.etraining...Managing user Online Training in IBM Netezza DBA Development by www.etraining...
Managing user Online Training in IBM Netezza DBA Development by www.etraining...
 
Oracle to Netezza Migration Casestudy
Oracle to Netezza Migration CasestudyOracle to Netezza Migration Casestudy
Oracle to Netezza Migration Casestudy
 
Netezza vs teradata
Netezza vs teradataNetezza vs teradata
Netezza vs teradata
 
Using Netezza Query Plan to Improve Performace
Using Netezza Query Plan to Improve PerformaceUsing Netezza Query Plan to Improve Performace
Using Netezza Query Plan to Improve Performace
 
NENUG Apr14 Talk - data modeling for netezza
NENUG Apr14 Talk - data modeling for netezzaNENUG Apr14 Talk - data modeling for netezza
NENUG Apr14 Talk - data modeling for netezza
 
Netezza Online Training by www.etraining.guru in India
Netezza Online Training by www.etraining.guru in IndiaNetezza Online Training by www.etraining.guru in India
Netezza Online Training by www.etraining.guru in India
 
Netezza fundamentals-for-developers
Netezza fundamentals-for-developersNetezza fundamentals-for-developers
Netezza fundamentals-for-developers
 
netezza-pdf
netezza-pdfnetezza-pdf
netezza-pdf
 
Netezza pure data
Netezza pure dataNetezza pure data
Netezza pure data
 
Netezza Architecture and Administration
Netezza Architecture and AdministrationNetezza Architecture and Administration
Netezza Architecture and Administration
 
The IBM Netezza Data Warehouse Appliance
The IBM Netezza Data Warehouse ApplianceThe IBM Netezza Data Warehouse Appliance
The IBM Netezza Data Warehouse Appliance
 
The IBM Netezza datawarehouse appliance
The IBM Netezza datawarehouse applianceThe IBM Netezza datawarehouse appliance
The IBM Netezza datawarehouse appliance
 
Fast Analytics
Fast Analytics Fast Analytics
Fast Analytics
 
Ibm pure data system for analytics n200x
Ibm pure data system for analytics n200xIbm pure data system for analytics n200x
Ibm pure data system for analytics n200x
 
White Paper: Backup and Recovery of the EMC Greenplum Data Computing Applian...
 White Paper: Backup and Recovery of the EMC Greenplum Data Computing Applian... White Paper: Backup and Recovery of the EMC Greenplum Data Computing Applian...
White Paper: Backup and Recovery of the EMC Greenplum Data Computing Applian...
 
Netezza vs Teradata vs Exadata
Netezza vs Teradata vs ExadataNetezza vs Teradata vs Exadata
Netezza vs Teradata vs Exadata
 
Teradata training
Teradata trainingTeradata training
Teradata training
 
Greenplum Architecture
Greenplum ArchitectureGreenplum Architecture
Greenplum Architecture
 

Similar to Bigdata netezza-ppt-apr2013-bhawani nandan prasad

GLOC 2014 NEOOUG - Oracle Database 12c New Features
GLOC 2014 NEOOUG - Oracle Database 12c New FeaturesGLOC 2014 NEOOUG - Oracle Database 12c New Features
GLOC 2014 NEOOUG - Oracle Database 12c New FeaturesBiju Thomas
 
ORACLE 12C-New-Features
ORACLE 12C-New-FeaturesORACLE 12C-New-Features
ORACLE 12C-New-FeaturesNavneet Upneja
 
Oracle 12 c new-features
Oracle 12 c new-featuresOracle 12 c new-features
Oracle 12 c new-featuresNavneet Upneja
 
Impala tech-talk by Dimitris Tsirogiannis
Impala tech-talk by Dimitris TsirogiannisImpala tech-talk by Dimitris Tsirogiannis
Impala tech-talk by Dimitris TsirogiannisFelicia Haggarty
 
Powering GIS Application with PostgreSQL and Postgres Plus
Powering GIS Application with PostgreSQL and Postgres Plus Powering GIS Application with PostgreSQL and Postgres Plus
Powering GIS Application with PostgreSQL and Postgres Plus Ashnikbiz
 
Technical Introduction to PostgreSQL and PPAS
Technical Introduction to PostgreSQL and PPASTechnical Introduction to PostgreSQL and PPAS
Technical Introduction to PostgreSQL and PPASAshnikbiz
 
Informix partitioning interval_rolling_window_table
Informix partitioning interval_rolling_window_tableInformix partitioning interval_rolling_window_table
Informix partitioning interval_rolling_window_tableKeshav Murthy
 
PHP Detroit -- MySQL 8 A New Beginning (updated presentation)
PHP Detroit -- MySQL 8 A New Beginning (updated presentation)PHP Detroit -- MySQL 8 A New Beginning (updated presentation)
PHP Detroit -- MySQL 8 A New Beginning (updated presentation)Dave Stokes
 
Lessons Learned From Running 1800 Clusters (Brooke Jensen, Instaclustr) | Cas...
Lessons Learned From Running 1800 Clusters (Brooke Jensen, Instaclustr) | Cas...Lessons Learned From Running 1800 Clusters (Brooke Jensen, Instaclustr) | Cas...
Lessons Learned From Running 1800 Clusters (Brooke Jensen, Instaclustr) | Cas...DataStax
 
Aioug vizag oracle12c_new_features
Aioug vizag oracle12c_new_featuresAioug vizag oracle12c_new_features
Aioug vizag oracle12c_new_featuresAiougVizagChapter
 
Practical Partitioning in Production with Postgres
Practical Partitioning in Production with PostgresPractical Partitioning in Production with Postgres
Practical Partitioning in Production with PostgresEDB
 
Big and Fast Data - Building Infinitely Scalable Systems
Big and Fast Data - Building Infinitely Scalable SystemsBig and Fast Data - Building Infinitely Scalable Systems
Big and Fast Data - Building Infinitely Scalable SystemsFred Melo
 
Scale Your Mission-Critical Applications With Neo4j Fabric and Clustering Arc...
Scale Your Mission-Critical Applications With Neo4j Fabric and Clustering Arc...Scale Your Mission-Critical Applications With Neo4j Fabric and Clustering Arc...
Scale Your Mission-Critical Applications With Neo4j Fabric and Clustering Arc...Neo4j
 
002 Introducing Neo4j 5 for Administrators - NODES2022 AMERICAS Beginner 2 - ...
002 Introducing Neo4j 5 for Administrators - NODES2022 AMERICAS Beginner 2 - ...002 Introducing Neo4j 5 for Administrators - NODES2022 AMERICAS Beginner 2 - ...
002 Introducing Neo4j 5 for Administrators - NODES2022 AMERICAS Beginner 2 - ...Neo4j
 
Distributing Data The Aerospike Way
Distributing Data The Aerospike WayDistributing Data The Aerospike Way
Distributing Data The Aerospike WayAerospike, Inc.
 
implementation of a big data architecture for real-time analytics with data s...
implementation of a big data architecture for real-time analytics with data s...implementation of a big data architecture for real-time analytics with data s...
implementation of a big data architecture for real-time analytics with data s...Joseph Arriola
 
The Proper Care and Feeding of MySQL Databases
The Proper Care and Feeding of MySQL DatabasesThe Proper Care and Feeding of MySQL Databases
The Proper Care and Feeding of MySQL DatabasesDave Stokes
 
Apache Kudu - Updatable Analytical Storage #rakutentech
Apache Kudu - Updatable Analytical Storage #rakutentechApache Kudu - Updatable Analytical Storage #rakutentech
Apache Kudu - Updatable Analytical Storage #rakutentechCloudera Japan
 

Similar to Bigdata netezza-ppt-apr2013-bhawani nandan prasad (20)

GLOC 2014 NEOOUG - Oracle Database 12c New Features
GLOC 2014 NEOOUG - Oracle Database 12c New FeaturesGLOC 2014 NEOOUG - Oracle Database 12c New Features
GLOC 2014 NEOOUG - Oracle Database 12c New Features
 
ORACLE 12C-New-Features
ORACLE 12C-New-FeaturesORACLE 12C-New-Features
ORACLE 12C-New-Features
 
Oracle 12 c new-features
Oracle 12 c new-featuresOracle 12 c new-features
Oracle 12 c new-features
 
Impala tech-talk by Dimitris Tsirogiannis
Impala tech-talk by Dimitris TsirogiannisImpala tech-talk by Dimitris Tsirogiannis
Impala tech-talk by Dimitris Tsirogiannis
 
Powering GIS Application with PostgreSQL and Postgres Plus
Powering GIS Application with PostgreSQL and Postgres Plus Powering GIS Application with PostgreSQL and Postgres Plus
Powering GIS Application with PostgreSQL and Postgres Plus
 
Technical Introduction to PostgreSQL and PPAS
Technical Introduction to PostgreSQL and PPASTechnical Introduction to PostgreSQL and PPAS
Technical Introduction to PostgreSQL and PPAS
 
Informix partitioning interval_rolling_window_table
Informix partitioning interval_rolling_window_tableInformix partitioning interval_rolling_window_table
Informix partitioning interval_rolling_window_table
 
PHP Detroit -- MySQL 8 A New Beginning (updated presentation)
PHP Detroit -- MySQL 8 A New Beginning (updated presentation)PHP Detroit -- MySQL 8 A New Beginning (updated presentation)
PHP Detroit -- MySQL 8 A New Beginning (updated presentation)
 
Lessons Learned From Running 1800 Clusters (Brooke Jensen, Instaclustr) | Cas...
Lessons Learned From Running 1800 Clusters (Brooke Jensen, Instaclustr) | Cas...Lessons Learned From Running 1800 Clusters (Brooke Jensen, Instaclustr) | Cas...
Lessons Learned From Running 1800 Clusters (Brooke Jensen, Instaclustr) | Cas...
 
Aioug vizag oracle12c_new_features
Aioug vizag oracle12c_new_featuresAioug vizag oracle12c_new_features
Aioug vizag oracle12c_new_features
 
Practical Partitioning in Production with Postgres
Practical Partitioning in Production with PostgresPractical Partitioning in Production with Postgres
Practical Partitioning in Production with Postgres
 
Big and Fast Data - Building Infinitely Scalable Systems
Big and Fast Data - Building Infinitely Scalable SystemsBig and Fast Data - Building Infinitely Scalable Systems
Big and Fast Data - Building Infinitely Scalable Systems
 
Scale Your Mission-Critical Applications With Neo4j Fabric and Clustering Arc...
Scale Your Mission-Critical Applications With Neo4j Fabric and Clustering Arc...Scale Your Mission-Critical Applications With Neo4j Fabric and Clustering Arc...
Scale Your Mission-Critical Applications With Neo4j Fabric and Clustering Arc...
 
002 Introducing Neo4j 5 for Administrators - NODES2022 AMERICAS Beginner 2 - ...
002 Introducing Neo4j 5 for Administrators - NODES2022 AMERICAS Beginner 2 - ...002 Introducing Neo4j 5 for Administrators - NODES2022 AMERICAS Beginner 2 - ...
002 Introducing Neo4j 5 for Administrators - NODES2022 AMERICAS Beginner 2 - ...
 
Distributing Data The Aerospike Way
Distributing Data The Aerospike WayDistributing Data The Aerospike Way
Distributing Data The Aerospike Way
 
Apache cassandra v4.0
Apache cassandra v4.0Apache cassandra v4.0
Apache cassandra v4.0
 
implementation of a big data architecture for real-time analytics with data s...
implementation of a big data architecture for real-time analytics with data s...implementation of a big data architecture for real-time analytics with data s...
implementation of a big data architecture for real-time analytics with data s...
 
The Proper Care and Feeding of MySQL Databases
The Proper Care and Feeding of MySQL DatabasesThe Proper Care and Feeding of MySQL Databases
The Proper Care and Feeding of MySQL Databases
 
Apache Kudu - Updatable Analytical Storage #rakutentech
Apache Kudu - Updatable Analytical Storage #rakutentechApache Kudu - Updatable Analytical Storage #rakutentech
Apache Kudu - Updatable Analytical Storage #rakutentech
 
Gcp data engineer
Gcp data engineerGcp data engineer
Gcp data engineer
 

More from Bhawani N Prasad

Understanding Robotic process automation by bhawani nandan prasad
Understanding Robotic process automation by bhawani nandan prasadUnderstanding Robotic process automation by bhawani nandan prasad
Understanding Robotic process automation by bhawani nandan prasadBhawani N Prasad
 
Apache spark with akka couchbase code by bhawani
Apache spark with akka couchbase code by bhawaniApache spark with akka couchbase code by bhawani
Apache spark with akka couchbase code by bhawaniBhawani N Prasad
 
Agile overview class for scrum masters
Agile overview class for scrum mastersAgile overview class for scrum masters
Agile overview class for scrum mastersBhawani N Prasad
 
Machine learning computer science by bhawani n prasad
Machine learning computer science by bhawani n prasadMachine learning computer science by bhawani n prasad
Machine learning computer science by bhawani n prasadBhawani N Prasad
 
What we can do in Retail analytics by bhawani nandanprasad
What we can do in Retail analytics by bhawani nandanprasadWhat we can do in Retail analytics by bhawani nandanprasad
What we can do in Retail analytics by bhawani nandanprasadBhawani N Prasad
 
Big data analytics bhawani nandan prasad
Big data analytics   bhawani nandan prasadBig data analytics   bhawani nandan prasad
Big data analytics bhawani nandan prasadBhawani N Prasad
 
Define enterprise integration strategy by industry leader bhawani nandanprasad
Define enterprise integration strategy by industry leader bhawani nandanprasadDefine enterprise integration strategy by industry leader bhawani nandanprasad
Define enterprise integration strategy by industry leader bhawani nandanprasadBhawani N Prasad
 
New IBM Information Server 11.3 - Bhawani Nandan Prasad
New IBM Information Server  11.3 - Bhawani Nandan PrasadNew IBM Information Server  11.3 - Bhawani Nandan Prasad
New IBM Information Server 11.3 - Bhawani Nandan PrasadBhawani N Prasad
 
Economic growth inequality across globe by bhawani nandan prasad
Economic growth inequality across globe  by bhawani nandan prasadEconomic growth inequality across globe  by bhawani nandan prasad
Economic growth inequality across globe by bhawani nandan prasadBhawani N Prasad
 
Agile lifecycle handbook by bhawani nandan prasad
Agile lifecycle handbook by bhawani nandan prasadAgile lifecycle handbook by bhawani nandan prasad
Agile lifecycle handbook by bhawani nandan prasadBhawani N Prasad
 
Agile project management tips and techniques
Agile project management tips and techniquesAgile project management tips and techniques
Agile project management tips and techniquesBhawani N Prasad
 
Cognos 10 upgrade migrate fixpack by bhawani nandan prasad
Cognos 10 upgrade migrate fixpack by bhawani nandan prasadCognos 10 upgrade migrate fixpack by bhawani nandan prasad
Cognos 10 upgrade migrate fixpack by bhawani nandan prasadBhawani N Prasad
 
Software development with scrum methodology bhawani nandan prasad
Software development with scrum methodology   bhawani nandan prasadSoftware development with scrum methodology   bhawani nandan prasad
Software development with scrum methodology bhawani nandan prasadBhawani N Prasad
 
Agile formanagers by-bhawaninandanprasad
Agile formanagers by-bhawaninandanprasadAgile formanagers by-bhawaninandanprasad
Agile formanagers by-bhawaninandanprasadBhawani N Prasad
 
Dsdm by bhawani nandanprasad
Dsdm by bhawani nandanprasadDsdm by bhawani nandanprasad
Dsdm by bhawani nandanprasadBhawani N Prasad
 

More from Bhawani N Prasad (20)

Understanding Robotic process automation by bhawani nandan prasad
Understanding Robotic process automation by bhawani nandan prasadUnderstanding Robotic process automation by bhawani nandan prasad
Understanding Robotic process automation by bhawani nandan prasad
 
Apache spark with akka couchbase code by bhawani
Apache spark with akka couchbase code by bhawaniApache spark with akka couchbase code by bhawani
Apache spark with akka couchbase code by bhawani
 
Agile overview class for scrum masters
Agile overview class for scrum mastersAgile overview class for scrum masters
Agile overview class for scrum masters
 
Product Management
Product ManagementProduct Management
Product Management
 
Product Engineering
Product EngineeringProduct Engineering
Product Engineering
 
Machine learning computer science by bhawani n prasad
Machine learning computer science by bhawani n prasadMachine learning computer science by bhawani n prasad
Machine learning computer science by bhawani n prasad
 
PM conpetency skills
PM conpetency skillsPM conpetency skills
PM conpetency skills
 
What we can do in Retail analytics by bhawani nandanprasad
What we can do in Retail analytics by bhawani nandanprasadWhat we can do in Retail analytics by bhawani nandanprasad
What we can do in Retail analytics by bhawani nandanprasad
 
Big data analytics bhawani nandan prasad
Big data analytics   bhawani nandan prasadBig data analytics   bhawani nandan prasad
Big data analytics bhawani nandan prasad
 
Program management-steps
Program management-stepsProgram management-steps
Program management-steps
 
Define enterprise integration strategy by industry leader bhawani nandanprasad
Define enterprise integration strategy by industry leader bhawani nandanprasadDefine enterprise integration strategy by industry leader bhawani nandanprasad
Define enterprise integration strategy by industry leader bhawani nandanprasad
 
New IBM Information Server 11.3 - Bhawani Nandan Prasad
New IBM Information Server  11.3 - Bhawani Nandan PrasadNew IBM Information Server  11.3 - Bhawani Nandan Prasad
New IBM Information Server 11.3 - Bhawani Nandan Prasad
 
Economic growth inequality across globe by bhawani nandan prasad
Economic growth inequality across globe  by bhawani nandan prasadEconomic growth inequality across globe  by bhawani nandan prasad
Economic growth inequality across globe by bhawani nandan prasad
 
Agile lifecycle handbook by bhawani nandan prasad
Agile lifecycle handbook by bhawani nandan prasadAgile lifecycle handbook by bhawani nandan prasad
Agile lifecycle handbook by bhawani nandan prasad
 
Agile project management tips and techniques
Agile project management tips and techniquesAgile project management tips and techniques
Agile project management tips and techniques
 
Cognos 10 upgrade migrate fixpack by bhawani nandan prasad
Cognos 10 upgrade migrate fixpack by bhawani nandan prasadCognos 10 upgrade migrate fixpack by bhawani nandan prasad
Cognos 10 upgrade migrate fixpack by bhawani nandan prasad
 
Software development with scrum methodology bhawani nandan prasad
Software development with scrum methodology   bhawani nandan prasadSoftware development with scrum methodology   bhawani nandan prasad
Software development with scrum methodology bhawani nandan prasad
 
Agile formanagers by-bhawaninandanprasad
Agile formanagers by-bhawaninandanprasadAgile formanagers by-bhawaninandanprasad
Agile formanagers by-bhawaninandanprasad
 
Dsdm by bhawani nandanprasad
Dsdm by bhawani nandanprasadDsdm by bhawani nandanprasad
Dsdm by bhawani nandanprasad
 
Cmmi vs-agile
Cmmi vs-agileCmmi vs-agile
Cmmi vs-agile
 

Recently uploaded

Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonAnna Loughnan Colquhoun
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Scriptwesley chun
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountPuma Security, LLC
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUK Journal
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking MenDelhi Call girls
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...apidays
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Enterprise Knowledge
 
Advantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessAdvantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessPixlogix Infotech
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Drew Madelung
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdfhans926745
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)wesley chun
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfsudhanshuwaghmare1
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...Martijn de Jong
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsMaria Levchenko
 

Recently uploaded (20)

Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
Advantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessAdvantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your Business
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 

Bigdata netezza-ppt-apr2013-bhawani nandan prasad

  • 1. © 2013 AgreeYa Solutions. All rights reserved.1 © 2013, AgreeYa Solutions. All rights reserved. www.agreeya.com Netezza Overview Netezza Architecture Netezza Performance Tuning Netezza Admin April 10, 2013 – BHAWANI NANDAN PRASAD – BI Practice Head SMP – IIM Calcutta, MBA – Stratford University USA, B.E. (IT)
  • 2. © 2013 AgreeYa Solutions. All rights reserved.2 Agenda • Netezza Architecture • Netezza Connectivity • NZSQL • Data Types in Netezza • Metadata Tables • Types of Joins in Netezza • Data Loading and Unloading in Netezza • Data Distribution in Netezza • Transactions in Netezza • GROOM/Reclaim Process in Netezza • Zone Maps in Netezza • GENERATE STATISTICS in Netezza
  • 3. © 2013 AgreeYa Solutions. All rights reserved.3 © 2013, AgreeYa Solutions. All rights reserved. www.agreeya.com Netezza Architecture
  • 4. © 2013 AgreeYa Solutions. All rights reserved.4 Netezza Architecture
  • 5. © 2013 AgreeYa Solutions. All rights reserved.5 Data Stream Processing in Netezza
  • 6. © 2013 AgreeYa Solutions. All rights reserved.6 Netezza Connectivity
  • 7. © 2013 AgreeYa Solutions. All rights reserved.7 NZSQL • Utility to interact with Netezza database • Useful to writing multi-liner queries, executing those for analysis or reporting purpose • Setting environment is a pre-requisite before starting on nzsql • Logging into nzsql opens the pg.log file and start capturing all activities performed by user on corresponding DB
  • 8. © 2013 AgreeYa Solutions. All rights reserved.8 Data Types in Netezza DATATYPE DESCRIPTION SIZE BOOL boolean, 'true'/'false' 1 BPCHAR char(length), blank-padded string, fixed storage length VAR CHAR single character 1 DATE ANSI SQL date 4 FLOAT4 single-precision floating point number, 4-byte storage 4 FLOAT8 double-precision floating point number, 8-byte storage 8 INT1 -128 to 127, 1-byte storage 1 INT2 -32 thousand to 32 thousand, 2-byte storage 2 INT4 -2 billion to 2 billion integer, 4-byte storage 4 INT8 ~18 digit integer, 8-byte storage 8 INTERVAL @ <number> <units>, time interval 12 NCHAR nchar VAR NUMERIC numeric(precision, decimal), arbitrary precision number 19 NVARCHAR nvarchar VAR TIME hh:mm:ss, ANSI SQL time 8 TIMESTAMP date and time 8 TIMETZ hh:mm:ss, ANSI SQL time 12 VARCHAR varchar(length), non-blank-padded string, variable storage length VAR
  • 9. © 2013 AgreeYa Solutions. All rights reserved.9 Metadata Tables in Netezza • Like any other database, Netezza also provides metadata tables and views which provides information about objects • Some of the frequently required MD tables are: System Table Name Usage _V_OBJECTS Used to display information related to different objects like tables, views, external tables, synonyms and more _V_TABLES Used to display information related to different tables present in Netezza _V_VIEW Used to display information related to different views present in Netezza _V_RELATION_COLUMN Used to display information related to different columns present in Netezza tables
  • 10. © 2013 AgreeYa Solutions. All rights reserved.10 Types of Joins in Netezza • Netezza internally processes joins in following order: – Hash Join (in memory) – Hash Join (in disk) – Sort Merge Join – Nested Loop Join – Cross Join • Netezza has three main types of joins available: – Co-located Join – Re-distribution of data – Broadcasting of data
  • 11. © 2013 AgreeYa Solutions. All rights reserved.11 Data Loading and Unloading in Netezza • NZLOAD (only loading) • EXTERNAL TABLES (both loading and unloading) • CTAS (CREATE TABLE AS) (both loading and unloading) • Nzsql with –o option (only unloading)
  • 12. © 2013 AgreeYa Solutions. All rights reserved.12 Data Distribution in Netezza • Key factor in shooting performance to great extent • Backbone of MPP architecture • Can be leverage using DISTRIBUTE ON clause after CRAETE TABLE statement • Of three types: – DISTRIBUTE ON (column name); – DISTRIBUTE ON RANDOM; – No DISTRIBUTE specification – Very useful while loading data into tables and fetching data from table
  • 13. © 2013 AgreeYa Solutions. All rights reserved.13 Selecting a distribution key • Columns with many distinct values • Column or columns based on selection set • As few columns as possible • Data distributed on same key • DO NOT use Boolean keys • Checking distribution of data in table
  • 14. © 2013 AgreeYa Solutions. All rights reserved.14 Collocated Join
  • 15. © 2013 AgreeYa Solutions. All rights reserved.15 Single Redistribute
  • 16. © 2013 AgreeYa Solutions. All rights reserved.16 Double Redistribute
  • 17. © 2013 AgreeYa Solutions. All rights reserved.17 Broadcast
  • 18. © 2013 AgreeYa Solutions. All rights reserved.18 Transactions in Netezza • Three basic columns to carry out transaction in Netezza – Createxid – Deletexid – Rowid • Values in these columns keep on changing with every transaction • These are hidden columns with every table in Netezza • Also used to track deleted records in many cases
  • 19. © 2013 AgreeYa Solutions. All rights reserved.19 Transactions in Netezza contd..
  • 20. © 2013 AgreeYa Solutions. All rights reserved.20 Aborted Transaction in Netezza
  • 21. © 2013 AgreeYa Solutions. All rights reserved.21 Locking, Concurrency and Isolation • Netezza implements serializable transaction isolation for highest level of consistency • Multi-versioning and Serialization dependency checking • User cannot explicitly lock a table in Netezza • UPDATE clause works differently in Netezza
  • 22. © 2013 AgreeYa Solutions. All rights reserved.22 GROOM/Reclaim in Netezza • Logically deleted records reside in memory in Netezza in following cases: – INSERT – UPDATE – Failed INSERT or aborted nzload operation – Failed UPDATE operation • Logically deleted records in Netezza causes: – Occupancy of extra disk space – Requires extra time for full table scan
  • 23. © 2013 AgreeYa Solutions. All rights reserved.23 GROOM/Reclaim contd.. • GROOM/ RECLAIM process recovers this unused disk space in Netezza • GROOM command support operations for: – Single table – All tables in one database – All tables in all database • Benefits of GROOM: – Permits shared access to target table – Can be interrupted without leaving target table locked – Refreshed materialized views created on base table • Syntax:
  • 24. © 2013 AgreeYa Solutions. All rights reserved.24 Zone Maps in Netezza • Zone Maps are similar to indexes in any other DB • Created on integer, date and timestamp fields • Created and refreshed automatically when: • GENERATE STATSTICS • NZLOAD • INSERT or UPDATE • GROOM Operation
  • 25. © 2013 AgreeYa Solutions. All rights reserved.25 GENERATE STATISTICS in Netezza • Netezza optimizer relies on GENERATE STATISTICS to gather statistics about tables • GENERATE STATISTICS collects statistics about each table columns: – Minimum and maximum values on character data – Maximum and average length on varchar – NULL Counts – Updates the system catalog • GENERATE STATISTICS can be collected at three levels: – Database Level – Table level – Column Level • Can also be collected using Nzadmin tool
  • 26. © 2013 AgreeYa Solutions. All rights reserved.26 GENERATE STATISTICS contd.. • Netezza system generates two basic statistics, table row count and min-max values for character columns while doing: – INSERT – UPDATE – CTAS (GENERATE STATISTICS is automatically created is row count >= 10k) – Nzload – GROOM – TRUNCATE TABLE • It is important to generate statistics for:
  • 27. © 2013 AgreeYa Solutions. All rights reserved.27 SPU Failover Activity  Disk timing : It shows the SPU showing the slow performance  Step 1) Pause the system • nzsql>> nzsystem pause  Step 2) Confirm that the system is paused • nzsql>> nzstate  Step 3) Failover the SPU • nzsql>> nzspu failover -id <SPU ID>  Step 4) Resume the system • nzsql>> nzsystem resume
  • 28. © 2013 AgreeYa Solutions. All rights reserved.28 Genstats Command  To generate statistics on any database table(s) for which the statistics • are not currently 100% "up-to-date".  The optimizer uses statistics to guide its decisions on how best to execute a query. The more reliable and up-to-date the statistics are,more accurate optimizer's decisions are likely to be.
  • 29. © 2013 AgreeYa Solutions. All rights reserved.29 Backup & Restore  Types of Back up :  Full Back up  Differential backup  Incremental Differential backup  Cumulative Differential backup  Elaborative Example •
  • 30. © 2013 AgreeYa Solutions. All rights reserved.30 Back up Command  Backup command / scripts is used for backing up tables from NPS.  Backup command / nz_backup script must be run locally (on the NPS host being backed up).  These command/scripts processes a single table, multiple tables, or an entire database.  The data format that is used can be either  ascii -- which is very portable.  binary-- which is Netezza's compressed/internal format, which is much faster, and results in significantly smaller backup sets.  gzip -- ascii, which is gzip'ed on the NPS host.  The data is written to (or read from) disk files or named pipes.  If pipes are used, another application is used to produce the data.  These scripts just concern themselves with the DATA itself. When backing up • a table, the DDL is not included.
  • 31. © 2013 AgreeYa Solutions. All rights reserved.31 Back up Command Examples  Full backup: • /nz/kit/bin/nzbackup -db CIDB_PRD -dir /back_folder • nohup nzbackup -db CIDB_PRD -u admin -dir /back_folder  Differential backup:  /nz/kit/bin/nzbackup -db CIDB_PRD -u admin -dir /back_folder -differential -v • nohup nzbackup -db CIDB_PRD -u admin -dir /back_folder -schema-only
  • 32. © 2013 AgreeYa Solutions. All rights reserved.32 Restore Command  Restore command / scripts is used to restore tables to NPS.  Restore command / nz_restore script must be run locally (on the NPS host being restored ).  These command/scripts processes a single table, multiple tables, or an entire database.  The data format that is used can be either  ascii -- which is very portable.  binary-- which is Netezza's compressed/internal format, which is much faster, and results in significantly smaller backup sets.  gzip -- ascii, which is gzip'ed on the NPS host.  The data is written to (or read from) disk files or named pipes.  If pipes are used, another application is used to produce the data.  These scripts just concern themselves with the DATA itself. When backing up • a table, the DDL is not included.
  • 33. © 2013 AgreeYa Solutions. All rights reserved.33 Restore Command  Syntax : nzrestore [-db database] [-dir directory] • [-connector name] [-connectorArgs] [-schema only] » [-users] [-v] [-rev] [-h] [-increment] [-mode] • [-backupset ID] [-lockdb]  Here,  -dir specifies the backup root directory when using the file system connector  -connector specifies the connector type either File System, Veritas or Tivoli • NOTE : If -connector is omitted defaults to File System connector  -connectorArgs specifies: • - DATASTORE_SERVER and DATASTORE_POLICY when using the Veritas connector • - The TSM password when using Tivoli connector • - may optionally be specified as environment variables  -If incremental is omitted, defaults to full backup  -mode specifies REST /NEXT mode .  -lockdb specifies locking of database during restore [ TRUE/FALSE]
  • 34. © 2013 AgreeYa Solutions. All rights reserved.34 Tape Back up Command  Similar to Backup Command.  Syntax is also similar  Command only Differs in the destination location which is “Tape” instead of any file location as that of normal backup .  Example :  nzbackup -v -db EDW_STANDBY -dir /migration/TF12_EDW_STANDBY_Tape_Backup/tape1 /migration/TF12_EDW_STANDBY_Tape_Backup/tape2 /migration/TF12_EDW_STANDBY_Tape_Backup/tape3 /migration/TF12_EDW_STANDBY_Tape_Backup/tape4 -streams 4
  • 35. © 2013 AgreeYa Solutions. All rights reserved.35 Netezza Performance Server
  • 36. © 2013 AgreeYa Solutions. All rights reserved.36 Defaults in Netezza  Default Users  nz (Linux OS user )  admin ( NPS database super-user with full access to all system functions and objects )  root ( Linux root user )  System Defaults  system database  public group  5480 – ODBC port  Note :  By default user created is added to the public group  User can’t be deleted from public group  group, user & database share a common namespace.so group name, user name and database names must be unique.
  • 37. © 2013 AgreeYa Solutions. All rights reserved.37 Managing Users  By default user have access to only system views allowing then to retrieve a list of used database objects.  Sql for Creating User :  CREATE USER user_name WITH PASWORD ‘string’ [options]  Sql for altering User credentials/privileges  ALTER USER user_name WITH [options]  Sql for deleting User :  DROP USER user_name  Note : Here options can be :  Row limit, Group name, Validity, Session Time out, Query Time out, Default priority, Maximum priority, Resource group  Nzsql command to list user • SYSTEM(ADMIN) => du  Nzsql command to list user’s permission • SYSTEM(ADMIN) => dpu
  • 38. © 2013 AgreeYa Solutions. All rights reserved.38 Managing Groups  By default group created is public group.  By default user is added in public group.  Sql for Creating Group :  CREATE GROUP group_name WITH PASWORD ‘string’ [options]  Sql for altering group credentials/privileges  ALTER GROUP group_name [ADD|OWNER|RENAME|WITH]  Sql for deleting User :  DROP USER use_name  Note : Here options can be :  Row limit, Session Time out, Query Time out, Default priority, Maximum priority, Resource limit, user names  Nzsql command to list user • SYSTEM(ADMIN) => dg  Nzsql command to list user’s permission • SYSTEM(ADMIN) => dpg
  • 39. © 2013 AgreeYa Solutions. All rights reserved.39 Permissions  Types of Permission :  Object Permissions [ 11 nos. ]: • List, Select • Insert, Delete, Update • Alter, Drop, Truncate • Lock, Abort, Load, Genstat  Admin Permissions [ 13 nos. ]: • Database, Temporary Table, External Table , System Table, view • User, Group • Create, Backup, Restore, Reclaim • Hardware, system  Scope of Permission :  Applicable only to Object Permissions :  Two classes • Local Scope : Applicable when logged into particular database • Global Scope : Applicable when logged into system database  By default Admin permissions are Global in Nature
  • 40. © 2013 AgreeYa Solutions. All rights reserved.40 Object Permissions  Object Permissions Granted in the system database are inherited by all other databases • i.e. they have global scope  Object Permissions Granted within database are local to the databases • i.e. they have local scope  Object Permissions are additive in nature • i.e. Effectively all permission the of an object • = User Permissions + Group Permissions + Public Permission  Sql for Granting Object Permission :  GRANT object_permission On object TO {PUBLIC | GROUP group_name | user_name } [ WITH GRANT OPTION ]  Sql for Revoking Object Permission :  REVOKE object_permission On object TO {PUBLIC | GROUP group_name | user_name }
  • 41. © 2013 AgreeYa Solutions. All rights reserved.41 Admin Permissions  Admin Permissions are Global in scope  Sql for Granting Admin Permission :  GRANT admin_permission TO {PUBLIC | GROUP group_name | user_name } [ WITH GRANT OPTION ]  Sql for Revoking Admin Permission :  REVOKE admin_permission TO {PUBLIC | GROUP group_name | user_name }  Nzsql command to list user’s permission  SYSTEM(ADMIN) => dpu  Nzsql command to list group’s permission  SYSTEM(ADMIN) => dpg
  • 42. © 2013 AgreeYa Solutions. All rights reserved.42 Listing All Permissions to User/Group
  • 43. © 2013 AgreeYa Solutions. All rights reserved.43 Viewing The Distribution & Skew  In CLI  on linux prompt • $ nz_skew utility ( on Linux Prompt )  on nzsql prompt • nzsql => SELECT datasliceid, COUNT(datasliceid) AS "ROWS" FROM MB_STU_PRE • GROUP BY datasliceid • ORDER BY "ROWS";  In GUI – In nzAdmin –> Tools –> Table Skews  NOTE :  For changing the distribution key Create Table table_name AS ( select clause ) is used with Distribution Key  If distribution clause is not specified in the CTAS, parent table distribution key column is used as distribution by default.  The default threshold to display skew of table 100 MB.
  • 44. © 2013 AgreeYa Solutions. All rights reserved.44 Log Files in Netezza  All the log file in Netezza are in the directory:  /nz/kit/log/  Various log created are  Alcapp, alcloader, waitForAlcapp  backupsvr, bnrmgr, restoresvr  bootsvr, dbos  Clientmgr , eventmgr, sessionmgr, sysmgr  fcommrtx  gencErrors, hostStatsGen  Loadmgr , nzloadTmpLogs  Plans , planshist, postgres  sendMail  ssgdba  startupsvr, statsSvr
  • 45. © 2013 AgreeYa Solutions. All rights reserved.45 Priority  Priority are  Job Priority  Session Priority  Priority values are defined for a user, a group, or as the system default  Sys determines value of priority to use when the user connects to the host – and executes SQL commands  Two more are there- SYSTEM CRITICAL (highest) and SYSTEM BACKGOUND (lowest), which are not visible to user.  The possible priorities are critical, high, normal, low, or none.  The default priority for groups, and the system is none.  If priorities are not set, user sessions run at normal priority.
  • 46. © 2013 AgreeYa Solutions. All rights reserved.46 Priority (Contd…)  The syntax to set system priority is:  SET SYSTEM DEFAULT – [SESSIONTIMEOUT | ROWSETLIMIT | QUERYTIMEOUT ] TO [number | UNLIMITED ] – [DEFPRIORITY | MAXPRIORITY ] to [CRITICAL | HIGH | NORMAL | LOW | NONE]  The syntax to create group and set default priority is :  SHOW SYSTEM DEFAULT MAXPRIORITY;  SHOW SYSTEM DEFAULT DEFPRIORITY;  The syntax to create group and set default priority is :  CREATE GROUP group_name WITH DEFPRIORITY TO HIGH;  The syntax to create group and set default priority is :  CREATE USER user_name WITH DEFPRIORITY TO CRITICAL;
  • 47. © 2013 AgreeYa Solutions. All rights reserved.47 Priority (Contd…)  The syntax to change the priority of a session  ALTER SESSION [<session_id>] SET PRIORITY TO <priority> ;  Example : – nzsql=> ALTER SESSION 21664 SET PRIORITY TO HIGH;  The syntax to change priority of a session using nzsession  nzsession priority -high -u nz -pw password -id 21664;
  • 48. © 2013 AgreeYa Solutions. All rights reserved.48 Migration  Based on the activities migration activities are classified as :  Data Migration  Environment migration :  Code Migration
  • 49. © 2013 AgreeYa Solutions. All rights reserved.49 Data Migration  Data Migration : It is done on two different ways :  nz_migrate utility –  Syntax : nz_migrate -shost <name/IP> -thost <name/IP> » -sdb <dbname> -tdb <dbname> » [optional args]  This script must be invoked from the 'source' machine.  Optionally, this script can automatically create the target database and objects via the options • -CreateTargetTable • -CreateTargetDatabase  Through means of external table –  Database/table is converted to external table from the source  External table is converted back to database/table to the target
  • 50. © 2013 AgreeYa Solutions. All rights reserved.50 Environment Migration  Environment Migration :  Objects created by developer in personal database EDW_UT is moved to development EDW_SIT database  All the Objects present in development database EDW_SIT is moved to Production database EDW_PROD  This is done through customized script “export/home/nz/psm/scripts/dlc/object.in.work.prod.bash “  NOTE : – There is also a script “export/home/nz/psm/scripts/dlc/object.in.work.create.bash” which – promotes objects from personal database EDW_UT to EDW_SIT for testing purpose
  • 51. © 2013 AgreeYa Solutions. All rights reserved.51 Code Migration  Code migration :  This is a manually method where ddl of the objects like table,view etc are obtained using nz_ddl_table, nz_ddl_view and through this ddl’s objects are created.  The privileges (ACL) of the object is obtained through nz_get_acl utility, is used to reproduce the ACL on the newly created object .  This is done through customized script .
  • 52. © 2013 AgreeYa Solutions. All rights reserved.52 Events and Alerts By default there are total 40 events for which alert is being raised. They are :1. CPUcoresOK_em_NzCS 2. CPUcoresReduced_em_NzCS 3. HostNoLongerOnline 4. HostNotOnline 5. MemFlt_rc_NzCS 6. NzDAC_QDR_fault_em_NzCS 7. Regen_em_NzCS 8. RunAwayQuery_TF12 9. RunAway_rc_NzCS 10. RunAway_rc_monitor 11. SystemOnline 12. coreRequest_em_NzCS 13. dFPGA_em_NzCS 14. dFPGA_em_NzCS_r 15. diskFull_8x_em_NzCS 16. diskFull_90_em_NzCS 17. diskFull_95_em_NzCS 18. histCapture_em_NzCS 19. histLoad_em_NzCS 20. hwFlt_FanOrPwr_em_NzCS
  • 53. © 2013 AgreeYa Solutions. All rights reserved.53 Monitoring & Gathering Scripts  Monitoring on the system is done through customized/ system scripts which executes daily in following ways :  Hourly performance data of each server is generated and mailed  Consolidated reports on various daily dba activites ( like backup, restore, genstat, reclaim etc..) for all the servers is generated and mailed  Complete health report is generated by nz_health and mailed  Report of all the log activities is generated by and mailed  SPU performance is checked by disk_timing script on every 8 hrs.
  • 54. © 2013 AgreeYa Solutions. All rights reserved.54 www.agreeya.com 54 Thank You BHAWANI NANDAN PRASAD BI & Analytics Practice Head Bhawani.prasad@agreeya.net +91 9717570222