SlideShare uma empresa Scribd logo
1 de 9
Baixar para ler offline
Oracle to Netezza
Migration
Whitepaper

This white paper provides key challenges faces and resolved
and the approach followed in Oracle to Netezza migration for
one of the fortune 100 Retailer.

Asis Mohanty, asismohanty@gmail.com
11/11/2011
Table of Contents
1.      Introduction .......................................................................................................................................... 3
2.      Migration Approach and Process .......................................................................................................... 3
3.      Impact Analysis ..................................................................................................................................... 3
     3.1 Database Impact Analysis: .................................................................................................................. 3
     3.2 ETL/ELT Impact Analysis: ..................................................................................................................... 4
     3.3 Applications and Reports Impact Analysis: ......................................................................................... 4
4.      Design and Development ...................................................................................................................... 4
     4.1 Database Objects Design and Development Considerations: ............................................................ 4
     4.2 ETL/ELT Design and Development Considerations: ............................................................................ 5
     4.3 Report Design and Development Considerations: .............................................................................. 5
5.      History Load .......................................................................................................................................... 5
6.      Testing ................................................................................................................................................... 6
7.      Lesson Learnt and Recommendations .................................................................................................. 7
8.      Appendix ............................................................................................................................................... 9
     Automation Recommendations ................................................................................................................ 9
     About the Author ...................................................................................................................................... 9




                                                                                       | Oracle to Netezza Migration                                    2
1. Introduction
Every organization wants to have advance analytics, real time or near real time data which will help then
in Business Intelligence and competitive Intelligence. High volume growth, scalability, Maintainability
cost is the critical factor for any organization. Netezza provides a platform which is highly Scalable with
availability, easy to integrate and maintain, cost effective (Pay per usage), Asymmetric Massively Parallel
Processing architecture for performance.

This white paper contains the approach followed, key challenges faces and resolved during the Oracle to
Netezza migration program for one of the Fortune 100 Retailer.


2. Migration Approach and Process
Oracle to Netezza migration has various SDLC process and steps as given below (Figure-1).




                        Figure-1: Various steps involved in Oracle to Netezza Migration



3. Impact Analysis
This is the most critical phase to identify all impacted Data Base Objects, ETLs and applications/Reports
required for downstream.

3.1 Database Impact Analysis:
Identify all impacted Data Base Objects such as Tables, Views, Stored procedures, Index, Partitions,
Volumetric (Volume of Data to be migrated) to be migrated from existing Oracle platform to Netezza
Platform. Create the impact inventory list of DB Objects.
                                                          | Oracle to Netezza Migration           3
Figure-2: Example of Existing DB & ETL Environment

For example in the above figure (Figure-2), as part of Data Base Object impact identify all Tables, Views,
Stored procedures, Index, Partitions, Volumetric being used in Stage, Warehouse and Mart
environment.

3.2 ETL/ELT Impact Analysis:
Identify all ETL mapping or Jobs and ELT scripts being used by all impacted DB Objects. Create the
inventory list of all impacted ETL mappings or Jobs and ELT Scripts along with patterns.

For example in the above figure (Figure-2), as part of ETL/ELT impact identify all ETL/ELT Scripts being
used from Source to Stage, Stage to Warehouse and Warehouse to MART.

3.3 Applications and Reports Impact Analysis:
Identify all Reports or applications which are using the Warehouse or Mart Data. Create the inventory
list of all impacted Reports and applications.




                     Figure-3: Example of Existing Reporting and Application Environment

For Example as shown in the above figure (Figure-2) identify all Reports and various application which is
using the Warehouse or Mart DB Objects (Tables, Views .etc).


4. Design and Development
This section contains the design and development of Database Objects, ETL/ELT and
Reports/Applications.

4.1 Database Objects Design and Development Considerations:
    •   As-Is migration verses re-architecture of Data model is most critical factor.


                                                         | Oracle to Netezza Migration             4
•   Identify all Oracle Vs Netezza DB Object data type, Size and compatibility. For example the Date
       time stamp format, CLOB, LONG Data type etc.
   •   Identify all Oracle Vs Netezza DB functions. Data type and function will be helpful while
       converting and Oracle SQL to Netezza SQL.
   •   Netezza does not support Index where as it supports Distribution Key. While creating the new
       Netezza Tables the Oracle Table Index must be considered.

4.2 ETL/ELT Design and Development Considerations:
   •   Identify different patterns of ETL Mapping/Jobs.
   •   Identify all impacted ETL where Source connection to be modified from Oracle to Netezza.
       (Example: All Sources to Stage ETL will fall under this category. Refer Figure-1)
   •   Identify all impacted ETL where Source and Target connection to be modified from Oracle to
       Netezza. (Example: All ETL Mapping/Jobs being used in Stage to Warehouse and Warehouse to
       Mart load)
   •   Identify all embedded SQL queries used in ETL mappings/Jobs which need to be converted from
       Oracle to Netezza specific SQL Query.
   •   Identify all ELT Scripts where embedded Oracle SQL queries to be converted to Netezza SQLs.
   •   Make the Netezza specific changes in ETL Mappings/Jobs and do unit testing. Early Unit testing
       identifies various issues.

4.3 Report Design and Development Considerations:
   •   Identify different patterns of Reports.
   •   Identify all embedded SQL queries used in reports by report.
   •   Identify all matrices which used Oracle specific Functions and Data type which needs to be
       converted to Netezza specific functions and data type.
   •   Make Netezza specific changes in Reports. There might be some extra space, rounding, Special
       Character issue which needs to be addressed during development phase.


5. History Load
History load from Oracle to Netezza can be done in following methods.

   •   Extract Oracle Table to flat file using Oracle Database utilities. Load the flat file to corresponding
       Netezza table using NZ Load. (Always use Bulk load/insert/update utility which is faster)
   •   Use the ETL tool to create one time mapping which will load history data from Oracle to
       Netezza.
   •   Use Unix/Perl script to extract data from Oracle and load it to Netezza.
   •   Use Third Party tool which uses the Bulk Read and Write mechanism to migrate history data
       from Oracle to Netezza. Wisdomforce Fastreader (www.wisdomforce.com/products-
       FastReader.html) supports the bulk/fast load from Oracle to Netezza.




                                                        | Oracle to Netezza Migration               5
•   Define the History data Delta if the history data volume is high, for example the existing
       Enterprise Data Warehouse has 6 Years worth of Data. Define two to three deltas each 2 Years
       worth of data to load.


6. Testing
Testing is the most important phase to identify any issue. Following are few of the recommendations:

   •   Database Object Testing
           o Compare the DB Objects (Oracle Vs Netezza) along with distribution Key.
           o Create automated script which can compare the DB Objects by querying System table
               scans.
   •   SQL Testing
           o Compare the Oracle SQL results with Netezza SQL results. The SQL used in ETL, Scripts,
               Reports must be compared during the development phase.
           o There are third party tolls available for Oracle to Netezza SQL conversion such as Sqissql
               (www.swissql.com). Even automated Perl or UNIX scripts can be created to convert the
               SQL.
   •   ETL Testing/Data Testing:
           o Run the existing Oracle ETL Job and new Netezza ETL Job. Compare the target Oracle
               table Vs Netezza table data.
           o If the Table volume is small, do a byte to byte data comparison of the data (Except Data
               Time Stamp fields)
           o If the volume is high use mathematical function (Min, Max and Average of additive field)
               along with count match. Do a sampling using Min/Max functions.
           o Created automated script to compare the Data. This script and method will be used for
               History data comparison too.
   •   Report/Application Testing:
           o Run the report/Application in Oracle and Netezza environment. Compare the report
               manually or use Reporting environment specific tool to compare the result. For Example
               Microstrategy has ‘Microstrategy integrity manager’ which compares the reports.
           o There are third party tools available which compared Byte-to-Byte of .csv, pdf, xls, html
               reports, for example Beyondcompare (www.scootersoftware.com) .
   •   Performance Testing:
           o Capture the ETL run statistics while running in Oracle environment and Netezza
               Environment. Compare the ETL load run statistics to know the performance. This must
               be done for the load window too.
           o Capture the Report run statistics for Oracle and Netezza environment. Compare the run
               statistics for Report performance in new Netezza environment.




                                                      | Oracle to Netezza Migration            6
7. Lesson Learnt and Recommendations
Following are few of the lessons learnt from the Oracle to Netezza Migration Project.

                POC
            o   Do POC for early identification of issues and approach of Migration.
                Product Issue Resolution
            o   There might be product related issue (Compatibility) which must be resolved quickly.
            o   Netezza Special Characters (Ex: Trademark) might appear differently in reports.
            o   Rounding of matrices in Reports.
            o   Extra space in matrices, field of Reports.
                Resource
            o   Cross skilled resource (resource having Oracle and Netezza Knowledge)
                Automation accelerators
            o   Automation is the key to avoid any manual mistake during the Analysis, Development
                and Testing. Following Figure-4 shows the automations recommendations for Oracle to
                Netezza Migration.




                         Figure-4: Automation Recommendations in various Phases

                Upgrade of Reporting and ETL Environment

                                                       | Oracle to Netezza Migration         7
o   Upgrade of ETL environment or Reporting environment along with DB Platform
    migration is not recommended.
    Performance
o   Perform Performance testing for any early indication of system performance.
    Usage of Advance Feature of Netezza
o   Use the advance feature such as NZ Load utilities for better performance for high
    volume of data load.




                                          | Oracle to Netezza Migration             8
8. Appendix


Automation Recommendations
Tools to Convert Oracle to Netezza SQLs and DDLs: Create Unix/Perl script which converts the Oracle
specific DDL or SQL to Netezza specific DDL/SQL. This script will use the Oracle Vs Netezza Data Type,
Functions and patterns to convert.

Tools to Convert the Oracle ETL to Netezza ETL: Create a tool which takes ETL xml as input and changes
the Oracle tag to Netezza tag (connections, extra Tags) and converts Oracle embedded SQLs to Netezza
SQLs.

Data Testing Tool: Create a Unix/Perl script/tool which compares the Oracle Table data Vs Netezza Table
data. Following are the two patterns:

Low volume Table: Compare the byte-to-byte data.

High Volume Table: Compare the Count of records, use mathematical functions such as Min, Max, Sum,
Average .etc for numeric field. Use sampling of data comparison (Top 10 records using Rank functions
.etc)

About the Author
Asis Mohanty has more than 11 Years of Industry experience on Data Warehousing and Business
Intelligence field. He is a Certified Business Intelligence Professional from www.tdwi.org and Certified
Data Management Professional from www.dama.org and Netezza Silver Certified Professional. For any
query contact Asis Mohanty at asismohanty@gmail.com .




                                                       | Oracle to Netezza Migration             9

Mais conteúdo relacionado

Mais procurados

HBase Operations and Best Practices
HBase Operations and Best PracticesHBase Operations and Best Practices
HBase Operations and Best PracticesVenu Anuganti
 
Oracle Parallel Distribution and 12c Adaptive Plans
Oracle Parallel Distribution and 12c Adaptive PlansOracle Parallel Distribution and 12c Adaptive Plans
Oracle Parallel Distribution and 12c Adaptive PlansFranck Pachot
 
How Adobe Does 2 Million Records Per Second Using Apache Spark!
How Adobe Does 2 Million Records Per Second Using Apache Spark!How Adobe Does 2 Million Records Per Second Using Apache Spark!
How Adobe Does 2 Million Records Per Second Using Apache Spark!Databricks
 
Time-Series Apache HBase
Time-Series Apache HBaseTime-Series Apache HBase
Time-Series Apache HBaseHBaseCon
 
High throughput data replication over RAFT
High throughput data replication over RAFTHigh throughput data replication over RAFT
High throughput data replication over RAFTDataWorks Summit
 
Part 2: Apache Kudu: Extending the Capabilities of Operational and Analytic D...
Part 2: Apache Kudu: Extending the Capabilities of Operational and Analytic D...Part 2: Apache Kudu: Extending the Capabilities of Operational and Analytic D...
Part 2: Apache Kudu: Extending the Capabilities of Operational and Analytic D...Cloudera, Inc.
 
Interactive Analytics in Human Time
Interactive Analytics in Human TimeInteractive Analytics in Human Time
Interactive Analytics in Human TimeDataWorks Summit
 
What’s New in the Upcoming Apache Spark 3.0
What’s New in the Upcoming Apache Spark 3.0What’s New in the Upcoming Apache Spark 3.0
What’s New in the Upcoming Apache Spark 3.0Databricks
 
Parquet Hadoop Summit 2013
Parquet Hadoop Summit 2013Parquet Hadoop Summit 2013
Parquet Hadoop Summit 2013Julien Le Dem
 
Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...
Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...
Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...Databricks
 
Apache Arrow: In Theory, In Practice
Apache Arrow: In Theory, In PracticeApache Arrow: In Theory, In Practice
Apache Arrow: In Theory, In PracticeDremio Corporation
 
Spark Summit EU talk by Mike Percy
Spark Summit EU talk by Mike PercySpark Summit EU talk by Mike Percy
Spark Summit EU talk by Mike PercySpark Summit
 
Interactive real time dashboards on data streams using Kafka, Druid, and Supe...
Interactive real time dashboards on data streams using Kafka, Druid, and Supe...Interactive real time dashboards on data streams using Kafka, Druid, and Supe...
Interactive real time dashboards on data streams using Kafka, Druid, and Supe...DataWorks Summit
 
Cloud-Native Apache Spark Scheduling with YuniKorn Scheduler
Cloud-Native Apache Spark Scheduling with YuniKorn SchedulerCloud-Native Apache Spark Scheduling with YuniKorn Scheduler
Cloud-Native Apache Spark Scheduling with YuniKorn SchedulerDatabricks
 
Delta from a Data Engineer's Perspective
Delta from a Data Engineer's PerspectiveDelta from a Data Engineer's Perspective
Delta from a Data Engineer's PerspectiveDatabricks
 
Streaming Visualization
Streaming VisualizationStreaming Visualization
Streaming VisualizationGuido Schmutz
 
Best Practices for Building and Deploying Data Pipelines in Apache Spark
Best Practices for Building and Deploying Data Pipelines in Apache SparkBest Practices for Building and Deploying Data Pipelines in Apache Spark
Best Practices for Building and Deploying Data Pipelines in Apache SparkDatabricks
 
Near real-time statistical modeling and anomaly detection using Flink!
Near real-time statistical modeling and anomaly detection using Flink!Near real-time statistical modeling and anomaly detection using Flink!
Near real-time statistical modeling and anomaly detection using Flink!Flink Forward
 

Mais procurados (20)

HBase Operations and Best Practices
HBase Operations and Best PracticesHBase Operations and Best Practices
HBase Operations and Best Practices
 
Oracle Parallel Distribution and 12c Adaptive Plans
Oracle Parallel Distribution and 12c Adaptive PlansOracle Parallel Distribution and 12c Adaptive Plans
Oracle Parallel Distribution and 12c Adaptive Plans
 
FASTER Key-Value Store and Log
FASTER Key-Value Store and LogFASTER Key-Value Store and Log
FASTER Key-Value Store and Log
 
How Adobe Does 2 Million Records Per Second Using Apache Spark!
How Adobe Does 2 Million Records Per Second Using Apache Spark!How Adobe Does 2 Million Records Per Second Using Apache Spark!
How Adobe Does 2 Million Records Per Second Using Apache Spark!
 
Time-Series Apache HBase
Time-Series Apache HBaseTime-Series Apache HBase
Time-Series Apache HBase
 
High throughput data replication over RAFT
High throughput data replication over RAFTHigh throughput data replication over RAFT
High throughput data replication over RAFT
 
Part 2: Apache Kudu: Extending the Capabilities of Operational and Analytic D...
Part 2: Apache Kudu: Extending the Capabilities of Operational and Analytic D...Part 2: Apache Kudu: Extending the Capabilities of Operational and Analytic D...
Part 2: Apache Kudu: Extending the Capabilities of Operational and Analytic D...
 
Interactive Analytics in Human Time
Interactive Analytics in Human TimeInteractive Analytics in Human Time
Interactive Analytics in Human Time
 
What’s New in the Upcoming Apache Spark 3.0
What’s New in the Upcoming Apache Spark 3.0What’s New in the Upcoming Apache Spark 3.0
What’s New in the Upcoming Apache Spark 3.0
 
Parquet Hadoop Summit 2013
Parquet Hadoop Summit 2013Parquet Hadoop Summit 2013
Parquet Hadoop Summit 2013
 
Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...
Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...
Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...
 
Tableau Architecture
Tableau ArchitectureTableau Architecture
Tableau Architecture
 
Apache Arrow: In Theory, In Practice
Apache Arrow: In Theory, In PracticeApache Arrow: In Theory, In Practice
Apache Arrow: In Theory, In Practice
 
Spark Summit EU talk by Mike Percy
Spark Summit EU talk by Mike PercySpark Summit EU talk by Mike Percy
Spark Summit EU talk by Mike Percy
 
Interactive real time dashboards on data streams using Kafka, Druid, and Supe...
Interactive real time dashboards on data streams using Kafka, Druid, and Supe...Interactive real time dashboards on data streams using Kafka, Druid, and Supe...
Interactive real time dashboards on data streams using Kafka, Druid, and Supe...
 
Cloud-Native Apache Spark Scheduling with YuniKorn Scheduler
Cloud-Native Apache Spark Scheduling with YuniKorn SchedulerCloud-Native Apache Spark Scheduling with YuniKorn Scheduler
Cloud-Native Apache Spark Scheduling with YuniKorn Scheduler
 
Delta from a Data Engineer's Perspective
Delta from a Data Engineer's PerspectiveDelta from a Data Engineer's Perspective
Delta from a Data Engineer's Perspective
 
Streaming Visualization
Streaming VisualizationStreaming Visualization
Streaming Visualization
 
Best Practices for Building and Deploying Data Pipelines in Apache Spark
Best Practices for Building and Deploying Data Pipelines in Apache SparkBest Practices for Building and Deploying Data Pipelines in Apache Spark
Best Practices for Building and Deploying Data Pipelines in Apache Spark
 
Near real-time statistical modeling and anomaly detection using Flink!
Near real-time statistical modeling and anomaly detection using Flink!Near real-time statistical modeling and anomaly detection using Flink!
Near real-time statistical modeling and anomaly detection using Flink!
 

Semelhante a Oracle to Netezza Migration Casestudy

To Study E T L ( Extract, Transform, Load) Tools Specially S Q L Server I...
To Study  E T L ( Extract, Transform, Load) Tools Specially  S Q L  Server  I...To Study  E T L ( Extract, Transform, Load) Tools Specially  S Q L  Server  I...
To Study E T L ( Extract, Transform, Load) Tools Specially S Q L Server I...Shahzad
 
MOUG17: SQLT Utility for Tuning - Practical Examples
MOUG17: SQLT Utility for Tuning - Practical ExamplesMOUG17: SQLT Utility for Tuning - Practical Examples
MOUG17: SQLT Utility for Tuning - Practical ExamplesMonica Li
 
MOUG17: DB Security; Secure your Data
MOUG17: DB Security; Secure your DataMOUG17: DB Security; Secure your Data
MOUG17: DB Security; Secure your DataMonica Li
 
Building the DW - ETL
Building the DW - ETLBuilding the DW - ETL
Building the DW - ETLganblues
 
"Data Dynamics: Trends & Patterns Revealed"
"Data Dynamics: Trends & Patterns Revealed""Data Dynamics: Trends & Patterns Revealed"
"Data Dynamics: Trends & Patterns Revealed"cakepearls17
 
Mutable data @ scale
Mutable data @ scaleMutable data @ scale
Mutable data @ scaleOri Reshef
 
Recipes 6 of Data Warehouse and Business Intelligence - Naming convention tec...
Recipes 6 of Data Warehouse and Business Intelligence - Naming convention tec...Recipes 6 of Data Warehouse and Business Intelligence - Naming convention tec...
Recipes 6 of Data Warehouse and Business Intelligence - Naming convention tec...Massimo Cenci
 
Mukhtar resume etl_developer
Mukhtar resume etl_developerMukhtar resume etl_developer
Mukhtar resume etl_developerMukhtar Mohammed
 
Spark SQL In Depth www.syedacademy.com
Spark SQL In Depth www.syedacademy.comSpark SQL In Depth www.syedacademy.com
Spark SQL In Depth www.syedacademy.comSyed Hadoop
 
4_etl_testing_tutorial_till_chapter3-merged-compressed.pdf
4_etl_testing_tutorial_till_chapter3-merged-compressed.pdf4_etl_testing_tutorial_till_chapter3-merged-compressed.pdf
4_etl_testing_tutorial_till_chapter3-merged-compressed.pdfabhaybansal43
 
Mukhtar_Resume_ETL_Developer
Mukhtar_Resume_ETL_DeveloperMukhtar_Resume_ETL_Developer
Mukhtar_Resume_ETL_DeveloperMukhtar Mohammed
 
Spark with Delta Lake
Spark with Delta LakeSpark with Delta Lake
Spark with Delta LakeKnoldus Inc.
 
NEAR-REAL-TIME PARALLEL ETL+Q FOR AUTOMATIC SCALABILITY IN BIGDATA
NEAR-REAL-TIME PARALLEL ETL+Q FOR AUTOMATIC SCALABILITY IN BIGDATANEAR-REAL-TIME PARALLEL ETL+Q FOR AUTOMATIC SCALABILITY IN BIGDATA
NEAR-REAL-TIME PARALLEL ETL+Q FOR AUTOMATIC SCALABILITY IN BIGDATAcsandit
 
NEAR-REAL-TIME PARALLEL ETL+Q FOR AUTOMATIC SCALABILITY IN BIGDATA
NEAR-REAL-TIME PARALLEL ETL+Q FOR AUTOMATIC SCALABILITY IN BIGDATA NEAR-REAL-TIME PARALLEL ETL+Q FOR AUTOMATIC SCALABILITY IN BIGDATA
NEAR-REAL-TIME PARALLEL ETL+Q FOR AUTOMATIC SCALABILITY IN BIGDATA cscpconf
 
Shane_O'Neill_CV_slim
Shane_O'Neill_CV_slimShane_O'Neill_CV_slim
Shane_O'Neill_CV_slimShane O'Neill
 
Db2 migration -_tips,_tricks,_and_pitfalls
Db2 migration -_tips,_tricks,_and_pitfallsDb2 migration -_tips,_tricks,_and_pitfalls
Db2 migration -_tips,_tricks,_and_pitfallssam2sung2
 

Semelhante a Oracle to Netezza Migration Casestudy (20)

To Study E T L ( Extract, Transform, Load) Tools Specially S Q L Server I...
To Study  E T L ( Extract, Transform, Load) Tools Specially  S Q L  Server  I...To Study  E T L ( Extract, Transform, Load) Tools Specially  S Q L  Server  I...
To Study E T L ( Extract, Transform, Load) Tools Specially S Q L Server I...
 
MOUG17: SQLT Utility for Tuning - Practical Examples
MOUG17: SQLT Utility for Tuning - Practical ExamplesMOUG17: SQLT Utility for Tuning - Practical Examples
MOUG17: SQLT Utility for Tuning - Practical Examples
 
MOUG17: DB Security; Secure your Data
MOUG17: DB Security; Secure your DataMOUG17: DB Security; Secure your Data
MOUG17: DB Security; Secure your Data
 
Building the DW - ETL
Building the DW - ETLBuilding the DW - ETL
Building the DW - ETL
 
Resume
ResumeResume
Resume
 
"Data Dynamics: Trends & Patterns Revealed"
"Data Dynamics: Trends & Patterns Revealed""Data Dynamics: Trends & Patterns Revealed"
"Data Dynamics: Trends & Patterns Revealed"
 
Mutable data @ scale
Mutable data @ scaleMutable data @ scale
Mutable data @ scale
 
Anil_Kumar_Andra_ETL
Anil_Kumar_Andra_ETLAnil_Kumar_Andra_ETL
Anil_Kumar_Andra_ETL
 
Recipes 6 of Data Warehouse and Business Intelligence - Naming convention tec...
Recipes 6 of Data Warehouse and Business Intelligence - Naming convention tec...Recipes 6 of Data Warehouse and Business Intelligence - Naming convention tec...
Recipes 6 of Data Warehouse and Business Intelligence - Naming convention tec...
 
Ibm redbook
Ibm redbookIbm redbook
Ibm redbook
 
Mukhtar resume etl_developer
Mukhtar resume etl_developerMukhtar resume etl_developer
Mukhtar resume etl_developer
 
Spark SQL In Depth www.syedacademy.com
Spark SQL In Depth www.syedacademy.comSpark SQL In Depth www.syedacademy.com
Spark SQL In Depth www.syedacademy.com
 
4_etl_testing_tutorial_till_chapter3-merged-compressed.pdf
4_etl_testing_tutorial_till_chapter3-merged-compressed.pdf4_etl_testing_tutorial_till_chapter3-merged-compressed.pdf
4_etl_testing_tutorial_till_chapter3-merged-compressed.pdf
 
resume_latest
resume_latestresume_latest
resume_latest
 
Mukhtar_Resume_ETL_Developer
Mukhtar_Resume_ETL_DeveloperMukhtar_Resume_ETL_Developer
Mukhtar_Resume_ETL_Developer
 
Spark with Delta Lake
Spark with Delta LakeSpark with Delta Lake
Spark with Delta Lake
 
NEAR-REAL-TIME PARALLEL ETL+Q FOR AUTOMATIC SCALABILITY IN BIGDATA
NEAR-REAL-TIME PARALLEL ETL+Q FOR AUTOMATIC SCALABILITY IN BIGDATANEAR-REAL-TIME PARALLEL ETL+Q FOR AUTOMATIC SCALABILITY IN BIGDATA
NEAR-REAL-TIME PARALLEL ETL+Q FOR AUTOMATIC SCALABILITY IN BIGDATA
 
NEAR-REAL-TIME PARALLEL ETL+Q FOR AUTOMATIC SCALABILITY IN BIGDATA
NEAR-REAL-TIME PARALLEL ETL+Q FOR AUTOMATIC SCALABILITY IN BIGDATA NEAR-REAL-TIME PARALLEL ETL+Q FOR AUTOMATIC SCALABILITY IN BIGDATA
NEAR-REAL-TIME PARALLEL ETL+Q FOR AUTOMATIC SCALABILITY IN BIGDATA
 
Shane_O'Neill_CV_slim
Shane_O'Neill_CV_slimShane_O'Neill_CV_slim
Shane_O'Neill_CV_slim
 
Db2 migration -_tips,_tricks,_and_pitfalls
Db2 migration -_tips,_tricks,_and_pitfallsDb2 migration -_tips,_tricks,_and_pitfalls
Db2 migration -_tips,_tricks,_and_pitfalls
 

Mais de Asis Mohanty

Cloud Data Warehouses
Cloud Data WarehousesCloud Data Warehouses
Cloud Data WarehousesAsis Mohanty
 
Cloud Lambda Architecture Patterns
Cloud Lambda Architecture PatternsCloud Lambda Architecture Patterns
Cloud Lambda Architecture PatternsAsis Mohanty
 
Cassandra basics 2.0
Cassandra basics 2.0Cassandra basics 2.0
Cassandra basics 2.0Asis Mohanty
 
Hadoop Architecture Options for Existing Enterprise DataWarehouse
Hadoop Architecture Options for Existing Enterprise DataWarehouseHadoop Architecture Options for Existing Enterprise DataWarehouse
Hadoop Architecture Options for Existing Enterprise DataWarehouseAsis Mohanty
 
Netezza vs Teradata vs Exadata
Netezza vs Teradata vs ExadataNetezza vs Teradata vs Exadata
Netezza vs Teradata vs ExadataAsis Mohanty
 
ETL tool evaluation criteria
ETL tool evaluation criteriaETL tool evaluation criteria
ETL tool evaluation criteriaAsis Mohanty
 
Cognos vs Hyperion vs SSAS Comparison
Cognos vs Hyperion vs SSAS ComparisonCognos vs Hyperion vs SSAS Comparison
Cognos vs Hyperion vs SSAS ComparisonAsis Mohanty
 
Reporting/Dashboard Evaluations
Reporting/Dashboard EvaluationsReporting/Dashboard Evaluations
Reporting/Dashboard EvaluationsAsis Mohanty
 
BI Error Processing Framework
BI Error Processing FrameworkBI Error Processing Framework
BI Error Processing FrameworkAsis Mohanty
 
Netezza vs teradata
Netezza vs teradataNetezza vs teradata
Netezza vs teradataAsis Mohanty
 
Change data capture the journey to real time bi
Change data capture the journey to real time biChange data capture the journey to real time bi
Change data capture the journey to real time biAsis Mohanty
 

Mais de Asis Mohanty (14)

Cloud Data Warehouses
Cloud Data WarehousesCloud Data Warehouses
Cloud Data Warehouses
 
Cloud Lambda Architecture Patterns
Cloud Lambda Architecture PatternsCloud Lambda Architecture Patterns
Cloud Lambda Architecture Patterns
 
Apache TAJO
Apache TAJOApache TAJO
Apache TAJO
 
Cassandra basics 2.0
Cassandra basics 2.0Cassandra basics 2.0
Cassandra basics 2.0
 
What is hadoop
What is hadoopWhat is hadoop
What is hadoop
 
Hadoop Architecture Options for Existing Enterprise DataWarehouse
Hadoop Architecture Options for Existing Enterprise DataWarehouseHadoop Architecture Options for Existing Enterprise DataWarehouse
Hadoop Architecture Options for Existing Enterprise DataWarehouse
 
Netezza vs Teradata vs Exadata
Netezza vs Teradata vs ExadataNetezza vs Teradata vs Exadata
Netezza vs Teradata vs Exadata
 
ETL tool evaluation criteria
ETL tool evaluation criteriaETL tool evaluation criteria
ETL tool evaluation criteria
 
COGNOS Vs OBIEE
COGNOS Vs OBIEECOGNOS Vs OBIEE
COGNOS Vs OBIEE
 
Cognos vs Hyperion vs SSAS Comparison
Cognos vs Hyperion vs SSAS ComparisonCognos vs Hyperion vs SSAS Comparison
Cognos vs Hyperion vs SSAS Comparison
 
Reporting/Dashboard Evaluations
Reporting/Dashboard EvaluationsReporting/Dashboard Evaluations
Reporting/Dashboard Evaluations
 
BI Error Processing Framework
BI Error Processing FrameworkBI Error Processing Framework
BI Error Processing Framework
 
Netezza vs teradata
Netezza vs teradataNetezza vs teradata
Netezza vs teradata
 
Change data capture the journey to real time bi
Change data capture the journey to real time biChange data capture the journey to real time bi
Change data capture the journey to real time bi
 

Último

Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processorsdebabhi2
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherRemote DBA Services
 
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...Zilliz
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodJuan lago vázquez
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...apidays
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...apidays
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDropbox
 
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
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxRustici Software
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAndrey Devyatkin
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsNanddeep Nachan
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024The Digital Insurer
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...DianaGray10
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...Zilliz
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native ApplicationsWSO2
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?Igalia
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...apidays
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
Apidays Singapore 2024 - Modernizing Securities Finance by Madhu Subbu
Apidays Singapore 2024 - Modernizing Securities Finance by Madhu SubbuApidays Singapore 2024 - Modernizing Securities Finance by Madhu Subbu
Apidays Singapore 2024 - Modernizing Securities Finance by Madhu Subbuapidays
 

Último (20)

Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
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...
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Apidays Singapore 2024 - Modernizing Securities Finance by Madhu Subbu
Apidays Singapore 2024 - Modernizing Securities Finance by Madhu SubbuApidays Singapore 2024 - Modernizing Securities Finance by Madhu Subbu
Apidays Singapore 2024 - Modernizing Securities Finance by Madhu Subbu
 

Oracle to Netezza Migration Casestudy

  • 1. Oracle to Netezza Migration Whitepaper This white paper provides key challenges faces and resolved and the approach followed in Oracle to Netezza migration for one of the fortune 100 Retailer. Asis Mohanty, asismohanty@gmail.com 11/11/2011
  • 2. Table of Contents 1. Introduction .......................................................................................................................................... 3 2. Migration Approach and Process .......................................................................................................... 3 3. Impact Analysis ..................................................................................................................................... 3 3.1 Database Impact Analysis: .................................................................................................................. 3 3.2 ETL/ELT Impact Analysis: ..................................................................................................................... 4 3.3 Applications and Reports Impact Analysis: ......................................................................................... 4 4. Design and Development ...................................................................................................................... 4 4.1 Database Objects Design and Development Considerations: ............................................................ 4 4.2 ETL/ELT Design and Development Considerations: ............................................................................ 5 4.3 Report Design and Development Considerations: .............................................................................. 5 5. History Load .......................................................................................................................................... 5 6. Testing ................................................................................................................................................... 6 7. Lesson Learnt and Recommendations .................................................................................................. 7 8. Appendix ............................................................................................................................................... 9 Automation Recommendations ................................................................................................................ 9 About the Author ...................................................................................................................................... 9 | Oracle to Netezza Migration 2
  • 3. 1. Introduction Every organization wants to have advance analytics, real time or near real time data which will help then in Business Intelligence and competitive Intelligence. High volume growth, scalability, Maintainability cost is the critical factor for any organization. Netezza provides a platform which is highly Scalable with availability, easy to integrate and maintain, cost effective (Pay per usage), Asymmetric Massively Parallel Processing architecture for performance. This white paper contains the approach followed, key challenges faces and resolved during the Oracle to Netezza migration program for one of the Fortune 100 Retailer. 2. Migration Approach and Process Oracle to Netezza migration has various SDLC process and steps as given below (Figure-1). Figure-1: Various steps involved in Oracle to Netezza Migration 3. Impact Analysis This is the most critical phase to identify all impacted Data Base Objects, ETLs and applications/Reports required for downstream. 3.1 Database Impact Analysis: Identify all impacted Data Base Objects such as Tables, Views, Stored procedures, Index, Partitions, Volumetric (Volume of Data to be migrated) to be migrated from existing Oracle platform to Netezza Platform. Create the impact inventory list of DB Objects. | Oracle to Netezza Migration 3
  • 4. Figure-2: Example of Existing DB & ETL Environment For example in the above figure (Figure-2), as part of Data Base Object impact identify all Tables, Views, Stored procedures, Index, Partitions, Volumetric being used in Stage, Warehouse and Mart environment. 3.2 ETL/ELT Impact Analysis: Identify all ETL mapping or Jobs and ELT scripts being used by all impacted DB Objects. Create the inventory list of all impacted ETL mappings or Jobs and ELT Scripts along with patterns. For example in the above figure (Figure-2), as part of ETL/ELT impact identify all ETL/ELT Scripts being used from Source to Stage, Stage to Warehouse and Warehouse to MART. 3.3 Applications and Reports Impact Analysis: Identify all Reports or applications which are using the Warehouse or Mart Data. Create the inventory list of all impacted Reports and applications. Figure-3: Example of Existing Reporting and Application Environment For Example as shown in the above figure (Figure-2) identify all Reports and various application which is using the Warehouse or Mart DB Objects (Tables, Views .etc). 4. Design and Development This section contains the design and development of Database Objects, ETL/ELT and Reports/Applications. 4.1 Database Objects Design and Development Considerations: • As-Is migration verses re-architecture of Data model is most critical factor. | Oracle to Netezza Migration 4
  • 5. Identify all Oracle Vs Netezza DB Object data type, Size and compatibility. For example the Date time stamp format, CLOB, LONG Data type etc. • Identify all Oracle Vs Netezza DB functions. Data type and function will be helpful while converting and Oracle SQL to Netezza SQL. • Netezza does not support Index where as it supports Distribution Key. While creating the new Netezza Tables the Oracle Table Index must be considered. 4.2 ETL/ELT Design and Development Considerations: • Identify different patterns of ETL Mapping/Jobs. • Identify all impacted ETL where Source connection to be modified from Oracle to Netezza. (Example: All Sources to Stage ETL will fall under this category. Refer Figure-1) • Identify all impacted ETL where Source and Target connection to be modified from Oracle to Netezza. (Example: All ETL Mapping/Jobs being used in Stage to Warehouse and Warehouse to Mart load) • Identify all embedded SQL queries used in ETL mappings/Jobs which need to be converted from Oracle to Netezza specific SQL Query. • Identify all ELT Scripts where embedded Oracle SQL queries to be converted to Netezza SQLs. • Make the Netezza specific changes in ETL Mappings/Jobs and do unit testing. Early Unit testing identifies various issues. 4.3 Report Design and Development Considerations: • Identify different patterns of Reports. • Identify all embedded SQL queries used in reports by report. • Identify all matrices which used Oracle specific Functions and Data type which needs to be converted to Netezza specific functions and data type. • Make Netezza specific changes in Reports. There might be some extra space, rounding, Special Character issue which needs to be addressed during development phase. 5. History Load History load from Oracle to Netezza can be done in following methods. • Extract Oracle Table to flat file using Oracle Database utilities. Load the flat file to corresponding Netezza table using NZ Load. (Always use Bulk load/insert/update utility which is faster) • Use the ETL tool to create one time mapping which will load history data from Oracle to Netezza. • Use Unix/Perl script to extract data from Oracle and load it to Netezza. • Use Third Party tool which uses the Bulk Read and Write mechanism to migrate history data from Oracle to Netezza. Wisdomforce Fastreader (www.wisdomforce.com/products- FastReader.html) supports the bulk/fast load from Oracle to Netezza. | Oracle to Netezza Migration 5
  • 6. Define the History data Delta if the history data volume is high, for example the existing Enterprise Data Warehouse has 6 Years worth of Data. Define two to three deltas each 2 Years worth of data to load. 6. Testing Testing is the most important phase to identify any issue. Following are few of the recommendations: • Database Object Testing o Compare the DB Objects (Oracle Vs Netezza) along with distribution Key. o Create automated script which can compare the DB Objects by querying System table scans. • SQL Testing o Compare the Oracle SQL results with Netezza SQL results. The SQL used in ETL, Scripts, Reports must be compared during the development phase. o There are third party tolls available for Oracle to Netezza SQL conversion such as Sqissql (www.swissql.com). Even automated Perl or UNIX scripts can be created to convert the SQL. • ETL Testing/Data Testing: o Run the existing Oracle ETL Job and new Netezza ETL Job. Compare the target Oracle table Vs Netezza table data. o If the Table volume is small, do a byte to byte data comparison of the data (Except Data Time Stamp fields) o If the volume is high use mathematical function (Min, Max and Average of additive field) along with count match. Do a sampling using Min/Max functions. o Created automated script to compare the Data. This script and method will be used for History data comparison too. • Report/Application Testing: o Run the report/Application in Oracle and Netezza environment. Compare the report manually or use Reporting environment specific tool to compare the result. For Example Microstrategy has ‘Microstrategy integrity manager’ which compares the reports. o There are third party tools available which compared Byte-to-Byte of .csv, pdf, xls, html reports, for example Beyondcompare (www.scootersoftware.com) . • Performance Testing: o Capture the ETL run statistics while running in Oracle environment and Netezza Environment. Compare the ETL load run statistics to know the performance. This must be done for the load window too. o Capture the Report run statistics for Oracle and Netezza environment. Compare the run statistics for Report performance in new Netezza environment. | Oracle to Netezza Migration 6
  • 7. 7. Lesson Learnt and Recommendations Following are few of the lessons learnt from the Oracle to Netezza Migration Project. POC o Do POC for early identification of issues and approach of Migration. Product Issue Resolution o There might be product related issue (Compatibility) which must be resolved quickly. o Netezza Special Characters (Ex: Trademark) might appear differently in reports. o Rounding of matrices in Reports. o Extra space in matrices, field of Reports. Resource o Cross skilled resource (resource having Oracle and Netezza Knowledge) Automation accelerators o Automation is the key to avoid any manual mistake during the Analysis, Development and Testing. Following Figure-4 shows the automations recommendations for Oracle to Netezza Migration. Figure-4: Automation Recommendations in various Phases Upgrade of Reporting and ETL Environment | Oracle to Netezza Migration 7
  • 8. o Upgrade of ETL environment or Reporting environment along with DB Platform migration is not recommended. Performance o Perform Performance testing for any early indication of system performance. Usage of Advance Feature of Netezza o Use the advance feature such as NZ Load utilities for better performance for high volume of data load. | Oracle to Netezza Migration 8
  • 9. 8. Appendix Automation Recommendations Tools to Convert Oracle to Netezza SQLs and DDLs: Create Unix/Perl script which converts the Oracle specific DDL or SQL to Netezza specific DDL/SQL. This script will use the Oracle Vs Netezza Data Type, Functions and patterns to convert. Tools to Convert the Oracle ETL to Netezza ETL: Create a tool which takes ETL xml as input and changes the Oracle tag to Netezza tag (connections, extra Tags) and converts Oracle embedded SQLs to Netezza SQLs. Data Testing Tool: Create a Unix/Perl script/tool which compares the Oracle Table data Vs Netezza Table data. Following are the two patterns: Low volume Table: Compare the byte-to-byte data. High Volume Table: Compare the Count of records, use mathematical functions such as Min, Max, Sum, Average .etc for numeric field. Use sampling of data comparison (Top 10 records using Rank functions .etc) About the Author Asis Mohanty has more than 11 Years of Industry experience on Data Warehousing and Business Intelligence field. He is a Certified Business Intelligence Professional from www.tdwi.org and Certified Data Management Professional from www.dama.org and Netezza Silver Certified Professional. For any query contact Asis Mohanty at asismohanty@gmail.com . | Oracle to Netezza Migration 9