Data Migration is a procedure where data is sent to and from in between computer storages. It is usable in making a new system or enhancing an existing one. Data Migration is where data gets derived from an old system and infused into a new system. C
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Data Migration Strategy for AFP Reengineering Project Version 1.0
Data Migration Strategy for AFP Reengineering Project Version 1.0
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Data Migration Strategy for AFP Reengineering Project Version 1.0
ABOUT THIS DOCUMENT Purpose The purpose of this document is to lay out the structure for data migration for an application reengineering project
Intended Audience This document is primarily for the use of consultants associated with Data Migration projects
Glossary TCS
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Tata Consultancy Services
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Data Migration Strategy for AFP Reengineering Project Version 1.0
Contents 1 INTRODUCTION..............................................................................4 Background...........................................................................................................4 Scope....................................................................................................................4 Assumptions.........................................................................................................5 Open Items............................................................................................................6 System Description.................................................................................................6 1.1.1 Source System Description............................................................................6 1.1.2 Target System Description.............................................................................6
Data Migration Strategy for AFP Reengineering Project Version 1.0
1
INTRODUCTION
Background ING has initiated a program to replace the existing Pension Fund Management applications running in Mainframe systems with the J2EE application. This project will replace these legacy systems with more flexible systems with up-to-date technological platforms and functionality. As part of the replacement, the data from the existing mainframe applications should be moved to the target Oracle database. ING has invited Tata Consultancy Services (TCS) Limited to prepare the data migration strategy document. This document details the various steps necessary for the life cycle of the data migration project that will feed the legacy data to state of the art “Oracle database”.
Scope The scope of this document is to define the strategy for the various phases of data migration. The phases in this data migration project are as follows. •
•
Preparation Stage o
Planning
o
Analysis
o
Design
o
Construction
o
Testing
Implementation Stage o
Pre-Implementation/Dry Runs
o
Implementation/Production data migration
This document also addresses •
Tools
•
Cutover Considerations
•
Proof of Concepts
•
Guidelines
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Data Migration Strategy for AFP Reengineering Project Version 1.0 •
Special Requirements
•
Change Control and Traceability
•
Challenges and Risks
•
Roadmap
Assumptions •
Target data model will be developed iteration wise and so may undergo several changes. So source data analysis has to be done based on evolving target data model. Once the target data model is baselined unmapped fields in source will be further analyzed to confirm whether it can be actually ignored.
•
ING will define the strategy, analysis, design and construct scripts for Data Cleansing. TCS will support and complement this.
•
The production cut-over window for implementation is expected to be 48 hours over a weekend. This could change based on the volume of the record, relationship between tables which defines the order of migration
•
The source inventory and corresponding data are based on the assumption that the go-live date will be on a weekend that doesn’t fall on a month-end.
•
The current strategy is to extract the data from mainframe source using Informatica power exchange and use Informatica powercenter to transform and load Oracle target database
•
Existing master data will not be updated during migration window.
•
Data to be migrated is frozen before the start of the migration
•
There will not be any explicit lock on the data to be migrated by any of the application accessing the data during the outage window
•
The current existing model is base lined and assumed to be 100% complete.
•
The scope of data migration project is to migrate only the data that will be accessed by the target application system
•
ING will provide the list of concurrent activities during the outage window. The impact of it will be studied and the outage window size will be decided
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Data Migration Strategy for AFP Reengineering Project Version 1.0
Open Items •
Need for migrating the historic and back up data in tapes which are not going to be accessed by the target application, target table and the strategy for the same will be analyzed by ING and discussed and finalized. Both ING and TCS will discuss and resolve on the extra effort involved and the impact on the plan.
•
The possible solution could be one time migration either through regular interface or using scripts and then incremental migration using regular interface.
•
The scope of migrating the data present in tapes which are rarely used by the application needs to be finalized. The feasibility of the target application system accessing the same tapes needs to be studied
•
Risk analysis, Implementation details, Roll back strategy, handling of exceptions are yet to be finalized.
•
The migration strategy of back up data when the layout is different is yet to be finalized.
System Description The scope of the data migration project is to migrate the data from the existing mainframe system to ORACLE Database. The System architecture related to these systems is:
1.1.1
Source System Description System
1
Operating
Software
System
Platform
IBM Mainframe
COBOL, VSAM, CICS
Database
DB2
OS/390
1.1.2
Target System Description System
1
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Operating
Software
System
Platform
UNIX
Java/J2EE
Database
Oracle
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Data Migration Strategy for AFP Reengineering Project Version 1.0
2
Migration Approach
Introduction Data migration is process by which data is moved from source databases to target databases. Currently source data is in VSAM and flat files and DB2 tables in Mainframe. This data needs to be moved to target databases in Oracle. The various phases involved in this endeavor are as described below. •
•
Preparation Stage o
Planning
o
Analysis
o
Strategy Definition
o
Design
o
Construction
o
Testing
Implementation Stage o
Pre-Implementation/Dry Runs
o
Implementation/Production data migration
The preparation stage will be used to develop data migration strategy and the data migration programs. This will be tested in non-production environment. All the factors that influence Implementation stage like business requirements, data volumes and infrastructure constraints should be taken into account in the preparation stage. This stage is very vital in the success of any data migration program. This stage will be done in seven iterations and will be synchronized with the iterations in ING Core AFP Project. The actual execution of the data migration programs on the production data will be done in implementation stage. Implementation is planned in two phases. Preceding each implementation will be a Pre-Implementation or dry run to test the data migration scripts with production data in simulated test environment.
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Data Migration Strategy for AFP Reengineering Project Version 1.0
Planning All planning activities required for data migration will be done in this phase. Other activities that will be taken up in this phase will be the finalization of source inventory, creation of standards, strategy for data analysis, cleansing, implementation and selection of tools.
Assumptions •
Project Plan is available
Activities
SL
Category
Task
1 2
Planning Planning
Conduct kick-off meeting for the phase Prepare detailed plan for the strategy documentation
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Schedule (WeekDay)
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Data Migration Strategy for AFP Reengineering Project Version 1.0 SL
phase Prepare detailed plan for the Iterations Consolidate source inventory. Creation of standards. Identify and evaluate tools for data migration Set up environment for next phase Identify candidates for Proof of Concept(POC) Document results of proof of concept (PoC) for identified candidates. Finalize the list of tools & environment setup definitions Identify development/testing environment. Identify, document and obtain approval for the configuration and reference data requirements Define Acceptance Criteria
Deliverables • • Tools
• Updated Project Plan Source Inventory list Inventory List for POC
The tool required for various phases of data migration has been identified during POC and the list is given below. Sl 1 2 3 4 5
6 7 8 9 10
Process Extraction
Sub-process VSAM DB2
File Comparison
Tools Informatica Power Exchange Informatica Power Exchange DFSORT,COBOL
Transformation
Informatica Power Center, COBOL
Loading
Informatica Power Center Source Analyzer and Warehouse
Data Analysis
Designer << ING >> <> << ING /TCS >> << ING >> Manual/SQL/Excel
Audit
Informatica
Validation
Informatica Reports
Reporting
Informatica
Scheduling
Informatica Power Center Workflow manager
Cleansing
Pre Extraction Extraction Transformation Target Database
Analysis Detailed analysis of source and target databases will be carried out in this phase. Data analysis will be carried out to understand the contents of source data and documented. Data cleansing requirements are documented and criteria for extraction audit and validation of source data are agreed upon.
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2.1.1
Analysis of Source Inventory
The VSAM files, DB2 tables and flat files (structures, data and copybook layouts) are assumed to be base lined for inventory purposes. As Archive data migration will take place if archives are in current source format, their inventory needs to be documented. When data is migrated from VSAM and DB2 to Oracle, the data that needs to be migrated and the data that is left in source because of duplications etc. need to be identified as part of scope analysis. Sl 1
Description No of VSAM files in
Quantity
Link for the list
667
inventory
List of VSAM files 2
No of DB2 tables in
313
inventory
list of tables 3
No of VSAM files to be migrated
4
No of DB2 tables to be migrated
5
No of VSAM backups
6
No of DB2 backups
7
Volume of data
8
Size of DB2 database
25GB
9
Size
245GB
of
VSAM
database is 10
No of DB2 Tables with Reference Data
11
No of VSAM files with Reference Data
12
No of DB2 tables with transaction data
13
No of VSAM files with transaction data
14
No of DB2 tables with Master data
15
No of VSAM files with Master Data
16
No of Databases in the system
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2.1.2
Source Data Analysis Data analysis for all the source entities needs to be documented. This will be done iteration wise based on the evolving target data model. ING will provide the field description, ranges, and domain values for all the fields. This will help us in deciding whether an unmapped source field can be ignored or not. The following excel format is agreed upon and ING and TCS will jointly complete for all the VSAM files and DB2 table attributes and their descriptions.
Field Analysis Template.xls
As part of Standardization measure, the domain values of the source database may have to be standardized for target (based on international standards, ING specifics or new application design). Such domain values should be agreed upon and signed off well in advance, as part of analysis phase. The analysis should also cover the following aspects of source and target data model, -
Business dependencies between the entities
-
Understanding of multiple record layouts
-
Technical dependencies between the entities
-
Database specific constraints that may have potential impact on the data conversion (for example the impact of migration of COMP-3, OCCURS, REDFINES, etc. from a mainframe environment to Unix/Oracle)
2.1.3
Data Cleansing Based on the data analysis, the fields that need to cleansed should be identified. Data cleansing is required to ensure that only accurate, consistent and complete data is loaded into target database. Data cleansing will be required for -
Junk Characters/Characters not supported by Oracle like nulls
-
Invalid Domain Values
-
Domain value standardization
-
Values not within Range of the field
-
Format consolidation (eg, dates , amount fields)
-
Referential integrity (eg, affiliate RUT in any transaction table should also be present in affiliate master)
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Data Migration Strategy for AFP Reengineering Project Version 1.0 The cleansing requirements should be documented clearly, stating the present conditions and the proposed corrective action. The field analysis template itself can be used for documenting cleansing requirements. Data cleansing requirements and routines will be provided by ING. We also need to identify at what stage the cleansing rules can be applied (extraction , transformation or load)
2.1.4
Extraction programs The extraction rules will be based on the business need and the data required for each iteration. Extraction rules to extract data from the source (VSAM / DB2) needs to be defined jointly by ING and TCS and the same will be incorporated in the extraction programs.
2.1.5
Analysis of Target Database Once the target database design is completed and baselined the following table will be updated
Sl
Table Name
Total
Not
Date
Unique Key
Null 1
Total
Assumptions •
Updated Project Plan is available
•
Finalized Source inventory list for current iteration is available
•
Target data model for current iteration is available
Activities
SL
Category
Task
1 2 3
Analysis Analysis Analysis
4
Analysis
5
Analysis
6
Analysis
Document base-lined source inventory Categorize the source entities in “Reference, Transaction and Master” Identify candidate field. Analyze and understand the domains, range/set of valid values of the identified candidate fields. Analyze the source and target data models for cardinality,optionality and relationships Understand the record identifiers for data stores with multiple layouts (Internal to COBOL programs – may be hidden in the data definition) Understand the impact of environment specific constructs like compressed data items (Comp variables in COBOL), repeating data groups (Occurs clause in COBOL) , reusage of storage space(Redefines and value clause in COBOL), date structures(date
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Data Migration Strategy for AFP Reengineering Project Version 1.0 SL
Category
7
Analysis
8
Analysis
9 10
Analysis Analysis
11
Analysis
12 13
Analysis Data Cleansing
Task
Schedule (WeekDay)
may not have century part, maybe Julian date) Identify System Dependencies (eg, Character set in mainframe is EBCDIC while it is ASCII in UNIX. Date format is Date + Time in target Oracle while it may not be the case in source) Classify the entities that “must be converted for the target”, entities that “must be only used for transformation”, entities that are “redundant”, entities that are “not required for target”, entities that are “in question”. Identify the owner for the entities that are “in question” Finalize and document the criteria for data extraction Identify the right source based on the discussion with maintenance and business team. Right instance of the data. Define general flow for migrations process (VSAM extract flat files versus master files) Review the standards for data mapping from target to source. Identify and document data cleansing requirements.
Deliverables • •
Data analysis findings Updated Inventory list
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Data Migration Strategy for AFP Reengineering Project Version 1.0
Challenges •
It is essential to baseline both source and target data models to reduce rework. However it is not practical when analysis is done in iterations. It is vital that any changes to the source and target baseline should be informed to the data migration team immediately. The changes should be immediately analysed and data analysis document updated.
•
All environment specific constructs should be identified. It should be verified whether the informatica tool will handle it. If the tool does not handle it suitable solutions should be identified for migrating them to target. During POC we have identified the following list
o
Character set in mainframe and Unix are different. Mainframe uses EBCDIC while Unix uses ASCII. Informatica power center is able to handle this conversion.
o
Occurs , and Redefines can be handled by Informatica power center.
o
For Occurs depending we have to manually alter the data to make it the maximum number before loading in informatica power center. Usage of Power Exchange will be able to address this problem.
o
Loading of DB2 null data into Oracle was found to be a problem. An extra field was manually added before every column that may contain null. This is to hold the null indicator. Usage of Power Exchange will be able to address this problem
o
In Oracle Date is defined as YYYY-MM-DD-Time but in Vsam files it can be of any combination. A transformation rule was written in power center to transform source date to target format
o
We could not find any Julian dates in POC. So a strategy for transforming it is not identified. Further analysis to be done to check if ING core AFP system uses Julian date or not.
Strategy definition The various strategies related to data migration are defined in this phase. The data migration strategy document is prepared in this phase. A proof of concept has been done to validate the migration strategy for extraction, transformation and load. This document will be updated with best practices and lessons learnt after each iteration.
2.1.6
Proof of concept
The migration of following VSAM files and DB2 tables will be the scope for the Proof of concepts. The extraction, transformation and load will be done for these sample data in the development environment. VSAM 1. 2. 3. 4. 5. 6. 7. 8.
The proof of concept is completed and the following is proved 1. 2. 3. 4. 5. 6.
Extraction of VSAM file to flat file and ftp to text file Extraction of DB2 to flat file and ftp to text file Mapping and transformation between source and staging tables using informatica power center Mapping and transformation between staging and target tables using informatica power center Loading of VSAM and DB2 extract flat file into staging tables using informatica power center Moving data from staging database to target database by executing the mapping and transformation scripts in informatica power center workflow 7. Transfer of scripts and integration between offshore and onsite
Define data migration strategy Define testing strategy Define Implementation strategy Create data migration strategy document Do proof of concept Review the data migration strategy document Presentation to selected audience Obtain sign-off from Clients on the strategy documents
Schedule (WeekDay)
Deliverables •
Data Migration Strategy Document
Design The objective of this phase is to define a set of rules to transform data from source to target. The mapping rules are based on source and target data structure and domain information provided by ING. The mapping repository is created to maintain list of mapping rules. The following template is used for mapping repository
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Data Migration Strategy for AFP Reengineering Project Version 1.0
"Mapping repository template.xls"
2.1.7
Mapping rules Direct mapping Identify target fields with one to one relationship with source and specify the source value to be used Transformation rule mapping For remaining target fields, document transformation rule in detail, specifying source fields and computation clearly. Default value mapping Identify target fields that have no relation with source and specify the default value to be populated. Functional and design people need to be involved in taking these kinds of decisions. Unmapped fields in source Unmapped fields in source will be analyzed and risk of not migrating these data will be estimated. This analysis will be done only if the field is unmapped even after all iterations are completed.
2.1.8
Data Format – Source to Text File VSAM to Flat file (Any COBOL Layout to Free format Layout) All the following conversions will be done by Informatica Power center itself based on the standards VSAM DATA TYPE
Flat File
COMP-3
Free format Signed Edited text numeric field
COMP-2
Free format numeric display field
Signed Decimal
Sign edited text field
COMP
Free format Signed Edited text numeric field
Numeric
Numeric
REMARKS
DB2 to Flat file DB2 Data Type
Flat file
REMARKS
SMALLINT
PIC -9(4)
1 <= n <= 15
INTEGER
PIC -9(9)
16 <= n <= 31
DECIMAL (p,s) or
PIC –9(p).9(p-s)
p – precision
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Data Migration Strategy for AFP Reengineering Project Version 1.0 NUMBER (p,s)
PIC 9(p).9(p-s)
s – scale 1 <= p <= 31 and 0 <= s <= p
CHAR (n)
2.1.9
PIC (n)
1 <= n <= 255
Non-key source fields becoming key fields in target For the source data where the non-key fields become key fields in target, proper integrity and the order of migration should be performed so that the complete information is retained without any data inconsistency and data redundancy. Unique & non-unique constraint will be analyzed and the proper validation technique will be ascertained, so that there is no undefined information in the system. Proper indexes will be defined in the target system so that the access time is within the SLA.
2.1.10 Date and time stamp / load date fields and user id Date will be ORACLE format of mm/dd/ccyy with default value set by the business. Time stamp will also be default ORACLE timestamp. For load dates field and update user id field the date when the loading/migration is done and a default User Id will be assigned.
Assumptions •
Baselined source and data model for the current iteration is available
•
Data analysis findings is available
Activities
SL
Category
Task
1 2
Design Review
Create mapping repository Review the mapping repository
Schedule (WeekDay)
Deliverables Mapping repository
Construction The objective of this phase is development of data migration suite. This phase consists of creation of extraction , transformation and load scripts for data migration.
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2.1.11 Data migration approach The data migration would occur in 2 stages. In the first stage data will be migrated from the source systems to the staging ORACLE database in the same layout as the file layouts. In the second stage we will move the data from the staging database to the target ORACLE database. Following diagram depicts the data migration steps:
Source 2.1.12 Source System (VSAM / DB2) to Staging database (Oracle)
2.1.12.1 Extract The strategy of extraction given here is without Informatica Power Exchange. The impact of having Informatica Power Exchange on extraction process will be analyzed and the same will be updated in this document after iteration 1. The data from VSAM file and DB2 table is extracted by the following steps Steps for extraction of VSAM files 1. REPRO JCL’s to extract the VSAM files into flat files will be written. Temporary variable to be used in the JCL and the name of the file to be hard coded at only one place. 2. The JCL should also contain a step to FTP the flat file in binary format to FTP Server. 3. Logical grouping of the files in one JCL should be determined and standardized
JCL COBO
Steps for extraction of DB2 tables 1. DB2 Unload JCL’s to extract the DB2 tables’ data into flat files will be written. Temporary variable to be used in the JCL and the name of the table and the load file to be hard coded at only one place 2. The JCL should also contain a step to FTP the flat file in binary format to FTP Server. 3. Logical grouping of the tables in one JCL should be determined and standardized
VSAM
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Data Migration Strategy for AFP Reengineering Project Version 1.0 Pre processing - Informatica Power center 1. The COBOL format programs with copybook names with “.CBL” extension will be written 2. The copybooks in the same folder with ".CPY” extension will be copied 3. The source descriptions will be defined in Informatica Source Analyzer 4. Using the source descriptions the target table descriptions (Staging Oracle db) will be defined in Informatica Warehouse designer 5. The staging target tables are created in the database Note: Any compatibility issues between Mainframe data and loading data into Infomatica Power center will be analyzed and the extraction process may have an impact. The document will be updated accordingly. The following are also done as part of the extraction process • Some degree of data cleansing activity will be performed as a part of the extraction process. These will include replacing junk characters by blanks, substituting zero for a numeric field. • Reporting mechanism on each extraction process will also be developed. This will report the details of the rejected records, bad records, excluded records, and bad data. • Transferring the text files from mainframe to UNIX environment will be performed by typical ftp. The file to be transferred will be split into number of files and split files will be compressed by PKZIP software. The compressed files will be transferred through UNIX box to Informatica server. In UNIX the files will be decompressed with the help of PKUNZIP software and will be loaded into Informatica server. 2.1.12.2 Transform The following are the steps that needs to be followed in informatica power center 1. The mapping rules are defined and linked between the source and target in the mapping designer 2. The transformations rules are designed and scripted in Transformation developer 3. Some degree of data cleansing activity will be performed as a part of the extraction process. 2.1.12.3 Load The following are steps involved in loading the data from VSAM and DB2 to staging oracle database. 1. The reusable sessions are created which will define the mapping 2. The workflow is created in the Workflow Manager which will define which session needs to be executed and sequence and time of execution 3. The workflow is executed to load the data from the source to the staging database. The number of workflows will be decided based on the sequence of the migration 4. The referential integrity will not be maintained in this database. 5. Indexes will be created based on the performance requirements
2.1.13 Staging database (Oracle) to Target database (Oracle)
2.1.13.1 Extract
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Data Migration Strategy for AFP Reengineering Project Version 1.0 The data from staging database is not extracted but it is physically represented as mapping and transformation and the Informatica Power center picks it up from staging database to the target database. Pre processing - Informatica Power center 1. The source descriptions (Staging database needs to be defined as source )will be defined in Informatica Source Analyzer 2. Using the logical target database design the target table descriptions (Target Oracle db) will be defined in Informatica Warehouse designer 3. The target tables will be available in the database already created by the application team 2.1.13.2 Transform The following are the steps that needs to be followed in informatica power center 1. The mapping rules are defined and linked between the source and target in the mapping designer 2. The transformations rules are designed and scripted in Transformation developer 3. Cleansing activities will also be done here. 4. The data will be ported to Informatica server through UNIX box. 2.1.13.3 Load The following are steps involved in loading the data from staging oracle database to target oracle database. 1. The reusable sessions are created which will define the mapping 2. The workflow is created in the Workflow Manager which will define which session needs to be executed and sequence and time of execution 3. The workflow is executed to load the data from the staging database to target database. 4. The referential integrity will be maintained in this database and hence the data loading will have to be performed based on the defined loading sequence. 5. Indexes will be created based on the Target database schema requirements. 6. Additional indexes may also be necessary to improve the performance requirements Note: For the input source data that does not require cleansing in staging will be migrated directly to the target. The analysis of cleansing for the files plays a major role in deciding this strategy. This approach will save a lot of time during the implementation
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2.1.14 Cleansing
2.1.14.1 Pre Migration (Production Phase)
This process will cleanse all the non voluminous and business non-critical data. The main purpose of cleaning the data directly in production is to avoid any cleaning activities of the similar data in the subsequent migration. Therefore the data, which is cleaned, will remain clean throughout the different phases of migration. The types of data that will be cleaned are: Name: These are entity properties data like, Customer name, Customer address, Dealer name, Bank name DSSO Name. Comment: These are entity attributes data, like, description, Comments, Attention fields and any other fields that are not participants in business validation.
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Appropriation: These are any standardization data like, customer name standardization, address standardization.
2.1.14.2 Extraction Process This level cleansing process is to clean voluminous business non-critical data. This also includes the data that are routine and static clean. The data that are cleaned in this process are:
Technical Data (does not need any Business intervention) Default Data (Handling of Space, Null, Date) Cleaning of junk character User identified incorrect data
2.1.14.3 During Transformation The major part of the data cleansing rules is applied in this stage. Cleansing at transformation include while transforming data from source to staging and also while transforming staging to target. These include: Inconsistency in Business Domain value (ZIP code, RUT) Unmapped data 2.1.14.4 In staging Some level of cleansing will be done on the data present in the staging table. Either Java programs or SQL will be written to clean the data present in staging. Note: If cleansing will be done during transformation and staging, then ING and TCS has to analyze the impact on the effort involved and the changes to the plan.
2.1.15 Audit trail data, summary data Audit trail data will contain total number of records migrated and summation of any numeric field. This will be re-validated in the target system to confirm the correctness of File transfer. Record rejection, record appropriation can also be included in the Audit data. Summary data will contain all type of key information for migration of a particular entity. For example for Affiliate Master, RUT, Name of the affiliate and any other information that are critical to the entity will be considered.
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2.1.16 Reports
Exceptions reports will be analyzed, gap will be studied and new/changed data cleansing definition will be incorporated. Both the rule definition and programs will be configured. Use of any reporting tool will be analyzed and finalized
2.1.17 Special Requirements Any special requirements that arise as part of Data Analysis will be documented and updated frequently
Assumptions •
Baselined source and target data model for the current iteration is available
•
Mapping repository available
•
Data cleansing requirements available
Activities
SL
Category
Task
1 2
Construction Construction
3
Construction
4
Construction
5 6
Data Cleansing Validation
7
Audit
Extraction routines to be written for extracting data from mainframe Source and target definitions to be created in informatica power center using information from source and target Data model Mapping and transformation rules are created in informatica power center based on the information collected in mapping repository Session and workflows are created using power center for executing the mapping and transformation rules. Data cleansing rules are also written if required in this stage Finalize and document the criteria for data validation, to the verify the correctness of migration (Business Validation) Finalize and document the criteria to verify the completeness of migration (Technical Validation)
Schedule (WeekDay)
Deliverables • • • • •
Extraction routines Source and target definitions Mapping and transformation rules Load routines (Sessions and workflows) Audit and validation routines
Testing This phase comprises of testing the data migration suite for each iteration. Testing will check all the transformations / mappings / workflows / cleansing / audit and validations. Individual test cases need to
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Data Migration Strategy for AFP Reengineering Project Version 1.0 be prepared for testing out various functionalities. The following matrix illustrates the broad areas that the test cases will pertain to –
Attributes
Measurement plan
Remarks
Business
1. Identify all business important fields that can be used for summation checks for data extracts and in target tables 2. Perform summations on the identified fields in incoming data files and match the sum 3. Perform summations on identified fields in the ODS and match with that of incoming data All data elements to be mapped to business rules
Business important fields that can be used for checksums need to be requested to ING users and it should be included in the extracts.
All data elements and relationship should pass associated
should be provided by
business rules
ING users and TCS will
(E.g.- Data attribute can contain only one out of a set of
do a feasibility analysis
Outlier
values) 1. Identify all integrity constraints 2. All data must pass through associated integrity constraints(Eg- There can be no detail records in the absence of a master) Identify the Minimum, Maximum and default values for
for the same Integrity constraints should be specified by ING users and verified and validated Min, Max and Default
conditions
data attributes
values should be
All data attributes should contain a valid value
provided in and verified
Raise alert when invalid values are detected 1. Identify all steps which need to generate alert (egInvalid incoming data, failed integrity checks, outliers, load 2. Raise alerts
important fields for checksum
Business rules
Integrity checks
All Business rules
Correctness of
Identify fields involving complex calculations
and validated Any specific alert requirements should be specified in the ETL strategy to incorporate the same in development ING users to specify
calculations
Recalculate once loading is complete
critical fields involving
Match with previously calculated values
complex calculations
Alert mechanism
and the same should be Identify data to be captured in audit trail ( Eg- File name,
incorporated Any specific audit
number of records on file, Records inserted from file,
requirements should be
Capture audit attributes during load process and store in
specified in the ETL
Audit table
specs and will be
Incoming data
Identify summary information for input data to be sent
incorporated Incoming control
summary
in additional file (File name, number of records, date
summary file
and
specification to be
Perform checks on incoming data ( Match record
provided and same
Audit trail
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Data Migration Strategy for AFP Reengineering Project Version 1.0 Attributes
Measurement plan
Remarks
count in control file and actual number of files
should be incorporated
received, match
in the extract
Business test
Raise alert in case of mismatch TCS will write 40 to 50 test cases to check the
Information on critical
cases
bussiness scenario for audit. Bussines test cases
reports to be provided
could be writing SQL queries to get the data from
by ING
target and verify it using existing mainframe data. The choice of the business criteria can be identified from the legacy reports or may be provided by ING
2.1.18 Validation The following are the validations that will be performed to ensure the correctness of the data migrated. No
Category
Source
Destination
1
Number of physical record
All Entities
All Entities
Criteria
2
Sum
Field-1; Table-1
Field-1; Table-1
deviation justified Exact match or
Field-2; Table-2
Field-2; Table-2
deviation justified
Exact match or
3
Sum against a branch
Field-1; Table-1 Field-1; Table-1
Field-1; Table-3 Field-1; Table-1
Exact match or
4
Total number of active
Field-1; Table-1
Field-1; Table-1
deviation justified Exact match or
5
affiliates Total number of deceased
Field-1; Table-1
Field-1; Table-1
deviation justified Exact match or
6
affiliates Totals
Field-1; Table-1
Field-1; Table-1
deviation justified Exact match or
7
Status Fields
Group By count
Group By count
deviation justified Exact match or
8
Null fields
Count Field-1
Count Field-1
deviation justified Exact match
9
Blank Fields
Count Field-2 Count Field-1
Count Field-2 Count Field-1
Exact match
10 11
Not Null Fields Duplicate Rows
Count Field-2 Count Field-1 Table-1
Count Field-2 Count Field-1 Table-1
Exact match Exact match
12 13
Deleted Rows Key fields (RUT, Folio
Group by range
Group by range
Justify Exact match
14 16
Number) Name Fields Round off
Compare by key Verify correct
Compare by key Verify correct
Exact match Exact match
Table-2
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Table-2
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Data Migration Strategy for AFP Reengineering Project Version 1.0 No
Category
Source
Destination
17
Truncation Error on Identified
decimal places Correct truncation
decimal places Correct truncation
18 19
Field Exceptions Bad Records
Defined Identify
Defined Defined
Criteria
Exact match Validate Validate
2.1.19 Audit Audit rules are expected to be defined by the ING Core AFP Data Migration team in the following format. The auditing should be done based on the reliable reports from business. Business reports will be provided by ING to be used for auditing
No
Category
Source
Destination
Criteria
2.1.20 Testing Lifecycle •
The construction and unit testing will be done by TCS onsite/offshore team after the finalization of design document. This will be going forward basis.
•
The Migration components will be delivered by TCS upon completion of construction/unit testing.
•
The components will be validated by ING Data Migration team.
•
After this primary validation a bigger Revolution Unit Testing will be performed by ING data Migration team after ING Core AFP application is delivered. TCS will support the testing.
•
After the completion of this phase Performance Testing and Revolution Unit Testing will be performed in parallel. TCS will support these two types of testing.
Assumptions •
Data migration suite available (Extraction, transformation and load routines)
•
Audit and validation routines available
•
Source Data for migration is available
Activities
SL
Category
Task
1 2
Testing Audit and validation
Test the data migration suite Run the Audit and validation scripts and verfy the completeness and correctness of data migration.
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Schedule (WeekDay)
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Data Migration Strategy for AFP Reengineering Project Version 1.0
Deliverables Tested data migration suite
Pre-Implementation(Dry Runs) Pre-Implementation or Dry run is the simulation of production implementation in test environment. The objective is to understand the complexities during implementation, in terms of the window for data migration, infrastructure requirements and to fine tune the programs and implementation procedures if required. Data migration implementation is planned in two phases. So Preimplementation run will also be done for each of these phases. This will be done by ING Data Migration team and the Business Capability Team. TCS will support this testing. •
The strategy describes the go-no-go checkpoints after different stages and a Root cause Analysis (RCA) will be done for each checkpoints. Based on the RCA the Data Mapping, Data Model, Design, Migration Design, Migration component codes will be revisited and necessary actions will be taken.
Assumptions •
Tested Data migration suite available for the current implementation phase
•
Test environment that is simulated based on production is available
•
Source Data for pre-implementation dry run is available
Activities
SL
Category
Task
1 2
Pre implementation Audit and validation
3
Performance
Test the data migration suite Run the Audit and validation scripts and verfy the completeness and correctness of data migration. Performance tuning of data migration suite if required
Schedule (WeekDay)
Deliverables Full volume Tested data migration suite
Implementation Implementation phase comprises of activities for implementing the actual production data migration. The implementation of data migration depends mainly on the implementation window, volume of data to be migrated and the type of data. On further analysis on data and discussions the implementation strategy will be finalized. As of now the implementation of phase 1 roll out alone is considered. Based on further analysis the document will be updated for the implementation of phase 2 roll out
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Data Migration Strategy for AFP Reengineering Project Version 1.0 All the back up data will be migrated two weeks ahead and the reference data will be migrated one week ahead and the transaction and master data will be done in one weekend before live. The same is depicted in the figure below
tt 28 28 S S
th 17 17 th
Sep Sep -nd 22 22 nd Oct Oct
Points to be considered to adopt this approach: 1. All the back up data be extracted in 48 hours 2. All the reference data be extracted in 48 hours 3. All the transaction, master and catch up reference data be extracted, cleaned, transformed and loaded in 48 hours 4. It is assumed that data migrated on first weekend is not going to change at all 5. It is assumed that data migrated on second weekend (reference data) may not change in one week 6. Additional effort is involved in doing catch up for reference data 7. Testing of the data will be in the parallel run time 8. Incremental migration may be required for reference data Note: Data cleansing implementation is not considered. The cleansing implementation will have impact on the strategy defined here and this document will be updated based on the cleansing implementation Following source files of ING Core AFP System split according to the modules and the best strategy and time for migrating these data will be tabularized in the following format once the approach is finalized.
Back Back up up files files Migration Migration
# The following no of records and database sizes are based on the available information in production. Sl
System
Data
# ( M )
1
Contracts
2 3
Contracts Contracts
TCS Confidential
Transa ction Master Refere
Volu me (GB)
Vertical Split (By Design)
Horizont al Split
Special Treatment
Link for the list of Tables/Files
Proposed date of Migration
Page 29 of 36
R R
Data Migration Strategy for AFP Reengineering Project Version 1.0 Sl
System
Data
# ( M )
4
7
Accounts -1 Accounts -1 Accounts -1 Claims -1
8 9
Claims -1 Claims -1
10
13
Accounts -2 Accounts -2 Accounts -2 Claims -2
14 15
Claims -2 Claims -2
16
Pensions
17 18
Pensions Pensions
19
Bonds
20 21
Bonds Bonds
5 6
11 12
Volu me (GB)
Vertical Split (By Design)
Horizont al Split
Special Treatment
Link for the list of Tables/Files
Proposed date of Migration
nce Transa ction Master Refere nce Transa ction Master Refere nce Transa ction Master Refere nce Transa ction Master Refere nce Transa ction Master Refere nce Transa ction Master Refere nce
Assumptions •
Full volume Tested Data migration suite available for the current implementation phase
Activities
SL
Category
Task
1 2
Implementation Implementation
Backup the source data to be migrated if required Backup the target data in phase 2 implementation as target database would be operational between phase 1
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Schedule (WeekDay)
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Data Migration Strategy for AFP Reengineering Project Version 1.0 SL
Category
3
Implementation
4 5 6 7
Implementation Audit and validation Implementation Implementation
8
Implementation
Task
Schedule (WeekDay)
and phase 2 implementation Execute the data migration suite (Extraction, Cleansing, transformation, Load scripts) Resolve and reconcile any data errors Execute the Audit and validation scripts and verfy the completeness and correctness of data migration. Resolve and reconcile any errors encountered Invoke fallback procedures if unable to Resolve and reconcile any errors encountered Make the target application go live
Deliverables Data migrated to target table as per data migration requirements.
2.1.21 Cutover Considerations
Sl 1
Candidate Master Files
Issue Weekend cutover will not have any issue. Go-live on weekday will require the
2
Quarterly Back up
files to be kept on hold. These type of files which are not going to be modified can be migrated two
3 4
files Contracts Deceased Data
5
Closed Claims
6
Inactive affiliate
6
DB2 tables
7
Maintenance
8
Changes Regulatory
9
Changes Final Backups prior to migration
weeks ahead All the contracts related files should go live in the month end only Data related to deceased can be migrated well in ahead as they are not going to be modified All data pertaining to closed claims can be migrated well in ahead The data related to inactive affiliate can be migrated well in ahead as they are not going to be modified Weekend cutover will not have any issue. Go-live on weekday will require the record locked Stop online users to do any maintenance transaction in the last 3-4 days before implementation. This will make the database more static. Stop applying regulatory changes in the last 1 month before implementation After the completion of the batch cycle final backups need to be taken.
2.1.22 Change Control Scope of Change Control
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Data Migration Strategy for AFP Reengineering Project Version 1.0
Artifacts Source Database Schema Source Data Target Data Model Extraction Rules Extraction Programs Extraction Jobs/Schedules Extracted Data on mainframe Transfer Programs Transformation Rules Transformation Programs Transformation Jobs/Schedules Transferred Data (in UNIX) Conversion Database Transformed Data Loading Programs Loaded Data Loading Jobs/Schedules Cleansing Rules Cleansing Programs/Scripts Cleansing Report Validation Rules Validation Programs Validation Reports Test Case (Unit/Integration) Test Script (Unit/Integration) Test Result (Unit/Integration) Test Report (Unit/Integration) Audit Rules Audit Programs Audit Reports
Owner Business Analyst Team Business Analyst Team Business Analyst Team Business Analyst Team Technical Team Technical Team Technical Team Technical Team Business Analyst Team Technical Team Technical Team Technical Team Technical Team Technical Team Technical Team Technical Team Technical Team Business Analyst Team Technical Team Technical Team Technical Team Technical Team Technical Team Technical Team Technical Team Technical Team Technical Team Business Analyst Team Technical Team Technical Team
Repository
Formal
2.1.23 Traceability The traceability of the data migration artifacts (documents and programs) are to be traced from target fields to the audit and validation routines in the ING Core AFP system. The following diagram depicts the traceability requirements in different stages.
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Data Migration Strategy for AFP Reengineering Project Version 1.0
S o u rr c e S o u c e
T a rr g e tt T a g e
C C
Following example can be used as a template for traceability matrix Sl
Trace
Tracing to
Filed Number Target Field Target Table Source Field Source Table / File Clean Rule Clean Program Clean Report Extract Rule Extract Program Extract Job Extract Schedule Transfer Spec Transfer Program Transfer Jobs Transfer Schedule Transform Spec Transform Program Transfer Job Transfer Schedule Clean Rule Clean Program Clean Report Load Spec Load Program Load Job Load Schedule Clean Rule Clean Program Clean Report Test Case (Unit/Integration)
Data Migration Strategy for AFP Reengineering Project Version 1.0 Sl
Trace
Tracing to
31 32 33 34 35 36 37 38 39
Test Script (Unit/Integration) Test Result (Unit/Integration) Test Report (Unit/Integration) Validation Rule Validation Script Validation Report Audit Rule Audit Script Audit Report
2.1.24 Backup and Recovery The backup and recovery process for the version controlled artifacts will be placed in Rational Clear Case tool under corresponding folders. The frequency of the back ups will depend on the type of the artifact.
The strategy developed for the data migration has been modularized to enable restart of the process at any stage of failure.
The exception handling during outage window will be decided based on the business criticality of the data that is migrated. The possible ways of handling exceptions are 1. Stop the migration. Delete everything and start from the first 2. Write the exception in separate file and continue with the migration without inserting that record 3. Write the exception in separate file and continue with the migration by inserting the record with pre defined values. 4. Stop the migration. Analyze the exception, solve it and restart the migration from the last commit point
3
Risks •
Target Database Design is not available on time. This may impact on defining mapping rules and transformation rules and the whole migration process.
•
Delay in Source Inventory analysis by ING
•
Delay in Data Cleansing activities by ING
•
The production cut-over window for implementation is expected to be 48 hours over a weekend. This window might get reduced.
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Data Migration Strategy for AFP Reengineering Project Version 1.0 •
Environment readiness
•
Cutover window – Network, Link, Database, Extended Production Window
•
Software Version Change (Oracle, Informatica, OS)
•
Major changes in source due to SAFP Regulatory Changes
•
Change in the layout of the files
4
Guidelines •
Pre extraction data cleansing is advisable for voluminous non-critical data
•
Vertical split of the source data is preferable only when design of the target data model demands it. Vertical split to handle the voluminous data is not recommended.
•
The scripts written for extraction and all other activities needs to be written in standard formats and back ups taken periodically.
•
Non Critical and back up data can be migrated two weeks before the system goes live.
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Data Migration Strategy for AFP Reengineering Project Version 1.0
5
Recommendation 1. Target data model for conversion database is yet to be firmed up. Once we have the firmed up target model, the mapping can be started. However, the iterative development model of ING Core AFP project definitely demands for an iterative construction and unit testing phase for the data migration programs. . 2. The assumption of month-end-weekend implementation of the whole ING Core AFP Data Migration may not hold good. The DM POC can be used as a contingency plan for the complete implementation. 3. The fine line between the several interfaces and the cutoff scenario of data migration has to be properly monitored. Several cutoff issues are related to the handling of the interfaces during the cutover window. We recommend formal weekly interaction among the interface team, migration team, business capability team and maintenance team. 4. The complete migration life cycle (extraction to loading on target data base) has been designed with redundancy and modularity. This is to ensure that in every logical break point one can commit or restart. 5. To minimize the risk, the implementation strategy assumed a one off data migration on the weekend and incremental build for five days. This portion will include only the data that are unlikely to change. The data related to active accounts will be migrated on the production cutover weekend. 6. We recommend a comprehensive traceability matrix based on the target fields on target database. This will provide the proper insight into the project as well as help in change control mechanism. 7. The data migration for the phase I and phase II will be assumed to be two separate implementations. 8. Candidate field analysis for all the source data is recommended upfront to identify the potential data-cleansing requirement.
