Our Automated Data Migration solution delivers modern Cloud-enabled databases and supporting applications that are functionally equivalent to their legacy counterparts. Our software converts the non-relational database into a relational database, providing instant access to vendor and third party tools for reporting, monitoring, maintaining, and documenting. In addition, it allows the data to be accessed and analysed by a vast talent pool of DBAs with modern skillsets.
Cloud Data Migration
Why modernise?
CHALLENGES
Scarcity of domain expertise
Developers and database administrators who understand mainframe technology are reaching retirement age and no one is trained to replace them, resulting in a steadily diminishing talent pool. As this shortage grows, the costs and risk of relying on legacy systems will continue to rise.
Limited flexibility
Successful digital transformation and business agility hinges on good data. Non-relational databases and the underlying assets that support them were not designed for today’s business demands or modern IT, resulting in difficult integrations, inaccessible data, and limited flexibility.
Exorbitant costs
Mainframes cost as much as 4,500 per cent more to operate than equivalent Cloud counterparts. This figure is compounded by rising licensing and maintenance fees for ancillary mainframe applications and databases.
Automated Data Migration
OUR SOLUTION
Our Automated Data Migration software converts your non-relational database into a relational database, providing instant access to vendor and third party tools for reporting, monitoring, maintaining, and documenting. In addition, it allows the data to be accessed and analysed by the vast talent pool of DBAs with modern skillsets.
Automated Data Migration options include:
- Adabas to relational (Oracle, Db2, SQL Server, or PostgreSQL)
- IDMS and IDMSX to relational
- IMS to relational
- VSAM to relational
- Db2 to an alternative relational model
Automated Data Migration consists of data and database conversion, as well as Automated Refactoring of applications used to access the in-scope data. Database and data conversion is typically performed simultaneously with the application refactoring activities, although they can be performed independently.
Automated Refactoring of ancillary applications
Automated Refactoring transforms the applications that use the in-scope data and databases to object-oriented Java or C# for deployment to the Cloud or in open systems on premises. We can also consolidate the application codebase associated with the data migration by refactoring it to COBOL for on-mainframe deployments.
Automated Data Migration activities
Database and data conversion includes de-construction of the database definitions into metadata artifacts. We tune our Automated Data Migration software to accommodate any additional conversion or architectural rules based upon assess and design phase findings and your feedback. This can include column and table naming, date type formats, overrides for redefines and group level clauses, and preferences for each statement and clause in the resulting DDL.
With our data migration experts at the helm, the Automated Data Migration software generates a brand new DDL which defines a complete relational database, providing the same data access, features, and unique functionality as the legacy database.
The resulting relational database definitions are tested by our data migration experts prior to delivery to your team to ensure that the generated code is syntactically correct. Retention of functionality is an important consideration when evaluating data migration solutions. Fortunately, we provide a complete replacement for all database and application functionality and unique constructs by default. For example, primary keys, foreign keys, and index definitions are automatically created, and all constraints are generated into the resulting DDL. Table spaces, indexes, table names, and column names are all generated according to customer naming standards as well. For mainframe-to-mainframe migrations, we even deliver the JCL required to compile and execute the extracts from the legacy database. Workbenches for date type handling also enable the new databases and applications to take advantage of all the date and time data types in the relational database.
The legacy database definition can be re-collected and processed through our Automated Data Migration software anytime throughout the project lifecycle. This prevents the need to put a freeze on changes to the legacy database structure, a huge win for critical business systems that change often.
Over 35 years of mainframe modernisation experience
More than 500 successful modernisation projects completed
Over 2.5 billion lines of code refactored through automation
Intelligent modernisation, unparalleled experience
OUR PROCESS
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Step 1: Discovery
We begin every modernisation initiative with a discovery, free of charge. Our modernisation experts work with you to understand and document your business and technical goals and objectives. Together, we gather information about your mainframe estate. Everything from infrastructure to language and database types and quantities are accounted for. The discovery concludes with a workshop where we recommend optimal modernisation approaches based on information gathered, as well as rough order of magnitude (ROM) pricing, and estimated project durations and resource requirements.
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Step 2: Assess and design
The assess and design phase is performed using our automated tools to fully catalogue and develop a complete understanding of the data, application, infrastructure, and operational components of a mainframe environment. It exposes mainframe artifacts that organisations did not know they had, relationships they did not realise existed, and assets that are no longer in use. Assess and design activities provide stakeholders with valuable insight to inform data migration decisions such as ancillary application disposition strategies and target environment architectures. It also mitigates cost and risk by highlighting potential challenges and how to overcome them. At the conclusion of the assess and design phase, all in-scope artifacts are accounted for, a detailed project plan is established, and the transformation process begins.
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Step 3: Transform
The transform phase includes the bulk of the Automated Refactoring activities associated with ancillary applications, the database and data conversion, and build-out of the target operating environment. Our tools generate the new target DDL, a set of custom extract programs, and the related JCL that extracts the data to a set of sequential delimited files, and generates the load syntax required to populate the new relational databases using the target database's load utility. These relational databases are delivered ready for creation and population, including the target DDL for the database definition, appropriate extract programs, JCL for extract execution, and load syntax for populating the relational tables. During transform, we also refactor applications in pre-defined work packets, perform functional tests, and deliver them into the DevOps pipeline. While customer teams build, test, and deploy these work packets in the target environment, we refactor the next work packet, iterating until the entire mainframe estate has been transformed.
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Step 4: Test and deploy
The test and deploy phase begins with the receipt of work packets through the DevOps toolchain as they are delivered from iterative refactoring activities in the transform phase. If issues arise, the work packet is passed back to us where we review the code, adjust our tooling, reprocess the work packet, and send it back into the DevOps pipeline to resume testing and deployment. This process repeats until the entire refactored estate has been deployed.
Prior to go-live, we refresh the target environment by transforming a final snapshot of the mainframe environment to account for changes that have taken place throughout the modernisation project as part of normal business operations. Fortunately, since all adjustments and optimisations have been made to the automated refactoring and data migration tools and not the refactored code or converted databases, this final refresh can be delivered into production over a weekend, eliminating code freezes.
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Step 5: Support
We work closely with you and your team to ensure a smooth transition into production. Part of this transition includes the cooperative construction and testing of a go-live production cutover plan to reduce the potential risks associated with application deployments. We also provide on-demand assistance during the warranty period following production deployment, as well as post-transformation support of the modernised application.
“The re-platformed system provides the DWP and its citizens with a faster and more responsive service with improved resilience and scalability.”
Mark Bell, VME-R Deputy Director,
UK Department for Work and Pensions
Additional insights to help you modernise
RESOURCES
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UK Government Agency
Faced with Fujitsu mainframe renewal deadline, we successfully refactored IDMSX database to Microsoft Azure Public Cloud.
Read case study Tools -
Liberate legacy data
This whitepaper provides insight to the common challenges in handling legacy data and options for solving them.
Read whitepaper Whitepaper -
2021 Mainframe Modernisation Report
Report Read report -
Desjardins I Financial Services
Tools Read case study -
Live Discussion: The 2021 Mainframe Modernisation Business Barometer Report
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Dwr Cymru Welsh Water I Utilities
Tools Read case study
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