appMIGRATE tool provides end to end data migration processes right from Requirements Definition to successful Cutover. All these processes and activities come built inside the tool, allowing data migration SDLC processes to become repeatable, scalable and predictable.
Project Timeline and Budget
appMIGRATE performs data profiling on the source data to reduce the data volume and explore data quality issues. Data profiling is done with the extracted data against the target applications Reference data and Setup data dependencies with predefined rules. Business process verification is another highlight. Data profiling engine is also used for on-going ERP wellness activities
Profiling source data improves data quality and reduces data quantity. Profiling the extracted data against the target reference data and business logic is built inside the appMIGRATE tool. Each Template comes packed with hundreds of pre-validations.
appMIGRATE helps investigate if existing data can be used for other purposes.
Improves the ability to search the data by tagging it with keywords, descriptions or assigning categories.
Allows assessment of risk involved in integrating data for new applications, including the challenges of joins.
Helps understanding data challenges early in any data intensive project, so that late project surprises are avoided. Finding data problems late in the project can lead to delays and cost overruns.
Provides enterprise view of all data, for uses such as Master Data Management where key data is needed, or Data Governance for improving data quality.
“For a large document management company we reduced Item Master data size to about 5% of the original size.”
appMIGRATE provides extensive features and functions for Data Quality needs of Data Migration projects. The main features offered include:
appMIGRATE Data Migration process specifies Data Quality Management activities as “Mandatory” for all Data Migration projects. The DQM activity is generally carried out in the early phase of the project and it is a precursor to Data Migration activities. The appMIGRATE tool has powerful features to collect high volume of master data into its data mart for performing DQM activities. It uses consolidation algorithms along with Hadoop technology to identify the matches and no matches for the collected data. Provides a good user experience in classifying the matches into “false positives” or “false negatives”. When the user merges the matching records, individual values can be copied from one to another. Using “Merge” activity, data gets harmonized using data cross references.
“DQM is a prime mover of a successful Data migration project. It is a mandatory process and cannot be wished away.”
Chain-Sys Reconciliation engine is used for validation of the Data Migration results. Also it is used for on-going ERP wellness activities.
Technical and Functional reconciliation is a mandatory step in Chain-Sys Data migration process. It ensures smooth transition to the target application. It also helps with the ongoing Data Maintenance efforts. appMIGRATE consistently delivers 99.999999% data reconciliation success in every project.
appMIGRATE tool provides out of the box data reconciliation processes and functions for both Technical and Functional reconciliations. The tool provides complete drill down capable dashboards for identifying the discrepancy between source and target systems. After the go-live, appMIGRATE helps you with the ongoing reconciliation needs between Sub Ledger and Main Ledger (Finance and Cost). Within Sub Ledger accounting, transactions and activities within modules are tracked and reported using this engine.
Data maintenance is a key part of appMIGRATE GET CLEAN STAY CLEAN solution strategy. For that appMIGRATE provides over 2000 predefined Templates for maintaining Setups, Master Data and Transactional Data (Open and Historical Data).
“Major Japanese consumer electronics firm’s European Division built over 200 Coexistence integration objects using appINTERFACE Module of appMIGRATE and saved over 70% in cost and time. The integrations were built in record times where the traditional ETL approaches had failed.”
Enterprises do not adopt simultaneous “big bang” Go Live into a single Target Application system from all their geographies and divisions. In a phased approach, existing data interfaces cannot be interrupted for the legacy systems, when feeder systems have already been migrated over. appCONNECT Module fits the bill for quick interface building between systems, both batch and real time.
During Implementations, appCONNECT module is used for building the integrations which are needed for the coexistence of the old and the new applications. The appMIGRATE Extract and Load Adapters are reused for the coexistence tasks.
Some of the Applications amongst whom data interfaces were rapidly built using appCONNECT: SAP R/3, SAP ECC6, Oracle E-Business Suite 11i, R12, Oracle Fusion CRM, Oracle HCM Cloud, Financials, Peoplesoft, Siebel, Steelwedge, etc. Cloud based systems were interfaced with as much ease as on premise systems.
appMIGRATE provides features and functions for a smooth cutover. Smooth cutover is achieved by a combination of features including: Change Data Capture, Data Scalability functions and Configuration Management.
The appMIGRATE product offers powerful Change Data Capture functionality. Using this feature, the source application tables redo log files can be monitored at a scheduled time frequency and the changes can be moved into the Change Data Capture tables. From the Change Data Capture tables, the data can be extracted, transformed and loaded into Target application. appMIGRATE offers Templates for “Update” of the data entities along with “Create” option, which will be used for updating the target application.
The change data capture by appMIGRATE engine over the source database happens asynchronously in a certain time frequency by the following process
For Data Migration situations of large data volumes, complex validations and intricate transformations, appMIGRATE offers the following features:
|CLUSTER||Clustering multiple application servers to load balance|
|MULTIPLE JVM||Create multiple JVMs under the application server to process more threads|
|PARALLEL PROCESSING||Process Extractions in parallel and Process Target Application Loads in parallel|
|MULTIPLE THREADS||Execute multiple threads simultaneous based on number of cores and processors|
|SPLIT PROCESSING||Split the loads into smaller batches of data and process in parallel|
|MERGE OPTION||Reduce the IO against the database|
“appMIGRATE achieved a record 3 hours cutover time at a very Transaction Intensive major Travel site."
“appMIGRATE successfully processed over 520 Million records of installed base for a major appliances brand in USA”