As per the data migration lifecycle, Sprinter uses an iterative model approach to minimize the risk of failure, early understanding of the failure, and provide a continuous and consistent release to the business. Every stage of this lifecycle can be operatable independently or together as a combined solution. In each stage, a dedicated report would be generated to double-check results and confirm the check and balance in-build audit process. Sprinter comes with a subscription-based model, supporting multi-tenancy & response UI-driven modern architecture providing enterprises to use single Sprinter software/service across teams within the organization. Due to robust roles & permissions, any department or team can operate their work independently without disturbing other departments’ projects or workloads. The entire data migration operation can be controlled by the support admin in the respective department. Below are high level description about components of sprinter solution:

1. Search & Discovery: Discovery module helps to audit and analyse the source database and its details. The outcome of the discovery module helps to understand the database, schemas, objects, record volumes, etc.

2. Plan & Strategy: Planning module helps to identify the required capacity for the database infrastructure and required data migration engine configuration. Additionally, this information helps to come up with tentative planning for the expected cost of the database infrastructure cost and operational cost.

3. Build & Migrate: Schema migration module helps to migrate the underlying DB and its object from source format into the designated target database (either online or offline mode, this can be executed into the target system post to the migration). If any delta is noticed, this can be led to platform customization or manually correcting the output and pushing back to the target system to completeness.

4. SQL/Script Migration : SQL / Scripts migration modules come with a unique component of Sprinter, which helps to migrate the DML, Procedures, Functions, Views, and Packages from the native form of the source database into the equivalent native form of the target database. If any delta is noticed, this can be led to platform customization or manually correcting the output and pushing it back to the target system to completeness.

5. Data Migration : Data migration helps to migrate the data in multiple stages.Stage 1 includes reading the data from a source system and locally stored close to native system to avoid network overhead. Depending upon the source system, connectivity to the source would be managed either by a generic connection or specific to native connector for better performance. Stage 2 includes NIFI engine which help to move the data from local storage into cloud storage through parallel and point-in-time recovery model. Depending upon the request either a batch process or real-time process would be enabled for data movement.Stage 3 includes java wrapped native cloud connector which is used to move the data from cloud storage into the target system. Based on the data migration model, this would be migrating either full load or incremental load.

6. Validate & Verify: Data validation is one of the critical features of the Sprinter, which helps to automate the pre-migration, post-migration, and reconciliation process without writing a single line of code. A completely UI-driven data validation route provides easy configuration and execution without worrying about the underlying volume, and complexity of the database/dataset. Execution output would be presented to the user as logs / reports. This would help the user understand the data quality of the data migration works.

7. Operate & Optimize: Tracking & Monitoring is one of the differentiators of Sprinter tool from competitors. Sprinter is the only tool that provides a tracking and monitoring facility to observe the cloud database audit, utilization, and capability assessment. This helps to control unwanted query execution, poor query design, failure attempt, storage utilization, license cost utilization, DB sizing, etc…