Introduction

Pipeline is a key module in LeapLogic that automates and manages multiple stages of workload modernization. It enables you to configure and execute stages such as migration, transformation, validation, and execution.
The absence of automated business logic transformation often results in fragile rewrites, inconsistent logic, and sunk costs—undermining the momentum built during assessment. The impact can be significant, leading to challenges such as:

LeapLogic Transformation Capabilities

LeapLogic automates end-to-end business logic and workload transformation, converting legacy data warehouse, ETL, BI, analytics, and other workloads into optimized, cloud-native equivalents. Its intelligent, AI-driven engine ensures accuracy, functional integrity, and performance at scale. LeapLogic’s pioneering Workload Transformation is redefining the art of migration and unlocking the full potential of the cloud.

Note:

IntelliParse™, SemantiSync™, etc. are conceptual capability descriptors used for marketing communication; actual feature names may vary within the product.

The table below summarizes the key features and capabilities of LeapLogic’s transformation process, highlighting its comprehensive coverage across logic modernization, automation, validation, security, and so on.

Feature	Description
Logic transformation spectrum (Breadth)	Legacy platforms and stacks: Data warehouse, ETL, Orchestration, BI Reports, Analytics (SAS), Mainframe, Big data workloads – Hadoop ecosystem workloads, Cloud-native workloads (cloud to cloud migration)
Logic transformation spectrum (Depth)	End-to-end business logic including DML, DDL, Procedural code, Shell scripts, ETL scripts, BI Reports, Macros, Scheduler scripts, Orchestrator scripts, Analytics scripts, Cobol/JCL, User Defined Functions, Views, Hive, Spark, Oozie, Sqoop, custom scripts, cloud SQLs, cloud procedures, etc.
Myriad architectural choices	Migrates to single-cloud, multi-cloud, hybrid-cloud, serverless architecture/fully managed services. Data estate modernization as per the target native architecture’s leading practices and established standards.
Data interaction method/technique	Intelligent transformation as per the preferred data interaction method such as end-to-end native processing, or from and into an external source, or orchestration via a different native service, and more…
Automation coverage	Up to 90% in the first iteration with up to 100% in subsequent iterations
Cloud mindset and strategy	Automatically assesses, recommends, customizes, and optimizes FOR THE CLOUD as per the native cloud’s best practices
Cloud-to-cloud modernization for interoperability	Enables cloud-to-cloud modernization from one cloud service provider or hyperscaler to another for maximum interoperability
Changed data model based intelligent conversion	LeapLogic natively supports intelligent, change-aware, and fully automated translation of legacy workloads to modern cloud-native architectures. Its engine dynamically interprets evolving data models and seamlessly rewrites or redesigns transformations to align with target paradigms such as slowly changing dimensions (SCD), data mesh, data fabric, and Lakehouse architectures. By embedding this capability within the translation process itself, LeapLogic eliminates manual intervention, ensures architectural consistency, and accelerates modernization while preserving business logic integrity.
Meet/Beat SLAs – inherent (automated) optimization and performance tuning	Automated performance tuning and optimization at schema, code, and orchestration level. Several built-in code-level optimizations eradicating technical debt and transforming as per the cloud’s best practices. Meets and even beats business SLAs consistently in all engagements along with best architecture enforcement.
Custom use cases and discrete enterprise scenarios	Customized use cases like rapid extensibility for newer patterns, integration with third-party tools, architectural blueprint as per choice of target services, data ingestion strategy as per org’s security needs, etc. LeapLogic’s USP is logic migration (with inherent optimizations) to industry-standard best architecture, and at the same time, customizing for each customer's unique architecture and design needs.
Validation & certification	Automated, pattern-driven data quality (DQ) checks and anomaly detection, auto-generates reconciliation scripts, SQL/query and data-level validation, cell-level reports, system integration testing, remediation workflows using automated validation pipelines.
Code Packaging, orchestration, and execution	Code packaging as production-ready jobs along with orchestration logic for seamless execution in the new target environment.
Optimized execution meeting desired SLAs	LeapLogic’s advanced execution framework enabling parallel execution of the transformed workloads, restartability, execution lineage, ultimately, meeting business SLAs and ensuring a controlled price-capacity-performance ratio in the new modernized environment.
Security and governance	LeapLogic enforces end-to-end data security—including masking, tokenization, and related controls—both at rest and in transit during migration. Since it operates as a standalone deployment within the client’s environment, all existing enterprise and target cloud security configurations are applied without exception. For governance, LeapLogic provides native integration with data catalogs such as Unity Catalog and AWS Glue, enabling seamless policy enforcement and lineage tracking.
Operationalization, cut-over and go-live	Productionizes all transformed workloads enabling a smooth cut-over and go-live including parallel runs, snapshotting and backup integration, automated rollbacks, audit logs and change tracking

Stages in Pipeline

Pipeline encompasses multiple stages designed to facilitate data and schema migration, code transformation, validation, and execution of workloads in batches.

These stages include:

Migration: Converts schemas to target-equivalent formats and migrates data to target tables.
Transformation: Transforms legacy EDW scripts to target-native equivalent code.
ETL Conversion: Converts ETL scripts such as Informatica, DataStage, Ab Initio, etc., to target compatible workflows.
BI Conversion: Transforms legacy BI scripts into modern target platform equivalent.
Analytics Conversion: Transforms legacy analytics workloads such as SAS into modern target platform equivalent.
Mainframe Conversion: Transforms Mainframe scripts such as Cobol and JCL to the target cloud platform.
Script Conversion: Transforms Shell statements and blocks to the target equivalent Python artifacts.
Orchestration: Transforms legacy scheduler scripts into modern scheduler tools.
Validation: Validates the migrated data and the transformed queries.
Execution: Executes the transformed artifacts on the target environment.

You can configure a pipeline in an integrated fashion or also as individual operations as required. It works in both ways. The integrated one eases the configuration part as the subsequent stages in the pipeline automatically consume the input from the previous stage.

This extremely easy-to-use interface enables you to perform specific tasks through individual transformations. Just drag and drop the required transformation, configure it with the required workloads, execute the pipeline, and see the reports.