Data Lifter Cuts Modernization Effort by 80% for a Global Energy Leader
Client Overview
Operating across more than 70 countries, the client is a global energy company with a business footprint spanning LNG, oil and gas, trading, mobility, chemicals and energy solutions. Its workforce of around 85,000 supports an enterprise where technology has to keep pace with the complexity of the business. Supporting an enterprise of this scale requires technology environments that can evolve with changing business needs. The client’s legacy data workloads had accumulated significant complexity over time, creating a need for a more structured approach to modernization.
Modernization was Blocked by Undocumented Legacy Code
Attempting to migrate legacy data without full structural visibility introduces severe operational risk and bloated developer timelines. When code logic and dependencies are hidden across legacy systems, teams spend more effort manually reversing undocumented decisions.
01
Scattered Codebase Across Systems
Business logic had become fragmented across multiple Python files and disparate data sources, including SQL Server and SAP HANA. Understanding how the pieces worked together required significant manual analysis.
02
Key Logic was Left Undocumented
Large parts of the existing code had little documentation around the logic they carried. Developers had to work through the code to understand what could be changed safely.
03
Code Analysis Took Significant Developer Time
Developers spent substantial effort tracing the existing code and rewriting it for the target environment. Manual analysis became a major part of the modernization cycle.
04
Legacy Code Needed a Modern Target
The existing Python workloads were not aligned with modern PySpark, Databricks and engineering practices. Moving them forward required more than a direct code rewrite.
05
Compliance had to be Built into the Process
Compliance checks were largely handled later in the development process, creating additional risk around SEMS code. The modernization approach needed engineering and compliance checks closer to the point where code was generated and reviewed.
Data Lifter Built the Context for PySpark Modernization
Data Lifter analyzed the existing Python workloads before converting them to the modern data environment. The approach brought code structure, dependencies and business rules into the modernization process so the resulting PySpark code could be reviewed against engineering standards.
01
The Code was Mapped Before it was Rewritten
Data Lifter first examined the legacy Python code to understand what each part of the workload was doing. AST analysis helped break down the code and establish its underlying structure.
02
Dependencies were Traced Across Files
The dependency graph captured relationships across the codebase, giving developers a clearer view of how different files and components were connected.
03
Carried the Right Context into Conversion
Relevant code, dependencies, transformation rules, and engineering standards were brought together before the modernized code was generated. This gave the conversion process the context needed to carry the existing logic into the new environment.
04
Legacy Python was Converted to PySpark
Data Lifter converted the Python workloads into PySpark for Databricks, aligning the resulting code with the target engineering environment.
05
Validated the Generated Code Before Production
The modernized code was validated against engineering and code-quality requirements. Issues were remediated before developers reviewed the final output for production use.
The Heavy Lifting of Data Modernization Became Lighter
80% Reduction in Manual Modernization Effort
Automated modernization handles legacy migration overhead, shifting developer focus entirely to core business logic.
2x Faster Modernization Cycle
Higher execution throughput enables engineering teams to run large-scale migration programs within existing capacity.
60–70% Reduction in Legacy Code Analysis Effort
Streamlined code analysis cuts legacy evaluation effort and accelerates downstream conversion and developer review.
4+ Code-Quality Checks are Embedded
Embedded quality checks enforce strict governance directly within the modernization pipeline.
4,000+ Interdependent Lines Per Migration
Structured dependency resolution processes thousands of interconnected Python lines per migration without breaking functionality.
Making Modernization a Repeatable Engineering Process
Prior to Data Lifter, modernization sent developers down a manual path of tracing complex code and dependencies, with gaps in undocumented logic creating production risk. By bringing AST parsing and dependency graphing upstream, the migration moved from exploratory analysis to an automated, policy-enforced pipeline. The client established a repeatable engineering pattern where structural clarity, target alignment, and compliance checks happen before a single line of PySpark reaches production.