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The Opportunity
Opaque, auto-generated pipelines
Talend’s visual jobs compile into massive Java files with embedded SQL and complex variable substitutions, making the real business logic hard to inspect and extract.
Manual migration doesn’t scale
Reverse-engineering each job by hand requires hours of repetitive work per pipeline, turning large migrations into months of error-prone effort.
High risk at enterprise scale
At hundreds of jobs, small inconsistencies compound into operational risk, higher costs, and long delivery timelines that are difficult to justify or maintain.
The Solution
We built NextGen, an AI-assisted migration tool that supports two complementary migration approaches: SQL-centric extraction and Java code logic simplification. Together, they automate the most time-consuming parts of the process while keeping engineers in control of validation and integration
Approach 1 – SQL-Centric Migration
In the first approach, NextGen parses Talend XML files to extract embedded SQL queries. It cleans and normalizes the SQL, resolves variable substitutions, and sends only compact, high-signal SQL to Azure OpenAI for translation into Spark SQL and PySpark.
By avoiding raw Talend exports and auto-generated Java noise, this approach reduces prompt size, improves translation accuracy, and keeps LLM usage cost-effective.
Approach 2 – Java Logic Reduction & Translation
In the second approach, NextGen operates directly on the Talend-generated Java code. Instead of translating it as-is, the tool programmatically strips away redundant constructs and non-essential code, while preserving the original execution logic and data flow.
The resulting simplified Java representation is then sent to the LLM for translation into Python and Spark constructs.
The Impact
NextGen transformed a months-long manual migration into an automated, repeatable pipeline. While human review is still required, the most time-consuming parts of the process were eliminated.
Across nearly 300 jobs, the tool reduced overall migration time by approximately 30%, improved consistency across pipelines, and allowed teams to focus on higher-value engineering tasks instead of repetitive rewrites.
