PyFluent Studio

The workspace where the parser has the last word.

Import the estate. Convert the framework. Watch lineage update as you edit. Match the new output to the baseline. Documentation is generated from the parse, not from a prompt.

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From import to accepted output

Five steps. Your team runs them. No services contract required.

01 — Analyze

Import the codebase

Python, pandas, PySpark, or SQL. The parser extracts lineage, STTM, and complexity without you marking anything up.

AST parse across files and modules
Project metrics and dependency map

Project metrics after import — Metrics and analysis after import

02 — Convert

Change the framework, keep the meaning

pandas to Polars or PySpark. SQL toward the warehouse. The conversion is a parse, not a rewrite you hope is close.

Framework conversion with lineage attached
Suggestions labeled as suggestions

Conversion and optimization next to lineage — Conversion with lineage context

03 — Build

Edit with the graph beside the code

The lineage view updates as you type. Schema cards and previews sit under each step so you do not write profiling code to see the data.

Live column graph
Inline previews

Visual editor with live lineage — Editor with live lineage

04 — Prove

Match the new run to the old one

Column-level comparison against the baseline. Keys, tolerances, pass or fail. Re-run it; the answer does not change.

Source-to-target matching
Regression on the same inputs

Validation results — Deterministic matching

05 — Ship

Export what production will run

Modules, Spark jobs, or Airflow DAGs. Docs and dictionaries come from the parse so they do not rot the week after the handoff.

Export to the runtime you already use
Docs generated from code and lineage

Assistant preparing production export — Production export and docs

Six modules. One platform.

You do not stitch Jupyter, a catalog, Great Expectations, and a conversion script.

Risk and complexity

Scoring, dependency heat, and debt across the estate.

Visual lineage

Trace any output column to its source. No annotations.

Conversion

pandas, Polars, PySpark, Snowflake, Databricks SQL.

STTM

Every column's journey, extracted by the parser.

Documentation

Dictionaries and pipeline docs from the actual graph.

Matching

Row-level comparison with a pass/fail you can defend.

Run it where the data already lives

Step-through execution on Databricks or Snowflake. You see the step that stopped, not a stack trace from last night's job.

SaaS on the MigryX portal, or the same image behind your firewall. Air-gapped if required. AI is optional.

Bring a sample. We convert it.

Start with a sample of your code, or book a demo and we will walk a job you already run.

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