Hard sources — not covered by free tools

Also parsed — certify what free tools miss

Targets

DatabricksSAS and mainframe batch

Warehouses

Runtimes

After migration

AtlasLineage and signed evidence

Not covered by Lakebridge, Cortex conversion, or BigQuery Migration Service. Targets delivered: Databricks, Snowflake, BigQuery.

Convert Alteryx workflows to modern data pipelines

Parser-driven modernization of Designer workflows (.yxmd), batch macros (.yxmc), and Server packages (.yxzp). Full lineage, automated conversion, validated parity.

Databricks Snowflake Google Cloud

MigryX analyzer showing a parsed Alteryx workflow as an interactive dependency graph

Alteryx workflow parsed into interactive lineage graph

Architecture

Alteryx in. Native platform out.

One source, every target. Deterministic parsers read the estate and emit native code for the platform you pick — not Designer workflows wrapped in a new scheduler.

Alteryx estate → MigryX parser → native platforms

Alteryx

Designer.yxmd / .yxmc

Server / Gallery.yxzp packages

Batch macrosIn-DB tools

R / Python toolsEmbedded runtimes

MigryX Parser

Deterministic parseAI where it helps

Lineage / STTMBefore cutover

Tool map250+ tool types

Macro expandBatch / iterative

Target platforms

DatabricksPySpark + Delta

SnowflakeSQL + Snowpark

Google CloudBigQuery + Composer

The parser is deterministic: the same input produces the same output on every run. Every output is validated against the original, row by row, before go-live.

Why now

The desktop model doesn't scale with the estate

Designer stays a desktop tool

Workflows live on individual machines. Sharing them means a Gallery, not a platform the rest of the data estate can run.

Everything runs in memory on one machine

Alteryx has no native distributed compute. When a dataset outgrows the machine, the workaround is file chunking or pushing to In-DB tools — not real parallelism.

Server administration grows with the Gallery

Gallery publishing, scheduler tuning, worker node sizing, and credential rotation all grow with workflow count. None of it is analytics work.

See it in action

Batch Macro expansion — the pattern that blocks manual rewrites

A batch macro driven by a Control Parameter iterating over regions. This is where copy-paste rewrites break down, because the macro body has no standalone equivalent. MigryX expands the structure and generates the parameterized form.

Alteryx Designer (.yxmc)

-- Batch Macro: Process_Region.yxmc
-- Control Parameter: REGION_CODE
-- Tools: Filter → Formula → Summarize → Output

Control Parameter: REGION_CODE
Macro Input:   SALES_TRANSACTIONS
Filter:        [Region] = [#1:REGION_CODE]
Formula:       [Margin] = [Revenue] - [Cost]
Summarize:     GroupBy [Product]
               Sum   [Margin] → [Total_Margin]
               Count        → [Txn_Count]
Output:        REGION_SUMMARY_[#1:REGION_CODE]

→

MigryX
converts

Python

# Batch macro → parameterized function
import pandas as pd

def process_region(df: pd.DataFrame, region_code: str) -> pd.DataFrame:
    """Expanded from Process_Region.yxmc"""
    filtered = df[df["region"] == region_code].copy()
    filtered["margin"] = filtered["revenue"] - filtered["cost"]

    return (
        filtered.groupby("product")
        .agg(total_margin=("margin", "sum"),
             txn_count=("margin", "count"))
        .reset_index()
    )

# Replaces the batch macro runner
results = pd.concat(
    [process_region(df, r) for r in df["region"].unique()],
    ignore_index=True
)

The control parameter becomes a function argument. The Filter/Formula/Summarize chain becomes pandas operations. Iteration is explicit and debuggable instead of hidden in the macro runner.

Coverage

What gets converted

Every Alteryx tool used in your workflows maps to a defined target equivalent, recorded in the lineage report.

Alteryx Component	Target Equivalent	Notes
Input Data tool	`pd.read_csv()` / `spark.read`	File type detected, connection strings mapped
Select tool	Column rename and type cast	dtype mapping preserved
Filter tool	`df[condition]` / `.filter()`	Compound filter expressions parsed
Formula tool	`df["col"] = expression`	Alteryx function library mapped
Multi-Row Formula	`.shift()` and window functions	Row offset and running state preserved
Join tool	`pd.merge()` / `.join()`	Inner, left, right and full outer
Union tool	`pd.concat()` / `.union()`	Auto-config by name or position
Summarize tool	`.groupby().agg()`	Aggregate functions and group keys
Sort tool	`.sort_values()` / `.orderBy()`	Multi-key, ascending and descending
Batch Macro (.yxmc)	Parameterized function	Control parameters become arguments
Tool Container	Code section or module	Disabled containers are skipped
In-DB tools	Native SQL or DataFrame ops	Push-down SQL preserved

Validation

Every conversion is validated to row-level parity

MigryX Data Matching compares Alteryx output against the new pipeline output, row by row and column by column, with configurable tolerance rules and mismatch drill-down.

See how Data Matching works →

FAQ

Alteryx modernization questions

What Alteryx files do you need to start?

Designer workflows (.yxmd), macros (.yxmc and .yxi), and Server packages (.yxzp). A .yxzp is ideal because it bundles the workflow with its dependencies. Parsing reads the files directly; it does not need a live Alteryx Server connection.

What happens to Batch and Iterative Macros?

Batch macros become parameterized functions, with the Control Parameter as an argument and the macro runner replaced by explicit iteration. Iterative macros become loops with the stopping condition made explicit, which is usually where hidden logic surfaces.

Do In-DB workflows convert differently from standard workflows?

Yes. In-DB tools already push SQL to the source, so that SQL is translated to the target dialect rather than rebuilt as DataFrame code. Mixed workflows convert to a combination of pushed-down SQL and pipeline code.

Can we keep some workflows in Alteryx during the migration?

Yes. Conversion is per workflow, so you can move a subset, validate it to parity, and run both side by side until you are ready to decommission Alteryx Server.