Converting Alteryx Workflows (.yxmd) to SQL

Which Alteryx file you need: .yxmd, .yxzp, .yxmc, .yxwz or .yxdb

Alteryx saves different things under different extensions, and only some of them hold logic you can turn into SQL. The one that matters is the workflow. A .yxmd is an XML document; open it in a text editor and every tool on the canvas appears as a Node element carrying its configuration, with the edges between tools listed under Connections.

A .yxzp is what Designer writes when you export a workflow. Alteryx describes it as a workflow (a .yxmd, .yxmc or .yxwz) plus all its dependencies zipped into one file, and by default the export pulls in input files, macros and chained apps as well. That's handy for passing a workflow to a colleague. Before a .yxzp leaves your company, open it and check what came along, because the input data often did.

• .yxmd: a workflow, and the file to convert
• .yxzp: an exported package with one workflow and its macros, input files and other dependencies
• .yxmc: a macro, meaning a workflow that runs as a single tool inside another workflow
• .yxwz: an analytic app, built in Designer for other people to run
• .yxdb: Alteryx's own data format for fields, values and spatial objects; convert the workflow that reads or writes it

Why every Alteryx tool needs its own SQL mapping

An Alteryx workflow is a canvas of tools connected by edges, each tool a self-contained operation with its own configuration format. There is no single 'Alteryx language' to translate the way you'd translate a script, so every tool type needs its own explicit mapping to a SQL construct.

That mapping has to cover every tool. A workflow with a tool nobody mapped should not silently drop that logic; it should surface as a visible gap the engineer has to address.

Which tools map cleanly to SQL

A core set of Alteryx tools has a direct, unambiguous SQL equivalent. These are the tools that make up the bulk of most production workflows.

• Input Data / Output Data → source tables and output targets
• Select → column selection and renames
• Filter → WHERE clause (True output) or its negation (False output)
• Formula → calculated columns, one per formula field
• Join (matched output) → INNER JOIN on the configured join keys
• Union, Summarize, Sort, Sample (first/last/random), Unique, Text To Columns → their direct SQL counterparts

Which tools need manual review

Some tools don't have a lossless SQL equivalent, and generated code should say so explicitly instead of guessing. The clearest example is the Join tool's L and R outputs: they represent unmatched-left and unmatched-right rows, which is anti-join logic that doesn't map to a single INNER/LEFT/RIGHT JOIN clause without additional conditions the tool doesn't expose directly.

The same caution applies to Alteryx macros (.yxmc), spatial tools and In-DB tools: none of these has a general-purpose SQL translation, so a trustworthy converter should mark them clearly rather than emit code that looks correct but isn't.

Where Deflows fits

Deflows reads .yxmd files and .yxzp packages, taking the workflow out of the package, and parses it into the same internal flow structure it uses for Tableau Prep, SSIS and Power Query. From there you generate SQL, dbt models or PySpark and review them the same way whichever tool the flow came from.

A macro call arrives as a flagged step named after its .yxmc file, and any other tool without a clean mapping is carried through with a warning, so nothing drops out of the output unnoticed. A package that holds only a macro or an analytic app is refused with a message saying it found no .yxmd inside.

Try a downloadable Alteryx to SQL example

This synthetic .yxmd fixture reads orders.csv, keeps the Filter tool's True output where Sales > 100, and records a result.csv destination. The separate input.csv includes a value at the threshold, a null and a negative sale; expected.json specifies exactly which rows should survive.

The local Deflows parser and deterministic PostgreSQL generator produced the SQL below. The production Convert API and native Alteryx/SSIS runtimes were not run. These source files are parser fixtures; native execution has not been certified. Web Convert may produce a different query. The download includes the source, synthetic input, generated output, expected rows, a verification script and provenance.

• Download the complete example (ZIP)
• Source workflow (.yxmd)
• Synthetic input (CSV)
• Generated PostgreSQL query
• Expected rows (JSON)
• Verification script (SQL)
• Run instructions and limits

How does the Alteryx filter become SQL?

The included input.csv supplies sample rows for the workflow's orders.csv source. Load those rows into the PostgreSQL example_orders table before executing the generated query. That source-location mapping is explicit; the filter still reads Sales > 100. A sale of exactly 100 and a null Sales value do not pass the connected True branch. The fixture leaves the False branch unconnected.

Each source or transform becomes a CTE, making the filter easy to compare with the XML. An explicit final OrderID sort makes the displayed rows reproducible. The query returns a result set; writing result.csv remains a separate export step.

WITH orders_input AS (
  SELECT
    "OrderID",
    "Region",
    "Sales"
  FROM "example_orders"
),
filter_2 AS (
  SELECT
    "OrderID",
    "Region",
    "Sales"
  FROM orders_input WHERE "Sales" > 100
)
SELECT * FROM filter_2
ORDER BY "OrderID";

Check the expected rows yourself

Extract the ZIP and run the following command with PostgreSQL and psql available. verify.sql creates temporary input tables, executes the SQL and compares every output column in both directions with EXCEPT ALL, preserving duplicate counts. It rolls the transaction back when finished. The expected result is PASS.

psql -X -v ON_ERROR_STOP=1 -f verify.sql

• Compare full rows, not only the row count: a changed amount must fail the check.
• The sample covers one filter path. Macros, joins, spatial tools and unknown expressions need separate cases.

What this example verifies

The exported SQL was executed locally against the included synthetic input using PostgreSQL in PGlite and matched the independently specified expected rows. Changing an expected amount made the row check fail. This demonstrates the stated transformation on this fixture and input.

Alteryx/SSIS source output comparison, native runtime execution and the Deflows Parity Test Kit were not run. This example does not establish a general conversion accuracy rate or a production parity guarantee.