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PlaidCloud

Migrate Alteryx Workflows

PlaidCloud converts Alteryx workflows, analytic apps, and macros into Advanced workflows that can be reviewed, scheduled, parameterized, and run in PlaidCloud. The importer preserves the workflow graph, uploads referenced files to Document, creates macro workflows when needed, and maps tools to native workflow steps or managed job executors.

Use this guide when you are moving a single workflow, a group of related workflows, or a larger Alteryx portfolio into PlaidCloud.

What PlaidCloud Creates

Section titled “What PlaidCloud Creates”

PlaidCloud creates a runnable Advanced workflow from the Alteryx design:

  • Workflow tools become PlaidCloud workflow steps with the original upstream and downstream relationships preserved.
  • Alteryx macros become PlaidCloud macro workflows with explicit macro inputs and macro outputs.
  • Analytic app questions become controlled workflow variables that users can set before a run.
  • Input files, output files, spatial sidecars, images, PDFs, and generated artifacts are stored in Document at the path selected during import.
  • Assisted Modeling (machine learning) pipelines become native ML Train and ML Score workflow steps.
  • Advanced operations such as fuzzy matching, spatial processing, PDF extraction, OCR, NLP, and reporting run through PlaidCloud’s managed job executors when a native SQL or workflow operation is not the best fit.
  • Browse, layout, annotations, and designer-only objects are retained where they help explain the converted workflow, but they do not add unnecessary runtime work.

Before You Start

Section titled “Before You Start”

Collect the workflow files and dependencies together before importing:

  1. Include Alteryx workflow, app, and macro files: .yxmd, .yxwz, and .yxmc.
  2. Include input data files such as CSV, Excel, Access, YXDB, XML, JSON, and database extracts.
  3. Include spatial sidecar files together. For example, keep shapefile groups and MapInfo files in the same folder.
  4. Include report assets such as images, PDFs, map layers, and templates.
  5. Choose the PlaidCloud project where the converted workflows should be created.
  6. Choose the Document account and folder where PlaidCloud should upload imported files.
  7. Decide whether this migration requires structural validation only or output parity validation.

Import A Workflow

Section titled “Import A Workflow”
  1. Open the target project in PlaidCloud.
  2. Open Tools → Import Alteryx Workflow.
  3. Choose the target project and select the .yxmd, .yxwz, or .yxmc file to import.
  4. Optionally set workflow, step, and table name prefixes.
  5. Choose the Document account and folder where imported files should be stored.
  6. Add any referenced files or folders that the workflow needs at runtime.
  7. Start the import.
  8. Review the conversion report shown when the import finishes.
  9. Open the generated Advanced workflow.

When the import finishes, the import window replaces its form with a conversion report: a summary of how many steps mapped with high confidence and how many need review, and — when any step needs a look — a table of the lower-confidence or caveated steps listing each step’s name, operation, confidence, and notes. Use it to go straight to the steps worth checking before you run the workflow; each of those steps also carries the same confidence note in its step memo on the canvas.

PlaidCloud stores imported dependencies in the Document location selected during import. Converted steps then reference those Document paths, so the workflow can run repeatedly without relying on a desktop file system.

Use The Converted Workflow

Section titled “Use The Converted Workflow”

After import, use the workflow like any other PlaidCloud Advanced workflow:

  1. Open the converted workflow canvas.
  2. Review the generated steps and branches.
  3. Set workflow variables for any converted app questions or runtime parameters.
  4. Run the workflow.
  5. Review run history, step outputs, readiness notes, and generated artifacts.
  6. Schedule the workflow when it is ready for repeatable operation.

Converted macros are available as PlaidCloud macro workflows. A workflow that called an Alteryx macro will call the generated PlaidCloud macro through the macro step. Macro runs are isolated from one another, so concurrent workflow runs can safely use the same macro definition.

Validate The Conversion

Section titled “Validate The Conversion”

PlaidCloud supports two practical validation levels.

Structural Validation

Section titled “Structural Validation”

Structural validation confirms that the workflow was converted into a runnable PlaidCloud DAG:

  • Every Alteryx tool has a PlaidCloud conversion route.
  • Required macros were found or generated.
  • Required input files were uploaded to Document.
  • Macro inputs and macro outputs are connected.
  • Workflow variables were created for user-controlled inputs.
  • The generated workflow opens and can be run in PlaidCloud.

Structural validation is useful for migration readiness, inventory review, and early portfolio conversion.

Output Parity Validation

Section titled “Output Parity Validation”

Output parity validation compares the PlaidCloud run against trusted Alteryx outputs:

  • Output schemas match.
  • Row counts match.
  • Row values match.
  • Row order is ignored unless the workflow explicitly depends on ordering.

For workflows that create reports, maps, PDFs, images, or model artifacts, validate the generated artifact or the data behind the artifact according to the way your team uses the output.

See Validate Converted Alteryx Workflows for a detailed validation checklist.

Machine Learning (Assisted Modeling) Conversions

Section titled “Machine Learning (Assisted Modeling) Conversions”

Alteryx machine-learning pipelines built with the Machine Learning tool family convert to PlaidCloud’s native ML steps:

  • A Classification or Regression tool, together with its upstream Assisted Modeling and Transformation stages and the downstream Fit tool, fuses into a single ML: Train Model step. The step carries the exact algorithm, target variable, feature columns, and hyperparameters from the Alteryx workflow, and writes the trained model as a one-row model table with the training metrics recorded in it.
  • Each Predict tool becomes an ML: Score step reading the trained model table and its data input. The prediction column is named after the target variable with a _predicted suffix.
  • XGBoost classifiers convert with an approximation note: the model trains as a scikit-learn gradient-boosted classifier, and any XGBoost-specific tree parameters with no equivalent are listed in the step’s mapping notes. Validate model metrics against the Alteryx run before relying on parity.
  • An Assisted Modeling wizard whose model was chosen interactively (and never saved into the workflow file) cannot convert — the importer keeps a placeholder step whose notes record the recovered target variable so you can hand-build the ML Train step.
  • A standalone one-hot-encoding stage is flagged in the conversion report. The fused ML Train step one-hot encodes categorical features internally, so the standalone stage usually needs no replacement.

Like every conversion, the fused steps carry their confidence and notes in the conversion report and on the canvas, so the pipelines worth double-checking are easy to find.

Review Conversion Coverage

Section titled “Review Conversion Coverage”

The Alteryx Conversion Matrix lists each supported Alteryx object, its coverage level, and the PlaidCloud operation used during conversion.

Use the matrix to understand how the importer handles each tool family:

  • Native DAG steps for common data preparation, joins, filters, formulas, sorting, unions, sampling, and reshaping.
  • Macro steps for macro inputs, macro outputs, macro invocation, control parameters, and macro concurrency.
  • Controlled workflow variables for analytic app questions such as check boxes, drop-downs, text boxes, radio buttons, folder pickers, and file pickers.
  • Document-backed file operations for input, output, directory, and dynamic file behavior.
  • Native ML Train and ML Score steps for Assisted Modeling machine-learning pipelines.
  • Managed job executors for specialized spatial, fuzzy matching, PDF, OCR, NLP, reporting, and artifact work.
  • Cloud-native equivalents where PlaidCloud creates a durable, shareable artifact rather than reproducing an Alteryx-specific desktop renderer or proprietary file format.
Section titled “Recommended Migration Practice”

For a large portfolio, migrate in batches:

  1. Import the workflows and macros into a migration project.
  2. Complete dependency packages before reviewing individual formulas or business logic.
  3. Run structural validation across the batch.
  4. Prioritize output parity validation for production workflows, regulatory workflows, and workflows with downstream consumers.
  5. Promote validated workflows into the target production project.
  6. Schedule production runs and monitor run history.
Section titled “Related Guides”

Migration Documentation Set

Section titled “Migration Documentation Set”

For larger migrations, use these focused guides with this migration guide: