Convert Alteryx workflows to Snowflake

Designer workflows (.yxmd) parsed structurally. Converted to Snowpark Python and Snowflake SQL. Orchestrated with Snowflake Tasks or Airflow.

Upload a workflow, get converted code →
Why Snowflake

Desktop analytics doesn't scale to warehouse workloads

Alteryx runs on the desktop, not the warehouse

Data moves out of the warehouse, through Alteryx, and back. Snowpark keeps processing inside Snowflake — no data movement, no memory ceiling.

Spatial tools need a modern home

Alteryx spatial tools (Trade Area, Distance, Buffer) can't process enterprise-scale geospatial data. Snowflake's GEOGRAPHY type and H3 hexagonal grids handle it natively.

R and Python tools are black boxes

Embedded R/Python tools in Alteryx have no versioning, no dependency management, and no isolation. Snowpark Python and Snowflake Cortex run in managed, versioned environments.

Parser output

Spatial Trade Area to Snowflake H3

The Spatial Match tool calculating drive-time trade areas — converted to Snowflake's H3 hexagonal grid system for native geospatial analytics at scale.

Alteryx Designer
-- Spatial Workflow: Store_Trade_Areas
-- Input: STORE_LOCATIONS (lat/lon)
-- Trade Area: 15-minute drive time
-- Spatial Match: Join CENSUS_TRACTS
--   where Trade Area contains Centroid

Input:         STORE_LOCATIONS
Trade Area:    Drive Time, 15 minutes
               Data source: TomTom
Spatial Match: CENSUS_TRACTS
               Target: Centroid
               Relation: Contains
Output:        STORE_CATCHMENT_AREAS
  Fields: Store_ID, Tract_ID, Population,
          Median_Income, Distance_Miles
MigryX
converts
Snowflake SQL
-- Spatial → H3 hexagonal grid
WITH store_cells AS (
    SELECT
        store_id,
        H3_POINT_TO_CELL(
            ST_MAKEPOINT(longitude, latitude), 7
        ) AS h3_index
    FROM store_locations
),
trade_area AS (
    SELECT
        s.store_id,
        n.h3_index AS neighbor_cell
    FROM store_cells s,
    LATERAL FLATTEN(
        input => H3_GRID_DISK(s.h3_index, 4)
    ) n
)
SELECT
    t.store_id,
    c.tract_id,
    c.population,
    c.median_income,
    H3_GRID_DISTANCE(sc.h3_index, t.neighbor_cell)
        AS ring_distance
FROM trade_area t
JOIN census_tracts c
    ON c.h3_index = t.neighbor_cell
JOIN store_cells sc
    ON sc.store_id = t.store_id;

Trade Area drive-time approximated with H3 grid rings at resolution 7 (~5.16 km²). Spatial Match becomes a join on H3 cell indices. Scales to millions of points without desktop memory constraints.

Coverage

Alteryx to Snowflake — component mapping

Alteryx Component Snowflake Equivalent
Input DataSnowflake SELECT / Stage
SelectColumn alias + CAST
FilterWHERE clause
FormulaSnowpark .withColumn() / SQL expression
SummarizeGROUP BY / Snowpark group_by()
JoinSnowflake JOIN
Multi-Row FormulaWindow functions (LAG/LEAD)
Spatial MatchH3 grid join / ST_CONTAINS
Trade AreaH3_GRID_DISK() rings
R/Python toolsSnowpark UDF / Snowflake Cortex
Batch MacroSnowpark function + Snowflake Task
Server scheduleSnowflake Tasks
Validation

Every conversion validated to row-level parity

Data Matching compares Alteryx output against Snowflake output — row by row, column by column. Tolerance rules handle precision differences between Alteryx numeric types and Snowflake NUMBER.

See how Data Matching works →
Methodology proven at scale
1,200
workflows converted
8X
performance gain
$1.9M
savings over 2 years
94
spatial workflows converted

Global Media Company: Alteryx to Snowflake in 6 Months

1,200 Alteryx workflows converted to Snowpark Python and Snowflake SQL — including 94 spatial analytics workflows using H3 grids and 87 R-based predictive models moved to Snowflake Cortex. Advertising yield report: 11 hours down to 82 minutes.

Read case study →

See it on your own Alteryx workflows

Upload an Alteryx workflow. Get parsed lineage, Snowpark code, and a validation report.

Book a Live Demo → hello@migryx.com