Designer workflows (.yxmd) parsed structurally. Converted to Snowpark Python and Snowflake SQL. Orchestrated with Snowflake Tasks or Airflow.
Upload a workflow, get converted code →Data moves out of the warehouse, through Alteryx, and back. Snowpark keeps processing inside Snowflake — no data movement, no memory ceiling.
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.
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.
The Spatial Match tool calculating drive-time trade areas — converted to Snowflake's H3 hexagonal grid system for native geospatial analytics at scale.
-- 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
-- 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.
| Alteryx Component | Snowflake Equivalent |
|---|---|
| Input Data | Snowflake SELECT / Stage |
| Select | Column alias + CAST |
| Filter | WHERE clause |
| Formula | Snowpark .withColumn() / SQL expression |
| Summarize | GROUP BY / Snowpark group_by() |
| Join | Snowflake JOIN |
| Multi-Row Formula | Window functions (LAG/LEAD) |
| Spatial Match | H3 grid join / ST_CONTAINS |
| Trade Area | H3_GRID_DISK() rings |
| R/Python tools | Snowpark UDF / Snowflake Cortex |
| Batch Macro | Snowpark function + Snowflake Task |
| Server schedule | Snowflake Tasks |
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 →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.
Upload an Alteryx workflow. Get parsed lineage, Snowpark code, and a validation report.