Alteryx to Snowflake

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

Architecture

Alteryx in. Snowflake out.

Deterministic parsers read the estate and emit native Snowflake code — not Designer workflows wrapped in a new scheduler.

Alteryx → MigryX parser → SQL + Snowpark + Tasks

Alteryx

Designer.yxmd / .yxmc

Server / Gallery.yxzp packages

Batch macrosIn-DB tools

R / Python toolsEmbedded runtimes

MigryX Parser

Deterministic parseAI where it helps

Row-level parityBefore cutover

SQL emitSet-based procedures

Snowpark emitWhere SQL is not enough

Snowflake

Snowflake SQLDDL from schemas

SnowparkPython DataFrames

TasksReplaces scheduler

StreamsChange data

Git + cloneDev -> prod

RBAC + tagsColumn security

MigryX AI handles the logic parsers cannot resolve alone, and every change it makes goes through the same parity checks. It runs on a model you approve, air-gapped if your estate requires it.

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