Teradata to Snowflake

Architecture

Teradata BTEQ in. Snowflake out.

Deterministic parsers read the estate and emit native Snowflake code — not BTEQ replayed through a Teradata-compatible shim.

Teradata BTEQ → MigryX parser → SQL + Snowpark + Tasks

Teradata BTEQ

BTEQ scripts.bteq / SQL

FastLoadBulk ingest

MultiLoad / TPumpUpsert paths

QUALIFY + PIWindow + index

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

Teradata's appliance model doesn't scale elastically

QUALIFY preserved natively

Snowflake supports QUALIFY as a first-class SQL clause. Teradata's QUALIFY ROW_NUMBER(), RANK(), and DENSE_RANK() patterns transfer directly—no subquery wrapping, no rewrite. The analytical intent stays readable and performant.

BTEQ becomes SnowSQL and stored procedures

BTEQ's .LOGON, .IF/.THEN, .GOTO control flow and embedded SQL are parsed and converted to Snowflake stored procedures and SnowSQL scripts. .LABEL and .QUIT logic becomes structured exception handling with Snowflake Scripting.

FastLoad becomes Snowpipe and COPY INTO

Teradata's proprietary bulk-loading utilities—FastLoad, MultiLoad, FastExport, TPump—are replaced by Snowflake's COPY INTO for batch loads and Snowpipe for continuous ingestion. No proprietary channel drivers or appliance firmware required.

Parser output

BTEQ with COLLECT STATISTICS and QUALIFY to Snowflake

A BTEQ script using COLLECT STATISTICS and QUALIFY for deduplication—converted to Snowflake SQL where QUALIFY is preserved natively and statistics are managed automatically.

Teradata BTEQ

.LOGON tdserver/dbc,dbc;

COLLECT STATISTICS ON customer_orders
  COLUMN (customer_id);

CREATE VOLATILE TABLE latest_orders AS (
  SELECT customer_id, order_date, amount,
         order_status, region
  FROM customer_orders
  WHERE order_date >= DATE - 365
  QUALIFY ROW_NUMBER() OVER (
    PARTITION BY customer_id
    ORDER BY order_date DESC
  ) = 1
) WITH DATA ON COMMIT PRESERVE ROWS;

SELECT region,
       COUNT(*) AS customer_count,
       SUM(amount) AS total_revenue,
       AVG(amount) AS avg_order_value
FROM latest_orders
GROUP BY region
ORDER BY total_revenue DESC;

.LOGOFF;
.QUIT;

→

MigryX
converts

Snowflake SQL

-- BTEQ → Snowflake SQL
-- COLLECT STATISTICS: Snowflake manages
-- statistics automatically (no action needed)

CREATE TEMPORARY TABLE latest_orders AS
  SELECT customer_id, order_date, amount,
         order_status, region
  FROM customer_orders
  WHERE order_date >= DATEADD(DAY, -365,
                               CURRENT_DATE())
  QUALIFY ROW_NUMBER() OVER (
    PARTITION BY customer_id
    ORDER BY order_date DESC
  ) = 1;

SELECT region,
       COUNT(*) AS customer_count,
       SUM(amount) AS total_revenue,
       AVG(amount) AS avg_order_value
FROM latest_orders
GROUP BY region
ORDER BY total_revenue DESC;

BTEQ control flow removed. QUALIFY preserved natively in Snowflake. VOLATILE TABLE becomes TEMPORARY TABLE. DATE arithmetic mapped to DATEADD. COLLECT STATISTICS removed—Snowflake manages optimizer statistics automatically.

Coverage