df.filter(col("order_status") == "completed")
  .groupBy("region", "product_id")
  .agg({"revenue": "sum", "quantity": "count"})

ROW_NUMBER() OVER (
  PARTITION BY customer_id
  ORDER BY created_at DESC NULLS LAST
) AS rn

to_hex(sha256(to_utf8(
  CONCAT_WS('|', customer_id, email, region)
)))

WHERE order_date >= DATE '2024-01-01'
  AND region IS NOT NULL
QUALIFY ROW_NUMBER() OVER (...) = 1

SELECT * FROM (
  SELECT *, ROW_NUMBER()
  OVER (PARTITION BY id
  ORDER BY ts DESC) AS rn
  FROM orders
) AS __q WHERE rn = 1

date_add('month', 3, order_date)
DATE_ADD(date, INTERVAL 3 MONTH)
DATEADD(month, 3, order_date)

v0.2 · 219 tests passing · 4 dialects

Convert pipelines.
Preserve behavior.
Ship with confidence.

Legacy pipeline code to production-ready SQL across Snowflake, Redshift, Athena and BigQuery. PySpark, Informatica, SSIS and more. Powered by LocalMind™ — AI that runs entirely on your device.

Try the Demo → View on GitHub →

Converts to: Snowflake Redshift Athena BigQuery

pipeline.py PySpark

# Multi-step DataFrame chain filtered = orders.filter(col("status") == "completed") by_region = filtered.groupBy("region") result = by_region.agg({"revenue": "sum"})\ .filter(col("sum_revenue") > 1000)\ .orderBy("region")

↓ converted to Snowflake

output.sql Snowflake SQL

SELECT region, SUM(revenue) AS sum_revenue FROM orders WHERE status = 'completed' GROUP BY region HAVING SUM(revenue) > 1000 ORDER BY region ASC NULLS LAST

How It Works

Three steps to production SQL

No account. No upload. No waiting. Paste, choose, ship.

›

STEP 01

Paste your pipeline code

PySpark DataFrame API, spark.sql() queries, multi-step variable chains, or raw SQL. Drop in your legacy code and the format is detected automatically.

›

STEP 02

Choose your dialect

Pick your target warehouse. Each dialect gets purpose-built conversion rules — not generic reformatting.

Snowflake Redshift Athena BigQuery

STEP 03

Get production SQL

Receive dialect-correct SQL with actionable warnings for anything that needs manual review. Copy directly into your dbt project.

Features

Built for production migrations,
not toy examples.

Every feature exists because of a real migration failure we've seen in the wild.

🔒

Privacy First

LocalMind™ AI runs entirely on your device — your code never leaves your machine. Your code, queries, and schemas never reach our servers. Not even the hosted demo sends anything externally. Ever.

🏭

4 Dialects, Done Right

Snowflake, Redshift, Athena and BigQuery each get purpose-built conversion rules. Date functions, window functions, NULL behavior, type casting — handled per dialect, not just reformatted.

⚠️

Smart Warnings

Catches silent production bugs — CONCAT_WS NULL propagation, HAVING alias mismatches, SHA2 type differences, LAG/LEAD defaults on older Redshift. Every warning includes an actionable fix.

⛓️

Multiple Input Formats

PySpark DataFrame API, spark.sql(), Informatica mappings, and raw SQL all supported. Multi-step variable chains traced automatically — the pattern most converters silently drop.

Conversion Accuracy

We handle what others miss.

These are the bugs that slip into production when converters only handle the happy path.

CONCAT_WS NULL safety CRITICAL

-- ❌ Naive conversion: NULL propagates silently customer_id || '|' || email || '|' || region -- Returns NULL if any value is NULL — surrogate key destroyed -- ✅ PipelineConvert preserves CONCAT_WS NULL-skipping CONCAT_WS('|', customer_id, email, region) -- → '123|smith@co.com|EMEA' (NULLs silently skipped)

Athena date literal casting ATHENA

-- ❌ Spark: implicit string → date (fails in Athena/Trino) WHERE order_date >= '2024-01-01' -- Error: cannot compare date with varchar -- ✅ PipelineConvert output WHERE order_date >= DATE '2024-01-01'

QUALIFY → subquery rewrite REDSHIFT

-- ❌ Redshift has no QUALIFY clause SELECT *, ROW_NUMBER() OVER (PARTITION BY id ORDER BY ts DESC) AS rn FROM t QUALIFY rn = 1 -- ✅ PipelineConvert auto-wraps in a subquery SELECT * FROM ( SELECT *, ROW_NUMBER() OVER (...) AS rn FROM t ) AS __q WHERE rn = 1

SHA2 return type differences ALL DIALECTS

-- Spark: SHA2(expr, 256) → returns hex STRING ✓ -- BigQuery SHA256() → returns BYTES (wrong type!) SHA256(email) -- type error in downstream join -- ✅ PipelineConvert wraps correctly per dialect TO_HEX(SHA256(email)) -- BigQuery to_hex(sha256(to_utf8(email))) -- Athena SHA2(email, 256) -- Snowflake / Redshift

Convert pipelines. Preserve behavior. Ship with confidence.

Three steps to production SQL

Built for production migrations,not toy examples.

We handle what others miss.

Ready to migratewithout the pain?

Convert pipelines.
Preserve behavior.
Ship with confidence.

Built for production migrations,
not toy examples.

Ready to migrate
without the pain?