Redshift to Snowflake migration that moves Redshift tables into Snowflake in parallel

UNLOAD each Redshift table to S3 as Parquet, COPY it into Snowflake, then run both warehouses in parallel while you port queries and dashboards. Migrating one reporting domain at a time keeps a working system in place throughout and lets you compare totals across two live systems instead of trusting a single cutover weekend.

Physical tuning, identifier case and SUPER columns. Distribution keys and sort keys have no Snowflake equivalent, so that tuning work is discarded rather than translated, and teams often over-engineer clustering keys trying to replace it. Redshift folds unquoted identifiers to lowercase while Snowflake folds them to uppercase, which breaks any query that quoted names. SUPER columns become VARIANT and the access syntax changes.

SMALLINT, INTEGER and BIGINT all become NUMBER(38,0), DECIMAL becomes NUMBER with the same precision and scale, REAL and DOUBLE PRECISION become FLOAT, and BOOLEAN stays BOOLEAN. VARCHAR becomes VARCHAR, and the byte-length limit disappears because Snowflake VARCHAR defaults to 16,777,216 bytes. TIMESTAMP becomes TIMESTAMP_NTZ, TIMESTAMPTZ becomes TIMESTAMP_TZ, and SUPER becomes VARIANT.

No, and you should not try to recreate them. Snowflake stores data in micro-partitions and prunes automatically on the natural load order, so most tables need no manual physical design at all. Add a clustering key only after a specific large table demonstrably scans too much, not as part of the migration.

The Redshift to Snowflake migration from Adapters copies Redshift tables into Snowflake on an incremental schedule, mapping Redshift types to Snowflake types and merging on the key, so both warehouses run in parallel and you cut over once the row counts and totals agree. Field mapping takes about a minute and no code is required.

Yes. This Redshift to Snowflake connector ships prebuilt: the field mapping is wired the moment you pick the pair, transforms are included, and the first sync can run within minutes. No code or engineering sprint required.

Pricing is flat and monthly: Starter at $49, Growth at $149, Scale at $399. Every plan includes this pair, visual field mapping, and per-record logs. There are no per-task or per-row fees, so the bill stays the same as volume grows.

Hourly on Starter, every 5 minutes on Growth, and down to every minute on Scale. Failed records retry automatically with backoff, and alerting plus a full per-record log come standard on every run.

No. Fields are auto-mapped the moment you pick the pair, and you can rewire any mapping visually before the first sync. Incremental runs use a Redshift watermark column so only changed rows move; DECIMAL keeps its precision as Snowflake NUMBER, VARCHAR byte-length limits widen to Snowflake VARCHAR without truncation, SUPER columns land as VARIANT, TIMESTAMP and TIMESTAMPTZ normalize to TIMESTAMP_TZ in UTC, lowercase Redshift identifiers fold to Snowflake uppercase consistently so quoted references keep working, distribution and sort keys are dropped in favor of Snowflake clustering where it earns its cost, and loads MERGE on the primary key so a replayed batch updates in place.