Snowflake migration
Snowflake migration without a risky cutover
We convert your code, move and validate your data, and run old and new systems side by side until the numbers match. Then we switch over and help you retire the legacy platform.
Quick answer
How do you migrate a data warehouse to Snowflake?
A Snowflake migration runs in six steps: assess the existing objects and dependencies, design the target architecture, convert code and SQL dialects, load historical and incremental data, validate with row counts, checksums and report reconciliation, then cut over wave by wave. Running the old and new systems in parallel until each wave reconciles removes most cutover risk.
Sources
What we migrate from
| Source platform | What usually needs converting |
|---|---|
| Teradata | BTEQ, FastLoad and MultiLoad scripts, macros, SET tables, QUALIFY-heavy SQL |
| Oracle | PL/SQL packages and procedures, sequences, CONNECT BY queries, materialized views |
| SQL Server / Synapse | T-SQL procedures, SSIS packages, temp-table patterns, identity columns |
| Netezza | nzsql scripts, distribution keys, zone-map assumptions |
| Amazon Redshift | Sort and distribution keys, UNLOAD/COPY jobs, Spectrum tables |
| Hadoop / Hive / Spark | HiveQL, partitioned file layouts, Spark jobs to Snowpark or SQL |
Approach
How a migration runs
Assess
Inventory tables, views, procedures, jobs and downstream reports. Map dependencies and usage so we know what to move, what to rewrite and what to retire.
Design
Target architecture: databases and schemas, warehouse sizing, role model, ingestion pattern and a wave plan grouped by business domain.
Convert
Automated conversion with Snowflake's SnowConvert where it fits, then engineers fix and test what automation misses: procedures, dialect edge cases, scheduling.
Move data
Historical load through stages and COPY INTO, then incremental or CDC feeds so Snowflake stays current during the parallel run.
Validate
Row counts, checksums and aggregate comparisons per table, plus report-level reconciliation signed off by your business owners.
Cut over and retire
Switch consumers wave by wave, monitor cost and performance, and decommission legacy jobs once each wave is stable.
What you get
Deliverables
- Migration inventory and wave plan
- Target architecture and role model
- Converted, tested code in your repository
- Reconciliation reports for every migrated table
- Runbooks and a handover to your team or ours
- A cost review once workloads are live
Related experience. Our team completed an 18-month mainframe core-banking migration with a parallel run and zero downtime. The same parallel-run discipline applies to warehouse migrations. Read the case study.
FAQ
Questions buyers ask us
How long does a Snowflake migration take?
It depends on the number of objects, the amount of procedural code and how many downstream systems depend on the warehouse. We give a timeline after the assessment, broken into waves so value arrives early.
Can we keep the old warehouse running during migration?
Yes. We recommend a parallel run for each wave until reconciliation passes and business owners sign off.
Do you use automated code conversion?
Yes, where it saves time. Automated tools handle most DDL and SQL; our engineers handle procedures, dialect edge cases and anything the tools flag.
Next step
Tell us what you need to build or who you need to hire.
A 30-minute call with a senior architect. You leave with a scoped plan or a role profile, whether or not you work with us.