Migrations
Off Teradata, Oracle, or Hadoop. Onto one governed Snowflake.
We move legacy warehouses, cloud data warehouses, and Hadoop onto a single governed Snowflake foundation: code converted automatically, data validated for parity, and a phased cutover that keeps the business running the whole way.
Every source platform
We migrate from all of them
Legacy appliances, cloud warehouses, Hadoop, or the database quietly doing warehouse duty. If your data lives there today, we have a path to Snowflake.
Legacy MPP & data warehouses
- Teradata
- IBM Netezza
- Oracle Exadata
- Vertica
- Greenplum
- SAP BW / BW4HANA
- Microsoft APS / PDW
Cloud data warehouses
- Amazon Redshift
- Google BigQuery
- Azure Synapse
- Databricks
Hadoop & data lakes
- Cloudera / Hortonworks
- Apache Hive
- Apache Spark
- HDFS
Databases doing warehouse duty
- Oracle Database
- Microsoft SQL Server
- IBM Db2
- PostgreSQL
- MySQL
Legacy ETL & analytics
- Informatica
- SSIS
- Talend
- SAS
Platform names and logos are trademarks of their respective owners, shown to indicate migration sources we support. On something not listed here? We have almost certainly seen it. Tell us your stack and we’ll map the path.
Why teams move
The case for leaving legacy behind
The platform changes, but the reasons rhyme: cost, operational burden, and a foundation that is finally ready for AI.
Cost that flexes with use
Pay for compute by the second and scale it independently from storage. No idle clusters, no capacity bought a year ahead.
Zero infrastructure to run
No appliances to patch, no Hadoop cluster to babysit, no capacity planning. Snowflake runs the platform so your team runs the data.
End-of-life and licensing relief
Teradata, Netezza, Exadata, and SAP BW carry rising license and support costs. Migration turns that into elastic, usage-based spend.
Performance at scale
Analytics that crawled on a row-based database or an overloaded Hadoop cluster run on right-sized warehouses, with no manual tuning.
One governed foundation
Replace scattered copies and brittle exports with one governed source of truth: access, lineage, and policy built in through Horizon.
AI and Cortex ready
Land on a foundation where Cortex and the leading models run next to governed data, so AI is the next step, not another project.
How we migrate
Automated, validated, and phased
A repeatable arc that de-risks the move. Most of the conversion is automated; the cutover never is.
Assess & plan
We inventory every table, view, stored procedure, and downstream report, map the dependencies, and build the business case before anything moves.
Convert
SnowConvert translates your SQL, stored procedures, and scripts automatically (BTEQ, PL/SQL, T-SQL), with the Snowpark Migration Accelerator for Spark. We remediate the edge cases by hand.
Migrate & validate
Historical loads plus incremental CDC through Openflow and Snowpipe keep data current, while automated row, aggregate, and hash checks prove parity against the source.
Parallel run & cutover
Both systems run side by side with nightly reconciliation. We cut over in phases by business unit, never a big-bang switch, and only once the numbers match.
Optimize & decommission
We tune warehouses and pipelines on real usage, retire the legacy system, and hand over documentation so your team owns it.
Automated, then proven
Automated where it counts, validated everywhere
SnowConvert translates the SQL, stored procedures, and scripts that took years to write, so the rebuild is measured in weeks, not a from-scratch rewrite. Then automated row, aggregate, and hash reconciliation proves the new system matches the old one, table by table, before anyone cuts over.
- Code conversion for Teradata, Oracle, SQL Server, Redshift, Hive and more
- Row, aggregate, and hash checks prove parity against the source
- A parallel run with nightly reconciliation before any cutover
The hard parts, handled
The legacy logic comes too
Migrations stall on the procedural code and pipelines, not the tables. Here is what we carry over for the platforms we see most.
BTEQ, FastLoad, and MultiLoad scripts, macros, and stored procedures converted; primary-index logic redesigned as clustering.
PL/SQL packages, triggers, and cursors translated to Snowflake Scripting; constraints moved into validated pipelines.
T-SQL and SSIS packages converted to Snowflake SQL and dbt models on a native scheduler.
Hive SQL and PySpark or Scala jobs moved to Snowflake SQL and Snowpark; Parquet landed as Iceberg.
Dialect differences and DISTKEY, SORTKEY, or partition logic translated; cost re-modeled for elastic compute.
Appliance-specific SQL and procedures converted off end-of-life hardware onto elastic Snowflake.
Backed by Snowflake
A certified team that has done this before
Migrations run on Snowflake's own tooling, delivered by a SnowPro-certified team with a verified track record across the Americas.
See migrations in production in our case studies →
Plan your migration.
Tell us what you're running today (Teradata, Oracle, Redshift, Hadoop, or anything else) and we'll map the path to Snowflake.