01Side by side
Teradata and Snowflake compared
Check each vendor's current documentation before relying on a row. Both products change often.
| Area | Teradata | Snowflake |
|---|---|---|
| Architecture | MPP, shared-nothing: rows hashed across AMPs by primary index | Storage in object storage; compute in separate virtual warehouses |
| Scaling | Add nodes; data redistributes | Resize or add warehouses without moving data |
| Tuning | Primary index, secondary and join indexes, statistics | Mostly automatic micro-partitions; optional clustering keys |
| Concurrency | Workload management rules in one system | Separate warehouses per team or job |
| Pricing model | Capacity or consumption agreement, set by the contract | Credits for compute time plus storage; check the vendor's current price list |
| Deployment | On premises, hybrid or Teradata's cloud edition | Cloud service on AWS, Azure or Google Cloud |
| Semi-structured data | Supported, with a different feel | Native VARIANT type with JSON functions |
02Architecture
Why does the architecture difference matter?
In Teradata the primary index decides which node holds each row, so good design and query design are tied together. A bad index choice creates skew, where one node does most of the work. Tuning is a core skill.
In Snowflake you do not choose a distribution key. Data is cut into micro-partitions and compute reads what it needs. Your tuning tools are warehouse size, query shape and, for very large tables, clustering keys. That removes a class of work, and it also removes some control.
03Cost
How do the pricing models differ?
Teradata contracts are negotiated, and the terms vary by deployment. We do not state them here. Snowflake bills compute by the second while a warehouse runs (with a one-minute minimum each time it resumes) and charges separately for storage.
That makes Snowflake cheap for bursty work and costly for a warehouse left running around the clock. A steady 24-hour load can cost less on fixed capacity. Model your own query pattern, not a vendor average.
- Count hours per day that each workload actually runs.
- Separate the heavy batch window from the light dashboard traffic.
- Add the cost of the data copy, the dual-run period and rewritten ETL.
04Workload fit
Which workloads fit which platform?
- Spiky or growing analytics, many teams, ad hoc queries: Snowflake's separate compute is a clean fit.
- Steady, very heavy, tightly tuned batch with strict service levels: Teradata may keep an edge, and a move can cost more than it saves.
- Lots of JSON or semi-structured data: Snowflake handles it more naturally.
- Data sharing with partners: Snowflake's sharing features are built for it.
- Strict on-premises or sovereign-data rules: Check whether a cloud service is allowed at all before comparing anything else.
05Migration effort
What takes the time in a Teradata migration?
The data copy is the easy part. The effort sits in the code around the warehouse.
Snowflake offers a free conversion tool called SnowConvert that translates much Teradata SQL and scripts. Treat its output as a draft. Review, run side by side and compare results before cutover.
| Teradata item | What it becomes | Effort |
|---|---|---|
| Tables and views | Snowflake tables and views; primary indexes dropped, distribution rethought | Low to medium |
| SQL dialect (QUALIFY, date formats, SET tables) | Mostly supported; SET table duplicate rules need explicit handling | Medium |
| BTEQ scripts | Rewritten as SnowSQL, Snowflake Scripting or orchestrator jobs; BTEQ control flow has no direct match | High |
| Macros | Stored procedures or views | Medium |
| Stored procedures | Snowflake Scripting or JavaScript procedures | Medium to high |
| FastLoad, MultiLoad, TPT | COPY INTO, Snowpipe or an ingest tool | Medium |
| Reports and BI semantic layer | Repointed and regression-tested | Medium to high |
06Alternatives
What other Teradata alternatives exist?
- Databricks: lakehouse; strong if data science and ML share the platform.
- Google BigQuery: serverless, billed by data scanned or by reserved slots.
- Amazon Redshift: closest to a classic warehouse on AWS. Ace8 is not affiliated with AWS.
- Azure Synapse or Microsoft Fabric: a fit if you are already on Microsoft.
- Open-source MPP: see our pages on Greenplum alternatives and Netezza migration for related exits.
07How Ace8 helps
How Ace8 helps plan the move
Ace8 is the AI-assisted application modernization practice of AceMQ. We inventory your BTEQ scripts, macros and loaders, estimate rework per script, and use AI-assisted tools with engineer review to convert them. We do not resell either platform.
The result is part of a modernization assessment. The service is described on the Teradata migration page, and the broader context is on data warehouse modernization.