Teradata to Snowflake Migration
Modernise your data strategy. Move to Snowflake for better performance, lower TCO, and analytics built for today.
Aegis Softtech accelerates Teradata migration with AI-powered code conversion, phased delivery, and a structured cutover plan, keeping your business running throughout.
Data Migrated
Projects Delivered
Avg. Migration
Accuracy Assurance
Why Teradata is Different From Every Other Migration Source?
Redshift and SQL Server use standard SQL conversion, which is largely automated. Oracle has PL/SQL complexity but manageable object counts. Hadoop has volume but no proprietary scripting layer.
Teradata has all of it at once:
Proprietary scripting at scale
BTEQ and TPT jobs with logic that has no direct Snowflake equivalent
Performance baked into the schema
Primary Indexes that require query analysis to translate, not just DDL conversion
Licensing that keeps billing
every week of parallel running costs double
Institutional knowledge risk
the engineers who built the BTEQ scripts often aren't the ones migrating them
This is why Teradata needs a source-specific playbook. Not a generic migration methodology with Teradata on the source list.
Teradata vs. Snowflake: The Architecture Gap
Teradata engineers performance into the schema: Primary Indexes, AMP distribution, NUSI structures. Snowflake services make those concepts obsolete.
Porting a Teradata schema directly into Snowflake produces a data warehouse that runs but underperforms. Migration requires redesign, not conversion.
| Teradata | Snowflake | Implication |
|---|---|---|
| Primary Index (PI/UPI) | No equivalent | Schema redesign from query patterns |
| NUSI | Optional Clustering Keys | Most NUSIs dropped |
| BTEQ Scripts | Tasks + Stored Procedures | Rewrite, not conversion |
| TASM | Resource Monitors + Warehouses | Policy redesign |
| TPT | Snowpipe / COPY INTO | Architecture change |
The Four Teradata-Specific Conversion Challenges
1 The BTEQ Rewrite Bill Arrives Late
Object counts don't reveal script complexity. Dynamic SQL, macro chains, and DBC.* queries have no Snowflake equivalent and they're where the hours accumulate. Engagements scoped without a complexity analysis routinely run 40–60% over on labour.
How We manage:
SnowConvert handles the first pass. Our certified Snowflake developers rewrite what it flags scoped against the Phase 1 inventory before execution begins.
2 The Parallel-Run Window Becomes a Cost Trap
Teradata licensing doesn't pause during migration. Without defined sign-off criteria and a decommission schedule, parallel running extends indefinitely and every week costs double.
How We manage:
Sign-off criteria, stakeholder gates, and decommission milestones are locked in the project plan before migration starts. Our migration practice treats Teradata shutdown as a delivery milestone, not an afterthought.
3 Schema Redesign Is Mistaken for Schema Conversion
PI choices, NUSI structures, and partition logic carried into Snowflake produce a technically functional but underperforming data warehouse. The rework cost later is higher than the design cost now.
How We manage:
Schema is redesigned from DBQL query analysis — not copied from source DDL. Clustering keys are built from real query patterns. Architecture is signed off on before a single table migrates.
4 Cutover Risk Is Higher Than Expected
Teradata feeds finance, compliance, and operational systems built over the years. Validation treated as a final-stage activity — and untested rollback plans — turn cutover into a high-stakes moment it doesn't need to be.
How We manage:
Automated reconciliation runs on every wave, not just at cutover. Rollback procedures are rehearsed in staging before production cutover is attempted.
How does Aegis Softtech migrate Teradata to Snowflake?
Assess the Existing Environment
Full object inventory — tables, views, BTEQ scripts, TPT jobs, stored procedures, UDFs. DBQL query analysis across 90 days. AMP skew and data quality baseline. This inventory is the cost driver for everything that follows.
Choose the Right Migration Strategy
For most Teradata environments: re-architect, not lift-and-shift. PI assumptions, NUSI structures, and BTEQ logic don't belong in Snowflake. For large multi-domain environments: phased by domain — decommissioning progressively to reduce parallel-run cost.
Phased Migration
Migration runs in waves — reference data first, core fact tables second, real-time workloads last. Each wave has its own validation gate before the next begins. Nothing moves to production without sign-off.
Prepare Snowflake Architecture
Schema hierarchy, table design without Primary Indexes, virtual data warehouse configuration by workload type, and RBAC — all designed before data moves. Retrofitting these post-migration is expensive.
Move Data Efficiently
TPT Export to a cloud staging layer (S3 / Azure Blob), then COPY INTO Snowflake. CDC for high-transaction tables that can't tolerate a static snapshot window. Files are pre-split to 100–250MB for optimal load throughput.
Use Snowflake Native Loading Features
COPY INTO for bulk loads. Snowpipe for the CDC tail once the bulk load completes. Zero-copy cloning for validation environments throughout every phase — no additional storage cost.
Our Snowflake implementation team configures loading pipelines with error handling and monitoring from day one.
Migration Tools
- SnowConvert: automated first-pass conversion of DDL, BTEQ, stored procedures
- Qlik Replicate / HVR: CDC-based migration for large, active tables
- dbt: rebuilds ELT transformation logic inside Snowflake
- Airflow / Prefect: replaces BTEQ job scheduling with proper orchestration
Optimise for Large-Scale Migration
Size up data warehouses for migration windows, size down for production. Load by business priority, not table size. Handle AMP-skewed tables separately. Partition large tables into monthly chunks. Resource Monitors configured before execution — not after the first unexpected invoice.
Validate Data Carefully
Automated row count and aggregate validation on every wave. Field-by-field sample reconciliation. The 10–15 canonical business reports identified upfront — those are the sign-off criteria, not a technical checksum. Teradata-Snowflake semantic differences are documented and addressed explicitly.
Optimise Before Go-Live
Top 100 DBQL queries benchmarked against Snowflake before cutover. Warehouses right-sized for production workload patterns. Resource Monitors, query timeouts, and clustering were reviewed. Every role is validated against minimum permissions. Rollback plan tested in staging.
Our Snowflake consulting team runs a pre-cutover health check on every engagement before the switch is flipped.
Expected Timelines for Teradata to Snowflake
The timeline is set by the Phase 1 object inventory, not estimated before it exists. BTEQ script count and parallel-run sign-off are the two variables that move timelines the most.
| Environment | Object Count | Timeline |
|---|---|---|
| Small (<5 TB) | <500 objects | 8–14 weeks |
| Medium (5–50 TB) | 500–2,000 objects | 16–28 weeks |
| Large (50–200 TB) | 2,000–8,000 objects | 28–48 weeks |
| Enterprise (200TB+) | 8,000+ objects | 12–24 months |
Our Team
Teradata to Snowflake Migration Cost Considerations
Teradata TCO is high by design: hardware, capacity licensing, and specialised DBA overhead compound year on year. Here's what drives your migration budget.
What drives the migration budget:
- Object conversion and BTEQ rewrite
The largest variable. Only the Phase 1 inventory makes it estimable. - Parallel-run duration
Directly controllable through phasing and defined sign-off criteria. - Post-migration data warehouse right-sizing
Data Warehouse right-sizing reveals 15–30% in avoidable credit spend within 90 days. - Schema Redesign Investment
The design cost is now a fraction of the rework cost later. Architecture signed off before a single table was migrated.
The Highest-Leverage Investment
SnowConvert is the single biggest cost lever available before execution begins. It converts standard DDL and BTEQ patterns, reserving manual developer hours for the logic that actually requires human judgement.
Reduction in manual rewrite hours on standard BTEQ patterns using SnowConvert — before a single developer touches the codebase.
Only 20–35% of a large Teradata inventory requires manual rewrite: dynamic SQL, macro chains, and DBC.* queries that SnowConvert cannot handle.
FAQs
Partially. SnowConvert converts standard patterns well. Scripts with dynamic SQL, macro chains, or DBC. Queries require manual rewrite, typically 20–35% of a large Teradata inventory.
Nothing directly. Micro-partition pruning handles most filtering needs. Clustering keys are applied selectively to large tables with a dominant filter pattern, not by default.
Yes, until business validation sign-off. The parallel-run duration is controlled by defining sign-off criteria upfront, not left open-ended.
It's a policy redesign, not a migration. TASM workload groups become separate Snowflake virtual warehouses. Throttling becomes Resource Monitors. Most TASM complexity disappears once workloads are isolated to dedicated warehouses.