Teradata-to-Snowflake Migration Services

For teams responsible for a working data warehouse

You may be approaching a Teradata renewal, evaluating Snowflake, or already converting workloads. The decision affects more than where tables are stored: overnight processing windows, financial totals, downstream reports, and operating costs all need acceptance criteria.

We work with data platform leaders, architects, and engineering teams to make those requirements explicit. An assessment can also conclude that remaining on Teradata is the better option for your workload.

What needs attention when moving from Teradata to Snowflake?

• SQL and business semantics. Data types, numeric precision, rounding, date handling, duplicate rows, and DML behavior need representative tests. A converted statement that runs is only the first check.
• Load and transformation pipelines. BTEQ scripts, bulk loads, stored procedures, scheduling, restart logic, and dependencies must be inventoried before deciding what to convert or rebuild.
• Performance and cost. Teradata distribution and indexing choices do not translate directly to Snowflake. Warehouse sizing, concurrency, pruning, and transformation patterns need workload-based benchmarks.
• Operational continuity. Incremental loads, reconciliation, access controls, downstream consumers, and rollback conditions must be included in the cutover plan.

Migration scope and deliverables

We agree the domains, responsibilities, acceptance criteria, and exclusions before delivery. Depending on your starting point, an engagement can cover the full programme or a defined phase.

1. Assessment and migration plan

Review the source architecture, workload inventory, data dependencies, and current cost baseline. Identify compatibility risks and prioritise migration waves.

Deliverables: architecture review, data landscape map, risk register, and a costed roadmap with documented assumptions.

2. Target design and proof of concept

Choose a representative workload, including difficult transformations and peak-load conditions. Design the Snowflake implementation and compare results, runtime, and consumption against the agreed baseline.

Deliverables: target architecture, a working proof of concept, benchmark results, and a go/no-go decision for the next phase.

3. SQL conversion and pipeline migration

Migrate by domain, adapting SQL and rebuilding pipelines where the architecture requires it. Use dbt and Airflow where they fit the target operating model. Include repeatable loads, restart behavior, and regression checks.

Deliverables: migrated code and pipelines, documented dependencies, and repeatable deployment and test procedures for the agreed scope.

4. Reconciliation and cutover

Compare source and target row counts, aggregates, and business-rule outcomes. Investigate differences, rehearse the transition, and agree the rollback triggers before production cutover.

Deliverables: reconciliation evidence, performance acceptance results, and a cutover and rollback runbook.

5. Handover and operational readiness

Prepare your engineers and analysts to run the migrated workloads. Document scheduling, monitoring, recovery procedures, and cost controls.

Deliverables: operational documentation, knowledge-transfer sessions, and an agreed handover checklist.

Experience on both sides of the migration

Our published Swiss retail case study describes a Teradata-to-Snowflake engagement covering workload analysis, target data-model redesign, ETL rebuilding, and automated source-to-target reconciliation. It explains how row counts, aggregates, and business logic were checked during delivery.

Read the Swiss retailer migration case study. Named references are available under NDA for qualified evaluations.

Our technical articles also show the issues we examine during migration:

• How Teradata and Snowflake DML and deletion behavior differ
• Why similar Teradata migrations can produce different Snowflake costs

Start with a clear scope

Free 30-minute discovery call

Discuss your current platform, the reason for considering Snowflake, and your main constraints. We use the conversation to identify a suitable next step. Detailed analysis and a costed migration roadmap are part of the paid assessment.

4-day platform diagnostic — €4,800

A focused entry engagement before committing to a larger programme. The diagnostic fee is credited in full against the assessment when commissioned within 90 days.

Migration assessment — €19,500

A fixed-price entry engagement, typically 2–3 weeks with 1–2 senior engineers depending on effort. It covers architecture review, landscape mapping, migration complexity, risk identification, and planning.

Prices exclude VAT. Implementation is scoped separately; duration and cost depend on workloads, dependencies, data volume, and required migration iterations. See our service pricing and delivery model for the wider engagement terms and an illustrative programme estimate.

Common questions

Can you help if we have already started migrating?

Yes. The first step is to review your current progress, unresolved compatibility issues, validation coverage, and performance or cost baseline. We then agree a targeted scope.

Can an automated SQL converter do the migration?

Conversion tools can accelerate parts of the work. You still need to verify business results, redesign unsuitable workload patterns, integrate pipelines, and prove operational readiness.

How long does a migration take?

The assessment is typically 2–3 weeks. A production migration needs its own schedule, based on the inventory, proof of concept, migration waves, and acceptance requirements. We establish that plan before committing to delivery dates.

Will Snowflake necessarily cost less?

No. The answer depends on your usage, architecture, commercial terms, and operating practices. We compare representative workloads and document cost assumptions instead of promising a percentage saving.