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Migration and Adoption Strategies — SA Quick Reference

What It Is

A strategic transition of workloads from rigid, legacy warehouses to the Snowflake Data Cloud. It involves choosing the right balance between rapid "lift-and-shift" for speed and "re-architecting" for long-term efficiency.

Why Customers Care

  • Eliminate Scaling Bottlenecks: Remove the "Data Gravity" and massive overhead of scaling legacy on-prem hardware (Teradata, Netease, Hadoop).
  • Minimize Business Risk: Avoid the "Big Bang" failure by using phased, workload-based migrations that ensure continuous operations.
  • Accelerate ROI: Shift from expensive, heavy-weight batch ETL to automated, real-time pipelines using Snowflake-native features.

Key Differentiators vs Alternatives

  • Flexible Migration Paths: You aren't locked into a single pattern; you can re-platform specific workloads to use Snowpark while lifting-and-shifting others.
  • Automated Data Integrity: Built-in capability to run "Parallel Runs" and reconciliation engines to ensure 100% parity between old and new systems.
  • Modernization, Not Just Migration: Unlike simple rehosting, our strategy focuses on replacing technical debt with Dynamic Tables and Streams/Tasks.

When to Recommend It

Recommend this when enterprise customers are hitting a "scaling wall" with legacy on-prem warehouses or aging Hadoop clusters. Look for signals of high maintenance costs, rigid data delivery schedules, or an organizational push to move from batch-heavy processing to real-time analytics.

Top 3 Objections & Responses

"If we just lift-and-shift, won't we just move our bad, expensive SQL to the cloud?" → We can use rehosting for speed, but the real strategy is "Re-platforming"—adjusting your code to use Snowflake-native features like Snowpark to actually drive down compute costs.

"A 'Big Bang' migration is too risky for our business operations." → We don't recommend a Big Bang. We utilize phased, workload-based migrations and "Coexistence" architectures to ensure your business never misses a beat.

"How can we trust that the data in Snowflake is identical to our legacy system?" → We implement a rigorous validation layer using row counts, schema checks, and aggregate checksums (like comparing SUM(revenue)) to guarantee total data parity.

5 Things to Know Before the Call

  1. The Cost Trap: Moving inefficient, procedural-heavy SQL "as-is" can lead to unexpected Snowflake compute spikes.
  2. The ROI Sweet Spot: The biggest value is found in "Re-architecting" (using Dynamic Tables) rather than just moving tables.
  3. The "Double Pay" Period: Be prepared to discuss the temporary cost of running both legacy and Snowflake environments during the coexistence phase.
  4. Validation is Non-Negotiable: Migration success is measured by data reconciliation, not just data movement.
  5. Change Management is Critical: The technical move is only half the battle; the customer needs a plan to retrain SQL devs on Snowflake-native syntax.

Competitive Snapshot

vs Advantage
Teradata / Netease Break free from "Data Gravity" and the high cost of scaling physical hardware.
Hadoop Clusters Replace rigid, heavy-weight batch pipelines with automated, near real-time processing.

Source: Migration and Adoption Strategies course section