Redshift to Snowflake Migration: A Strategic Guide for Modern Data Teams

Amazon Redshift has served many organizations well. But as data usage matures — more users, more concurrency, more advanced analytics, and growing AI ambitions — architectural constraints begin to surface.

Why teams are migrating from Redshift to Snowflake

Most Redshift to Snowflake migrations are not driven by dissatisfaction.

They’re driven by strategic questions like:

• Can our data platform scale with usage and diverse workloads?
• Are our engineering resources focused on innovation or maintaining infrastructure?
• Do we trust our metrics across the business?
• Are we architected for AI, semi-structured data, and advanced analytics?
• Is our governance keeping pace with growth?
• Can we support enterprise apps, analytics, and data science on a single auto-scaling platform?
• Are we able to securely share and extend data across our ecosystem?

For organizations thinking about data architecture as part of long-term strategy, Snowflake represents a shift in operating model,  not just a new warehouse.

Snowflake vs. Redshift: The architectural shift

Both platforms are cloud data warehouses. The key difference is how they manage compute and storage.

Capability	Amazon Redshift	Snowflake
Architecture	Tightly coupled compute & storage	Decoupled compute, storage, and services
Scaling	Resize cluster (data redistribution required)	Scale virtual warehouses instantly
Concurrency	Limited by cluster size	Multi-cluster concurrency scaling
Maintenance	Manual VACUUM / ANALYZE	Automated background services
Workload Isolation	Shared cluster	Independent compute per workload

Snowflake’s decoupled architecture allows teams to:

• Isolate ingestion, transformation, and BI workloads
• Scale compute without re-architecting
• Handle usage spikes gracefully
• Control costs at the workload level
• Bring business analytics, data science, and data engineering teams together on a single platform with integrated tooling

As analytics adoption grows, these differences become increasingly important.

Common reasons organizations make the move

Across industries, we typically see four drivers behind a Redshift to Snowflake migration.

Pipeline fragility & data trust issues

Over time, Redshift environments accumulate complexity:

• Distribution key tuning
• Manual maintenance tasks
• Hard-coded business logic
• Inconsistent metric definitions

As teams scale, this complexity leads to slower iteration and lower trust in reporting.

Snowflake simplifies physical design and makes it easier to centralize and govern transformation logic, which improves consistency and reliability.

Performance & concurrency limitations

As more users access the warehouse:

• Queries begin to queue
• Dashboards slow down during peak usage
• ETL jobs compete with reporting workloads

Snowflake virtual warehouses allow teams to isolate workloads and scale independently, reducing contention without architectural redesign. This shift moves teams from firefighting performance issues to focusing on uptime, reliability, and delivering consistent business value.

Growing need for governance

As data access expands across departments:

• Role sprawl increases
• Permissions become inconsistent
• Auditing becomes more important

A migration creates an opportunity to reset governance:

• Define clear role hierarchies
• Implement environment promotion standards
• Establish testing practices
• Monitor costs proactively

Governance isn’t just about compliance,  it’s about operational clarity.

AI & advanced analytics readiness

Organizations exploring:

• Predictive modeling
• Near real-time dashboards
• LLM use cases
• Data activation workflows

…require clean, governed, and well-structured data, a clearly defined semantic layer, and elastic compute.

Snowflake’s architecture supports these use cases, but only when implemented thoughtfully.

The migration often becomes the moment when teams modernize modeling standards and clean up technical debt.

A practical migration framework

While every environment differs, most Redshift to Snowflake migrations follow a structured path.

Assessment & planning

Strong migrations start by aligning architecture to strategy.

We begin by asking:

• What KPIs are most critical to the business?
• What should analytics-ready data products look like?
• How should domain-aware models be structured?
• How can we design a semantic layer that supports both BI and AI use cases?

Only then do we inventory objects, analyze workloads, and map dependencies. Clear scoping prevents surprises and ensures the new Snowflake environment is built for impact, not just compatibility.

Environment & security setup

• Design a Snowflake warehouse strategy
• Translate Redshift users and groups into Snowflake RBAC
• Configure SSO and network policies
• Establish dev/test/prod separation

Migration is often the right time to improve governance, not just replicate it.

Code conversion & modernization

One of the most time-intensive steps is translating Redshift SQL and objects into Snowflake-compatible syntax.

Snowflake’s SnowConvert AI can significantly accelerate this phase by:

• Automating large portions of SQL and DDL translation
• Flagging unsupported constructs
• Identifying areas requiring manual refinement

For many teams, SnowConvert reduces the manual effort involved in code conversion.

However, strong migrations go beyond automated translation. They also:

• Rethink Redshift-specific physical design assumptions
• Refactor stored procedures where needed
• Simplify legacy logic
• Align models to Snowflake’s execution patterns

The goal isn’t to recreate Redshift in Snowflake,  it’s to leverage Snowflake’s strengths.

Increasingly, modernization work is also being accelerated through Snowflake-native AI capabilities such as Cortex Code. Because Cortex operates within the Snowflake environment itself, with awareness of schemas, metadata, and architectural patterns,  it can assist with scaffolding dbt models, refactoring legacy logic, and debugging transformation failures in context.

When used thoughtfully, this reduces repetitive engineering effort while maintaining architectural standards. It doesn’t replace engineering judgment,  it enhances it.

As AI becomes embedded directly into the data platform, migration delivery is shifting from purely manual execution toward AI-augmented modernization.

Data migration

A common approach includes:

• Using Redshift’s UNLOAD to export data to S3
• Loading into Snowflake via COPY INTO
• Validating row counts and aggregates
• Benchmarking performance

Initial warehouse sizing and parallelization strategies can significantly impact migration speed.

Pipeline & BI transition

• Update ingestion jobs to target Snowflake
• Refactor transformation logic where modernizing
• Repoint dashboards and reporting tools
• Conduct performance testing

Workload isolation often results in immediate improvements for BI users.

Validation, cutover, & optimization

Before go-live:

• Conduct structured validation and UAT
• Perform final incremental sync
• Keep Redshift read-only temporarily as fallback

After migration:

• Right-size warehouses
• Enforce auto-suspend
• Monitor credit consumption
• Refine governance practices

Long-term value depends on how the platform is operated, not just how it’s migrated.

Lift-and-shift vs. modernization

Organizations typically choose between:

Lift-and-shift

• Faster execution
• Lower immediate disruption
• Preserves legacy modeling patterns

Modernization

• Cleans technical debt
• Standardizes models
• Improves governance
• Designs for scalability and AI use cases

Many teams choose a hybrid approach: migrate efficiently, then modernize in structured phases. The right answer depends on business urgency and long-term goals.

The bigger picture

Migrating from Redshift to Snowflake is not just a technical project.

It’s an opportunity to:

• Reset modeling standards
• Improve data trust
• Reduce operational overhead
• Enable higher concurrency
• Prepare for AI and advanced analytics

For organizations that view data architecture as part of long-term strategy, the migration decision should be aligned with where the business is headed — not just where the warehouse is today.

Data modernization and migration with Atrium

Atrium is an AI-native data modernization and Snowflake consulting partner. We help data-mature organizations migrate from Redshift to Snowflake using tools like SnowConvert — while designing scalable, governed, and future-ready data platforms.

If you’re evaluating a Redshift to Snowflake migration, we’re happy to share what a thoughtful, low-risk modernization path looks like.