If you’re planning a Snowflake to Databricks migration, it’s important to understand this upfront, this is not just a migration project. It’s a complete evolution of how your data platform operates.
Organisations often begin this journey with a specific goal in mind, reducing costs, improving performance, or modernizing their data stack. But as the migration unfolds, it becomes clear that this shift impacts everything: how data is stored, how it flows through pipelines, how teams interact with it, and how insights are generated.
Based on real-world implementations, here are deeper lessons and insights that can help you approach this transition with clarity and confidence.
1. Rethinking the Source of Truth
One of the most foundational changes during migration is redefining where your data lives and how it is accessed. In traditional warehouse-centric architectures, platforms like Snowflake often act as both the central storage layer and the compute engine for transformations and analytics.
However, in a modern Lakehouse approach cloud storage (like S3) becomes the single source of truth, processing happens directly on top of that data, and systems become loosely coupled instead of tightly dependent.
Why this shift is powerful:
When your data is centralized in storage rather than locked inside a compute system:
- You avoid unnecessary duplication across pipelines
- You gain flexibility to use multiple tools if needed
- You simplify governance and access control
- You reduce overall storage and compute costs
This architectural shift also makes your system more resilient. Even if processing layers change, your core data remains stable and accessible.
2. Migration Is Not Just About Moving Data
It’s tempting to think of migration as a simple “copy-paste” operation, move data from one platform to another and you’re done. But in reality, migration involves rethinking how your entire data ecosystem functions.
This includes:
- Evaluating whether existing pipelines are still relevant
- Identifying redundant or outdated datasets
- Simplifying overly complex workflows
- Aligning data structures with modern use cases
In large-scale environments, this becomes even more critical. Organisations often deal with thousands of tables, multiple ingestion sources, complex transformation logic, and interconnected reporting systems.
Without careful planning, simply moving everything “as-is” can carry forward inefficiencies into the new system.
Migration should be treated as an opportunity to clean, optimize, and modernize - not just transfer.
3. Expect Changes in How Data Is Processed
Every data platform has its own strengths, and this becomes very clear during migration. What worked well in a warehouse-based system may not be optimal in a distributed processing environment.
During migration, teams often discover:
- Some workflows are unnecessarily complex
- Certain transformations can be simplified
- Data processing can be made more efficient
This leads to important improvements such as:
- Breaking down large, monolithic pipelines into smaller, manageable steps
- Reducing dependency chains between processes
- Improving data freshness and processing speed
This phase is not just about adapting - it’s about evolving your data processing strategy to match modern requirements.
4. Optimization Should Start Early (Not Later)
One of the biggest mistakes organisations make is postponing optimisation until after migration is complete. But by that point, inefficient patterns may already be deeply embedded in the new system. Instead, optimisation should be built into every stage of migration.
What this looks like in practice:
- Designing pipelines with efficiency in mind from day one
- Eliminating redundant transformations early
- Structuring workflows to minimize unnecessary processing
- Aligning data models with actual usage patterns
This approach ensures that costs remain controlled from the beginning, performance issues are avoided rather than fixed later, and the system is scalable as data grows. In short, early optimisation helps you start strong instead of fixing problems later.
5. Validation Is Non-Negotiable
Data is the foundation of business decisions. If the data is wrong, everything built on top of it is at risk. That’s why validation is one of the most critical steps in migration.
A strong validation strategy goes beyond simple checks. It involves:
- Comparing outputs between legacy and new systems
- Ensuring key business metrics remain consistent
- Verifying data completeness across pipelines
- Monitoring discrepancies over time
Many organizations adopt a parallel run strategy, where both systems operate simultaneously until confidence is established. This provides a safety net during migration, time to identify and fix issues, and assurance that business operations won’t be disrupted. Validation is not just a step, it’s a continuous process that builds trust in the new system.
6. Handling Edge Cases and Unexpected Issues
Even with the best planning, migration will always bring surprises. Some issues only become visible when systems are actively running in the new environment.
Common examples include:
- Data formats that behave differently than expected
- Pipelines that depend on undocumented processes
- Edge cases in transformations that break under scale
- Performance issues in specific workloads
The key is not to avoid these challenges, but to be prepared for them. Successful teams expect uncertainty, build flexibility into their plans, prioritise quick debugging and resolution, and maintain strong communication across teams.
This mindset turns unexpected issues into manageable tasks rather than major roadblocks.
7. Managing Organizational Change
Technology is only one part of migration. The bigger challenge often lies with people. Moving to a new platform means:
- New workflows
- New tools and interfaces
- New ways of thinking about data
Without proper support, teams may struggle to adapt, slowing down adoption and reducing the impact of migration. That’s why organisations should invest in:
- Training programs tailored to different roles
- Clear documentation and best practices
- Internal champions who can guide teams
- Continuous enablement and support
When teams are confident and comfortable with the new system, the transition becomes much smoother — and the value of the platform is realized much faster.
8. Why Databricks Is Becoming the Preferred Choice
Many organisations are making the shift because Databricks offers a more modern and unified approach to data. Instead of separating tools for Data engineering, Analytics, and Machine learning.
Databricks brings everything together into a single platform. This provides several advantages:
- Reduced complexity from managing fewer tools
- Faster collaboration across teams
- Better scalability for growing data needs
- Cost efficiency through optimized processing
It also enables organisations to go beyond traditional analytics and explore advanced use cases like AI and real-time data processing .
9. Think Beyond Migration, Think Transformation
The most successful organisations don’t treat migration as a one-time project. They treat it as a transformation initiative.
This means focusing on:
- Long-term scalability rather than short-term fixes
- Simplified and maintainable architectures
- Systems that can evolve with business needs
- Enabling innovation through better data access
When approached this way, migration becomes more than just a technical upgrade, it becomes a foundation for future growth.
How We Approach Migration at KPI Partners
At KPI Partners, we’ve worked with organisations dealing with complex, large-scale data ecosystems, and we understand how challenging migration can be without the right approach. That’s why we see Snowflake to Databricks migration as more than a technical task, it’s a strategic transformation.
Through our Snowflake to Databricks Migration Accelerator, we help organizations navigate this journey in a structured and efficient way. Learn More: https://www.kpipartners.com/snowflake-to-databricks-migration-accelerator-kpi-partners
From our perspective, success comes from combining deep technical expertise with a strong understanding of business goals.
We focus on:
- Understanding the full data landscape, not just isolated systems
- Identifying risks and inefficiencies early
- Designing architectures that are scalable and future-ready
- Ensuring data consistency and reliability
- Supporting teams throughout the transition and beyond
Our goal is simple, not just to complete the migration, but to help organisations build a data platform that truly drives value.
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