In the data-driven world we live in, migrating to a cloud data platform like Snowflake can revolutionise your organisation’s analytics, scalability, and cost-effectiveness. But the journey from your current on-premise or legacy system to Snowflake doesn’t have to be a nerve-wracking, sleepless ordeal. With proper planning, the right tools, and a structured migration strategy, you can transition smoothly, without jeopardising your data or your sanity.
At DataPillar, we’ve supported enterprises across industries in successful, low-risk Snowflake migrations. In this article, we’ll walk you through a step-by-step framework to help you migrate to Snowflake confidently and securely.
Why Move to Snowflake?
Before diving into the “how”, let’s revisit the “why”. Snowflake is not just another cloud data warehouse. It offers:
- Scalability on demand – Instantly scale up or down with virtually no performance compromise.
- Separation of compute and storage – Pay only for what you use.
- Multi-cloud flexibility – Available on AWS, Azure, and Google Cloud.
- Built-in security and compliance – Enterprise-grade encryption and governance.
- Support for semi-structured data – JSON, Avro, ORC, and Parquet are supported natively.
For companies seeking agility, Snowflake offers a future-proof data architecture.
Step 1: Define the Migration Scope and Objectives
Start with a crystal-clear picture of what you want to achieve. Whether it’s improved performance, reduced costs, or consolidated reporting, your objectives will influence:
- What data do you migrate
- Which workloads take priority
- The order of migration phases
Checklist:
- Identify all source systems (databases, data lakes, ETL tools)
- Classify workloads (reporting, analytics, real-time streaming)
- Determine migration success metrics (e.g., faster query performance, reduced maintenance time)
By setting clear goals, you’ll align business stakeholders, IT teams, and data engineers from the beginning.
Step 2: Perform a Comprehensive Data Assessment
Every successful migration begins with understanding what you’re moving.
Data profiling helps uncover:
- Data quality issues (nulls, duplicates, inconsistencies)
- Schema complexity
- Usage frequency of tables and queries
- Redundant or obsolete datasets
Also, consider compliance obligations. If your industry is subject to GDPR, HIPAA, or ISO standards, ensure that sensitive data is properly flagged and protected both during and after the migration.
Tip: Use tools like Apache Atlas or Collibra for automated data discovery and lineage mapping.
Step 3: Choose the Right Migration Strategy
There’s no one-size-fits-all model for Snowflake migration. Your strategy should be tailored to your infrastructure and risk tolerance.
a. Lift and Shift
- Pros: Quick and simple. Data is copied as-is into Snowflake.
- Cons: Doesn’t optimise for Snowflake’s architecture. Potentially high storage costs.
b. Optimised Migration
- Pros: Transforms schema, redefines data models, and re-engineers ETL pipelines to fit Snowflake best practices.
- Cons: More effort upfront but yields better long-term ROI.
c. Phased or Hybrid Approach
- Migrate one business unit or workload at a time.
- Run both systems in parallel to validate outputs and reduce risk.
Recommendation: Most DataPillar clients benefit from a phased migration with iterative testing and optimisation.
Step 4: Set Up the Snowflake Environment
Before moving any data, configure your Snowflake account correctly:
- Create appropriate roles and permissions aligned with your data governance policies.
- Set up virtual warehouses for separate workloads (ETL, BI, ad hoc queries).
- Configure resource monitors to prevent unexpected costs.
- Choose the correct region and cloud provider (AWS, Azure, or GCP) based on latency and compliance needs.
Also consider setting up Snowpipe for automated data loading and Snowflake’s Time Travel feature for data recovery and change tracking.
Step 5: Build or Modernise Your ETL/ELT Pipelines
Legacy ETL tools may not be compatible with Snowflake’s cloud-native architecture. It’s often best to modernise your data pipelines by switching to ELT and leveraging Snowflake’s compute capabilities.
Popular tools that integrate natively with Snowflake:
- Fivetran – For no-code, prebuilt connectors
- dbt (data build tool) – For SQL-based transformations
- Apache Airflow – For orchestration
- Matillion or Talend – For advanced ETL processing
With Snowflake, heavy transformations can be deferred until after data is loaded (ELT), offering performance gains and simplified operations.
Step 6: Validate, Test, and Compare
Data integrity is non-negotiable. Ensure what’s in Snowflake matches the source system.
Validation Techniques:
- Row counts – Ensure record counts match between source and target.
- Checksums and hashes – Verify data values are identical.
- Query performance comparison – Benchmark report queries across both systems.
Test with real business reports and analytics dashboards. Engage data consumers in UAT (User Acceptance Testing) to confirm that outputs remain consistent and trustworthy.
Step 7: Optimise Performance in Snowflake
Once your data is live, it’s time to tune performance for cost and speed:
- Clustered tables or materialised views for frequent queries
- Use auto-suspend and auto-resume on virtual warehouses to save compute costs
- Result caching to reduce redundant processing
- Partitioning logic via micro-partitions
- Minimise data movement by using external tables or federated queries when necessary
Snowflake also offers insights into query profiling and performance via its Query History and Account Usage views.
Step 8: Decommission Legacy Systems and Monitor Snowflake Usage
Once confident in your Snowflake deployment, start retiring your old systems. This step reduces licensing and infrastructure costs.
Simultaneously, implement robust monitoring and cost tracking:
- Use Snowflake’s Resource Usage dashboards
- Set alerts via Snowflake’s native alerts or third-party tools like Sigma or Metaplane
- Conduct regular data audits and usage reviews to ensure access control and policy adherence
Common Pitfalls to Avoid
Despite its advantages, Snowflake migrations can go awry if rushed or mismanaged. Avoid these mistakes:
- Underestimating data complexity – Not all data is clean or migration-ready.
- Ignoring governance – Uncontrolled access can lead to compliance violations.
- Lack of user training – Snowflake’s interface and approach are different; train users early.
- Overprovisioning warehouses – Leads to unnecessary spend. Start small and scale as needed.
Why Partner with DataPillar?
At DataPillar, we specialise in tailored Snowflake migration services that eliminate the guesswork and reduce risk. Our clients benefit from:
- Industry-specific data architecture design
- ETL modernisation and pipeline refactoring
- Compliance-ready Snowflake environments
- Hands-on training and documentation
- Ongoing optimisation and cost control
We don’t just move your data—we help you unlock Snowflake’s full potential for better decision-making and business agility.
Final Thoughts
Migrating to Snowflake doesn’t have to mean sleepless nights and risky transitions. With careful planning, the right tools, and expert guidance, your organisation can move into the future of cloud data warehousing confidently.
If you’re considering a Snowflake migration, let’s talk. At DataPillar, we’ll guide you through every stage—from assessment to optimisation—ensuring you get the performance, scale, and value Snowflake promises.
Need help with Snowflake migration?
Get in touch with the experts at DataPillar today and take the first step toward a stress-free data transformation.