DATABRICKS MIGRATION · SNOWFLAKE
Migrate from Snowflake to Databricks with a controlled path to production
Consolidate data, analytics and AI on Databricks while protecting business-critical workloads, validating output parity and planning cutover around real dependencies.
A migration partner for the work between assessment and cutover
Official Databricks Consulting Partner
Databricks-focused expertise across data platforms, analytics and production AI.
Workload-by-workload planning
Dependencies, complexity and acceptance criteria shape the migration waves — not table count alone.
Validation and rollback built in
Data reconciliation, production readiness and explicit cutover controls reduce avoidable risk.
Snowflake → Databricks
Modernize the platform without treating migration as a bulk copy
A warehouse migration spans more than tables. SQL logic, orchestration, security, BI dependencies, data quality and operating practices all need a deliberate target design. Dateonic maps those dependencies, prioritizes migration waves and builds a Databricks foundation your team can run after cutover.
What We Migrate
Every layer that keeps a Snowflake workload running
01 · DISCOVERY
Platform & dependency assessment
Inventory databases, schemas, SQL objects, tasks, integrations, consumers and workload criticality; classify complexity and define migration waves.
02 · DATA
Data, history & ingestion
Design secure transfer and synchronization, preserve required history, validate quality and land data in the governed Databricks target.
03 · LOGIC
SQL, transformations & orchestration
Convert or refactor Snowflake SQL, procedures, tasks and pipelines; use migration tooling where it adds value, then review and test the result.
04 · CONTROL
Security, governance & consumers
Map roles and access into Unity Catalog, reconnect BI and downstream tools, and establish lineage, audit and ownership patterns.
Migration Method
A phased route from Snowflake inventory to stable Databricks operations
01 · ASSESS
Inventory the estate
Map data, code, orchestration, security, integrations and downstream dependencies before estimating the move.
02 · DESIGN
Define the target platform
Set the Databricks architecture, Unity Catalog model, workload patterns and non-functional requirements.
03 · PROVE
Migrate a representative slice
Test the target design on a workload that exposes real complexity, performance and dependency risks.
04 · MIGRATE
Move in controlled waves
Prioritize workloads, convert and refactor where needed, and track acceptance criteria for every wave.
05 · VALIDATE
Prove parity and cut over
Compare outputs, data quality, performance and downstream behavior before switching production consumers.
06 · OPTIMIZE
Stabilize and transfer ownership
Tune the new platform, retire agreed source workloads and hand over runbooks, standards and responsibilities.
Coexistence & Cutover
Move in waves, not with a blind big-bang switch
The transition model should match workload criticality. Where it fits the access pattern, Snowflake and Databricks can coexist during migration through selective federation or synchronized data paths. Parallel runs, acceptance criteria and rollback plans create evidence before production consumers switch.
Migration Risk Controls
Confidence before production cutover
Output parity
Validate data, aggregates, business rules and downstream behavior against agreed tolerances before cutover.
Access continuity
Map Snowflake roles, privileges, users and service access into a governed Unity Catalog model.
Operational readiness
Prepare monitoring, runbooks, support ownership, rollback criteria and team enablement before go-live.
Senior Migration Leadership
Architecture decisions stay connected to the cutover
Lukasz Wybieralski, Dateonic CTO and Data Architect, stays close from estate assessment and target design through validation, production readiness and handover — keeping business continuity and long-term platform ownership in view.
FAQ
Snowflake to Databricks migration questions, answered
QUESTION 01
What needs to migrate beyond Snowflake tables?
The scope can include views, SQL logic, procedures, tasks, ingestion and transformation pipelines, integrations, roles and privileges, BI dependencies, quality rules, monitoring and operating practices.
QUESTION 02
Do we have to stop using Snowflake all at once?
No. Many estates are better migrated in waves. Coexistence, selective federation or synchronized data paths can support validation and gradual consumer cutover where the architecture and security model allow it.
QUESTION 03
How do you validate migrated workloads?
Validation combines data reconciliation, business-rule checks, query and pipeline testing, performance evidence, downstream user acceptance and explicit pass or rollback criteria for each wave.
QUESTION 04
Can we migrate only selected workloads first?
Yes. A representative pilot or high-value workload can validate the target design and migration method before you commit to a broader roadmap.