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Solutions → Snowflake → Databricks

Snowflake to Databricks Migration Consulting

Modernize your data platform with a structured migration from Snowflake to Databricks — covering architecture, data pipelines, workloads, governance, performance optimization and production cutover.

Snowflake
→
Databricks
Book a Migration AssessmentTalk to a Databricks Architect

Built for these industries

InsuranceHealthcareBanking & Financial ServicesRetailManufacturingAgricultureLogistics

Why migrate

Why companies move from Snowflake to Databricks

We don't tell clients Snowflake is a bad platform — most migrations are driven by a specific need Databricks fits better.

Unifying data engineering, analytics and AI on one platform

Building a Lakehouse architecture on open formats

Advanced ML/AI workloads that need direct data access

Open data formats such as Delta and Parquet

Centralized governance with Unity Catalog

Streaming and batch workloads on one engine

Existing Databricks investment elsewhere in the business

Consolidating multiple data platforms into one

Scope

What Karsient migrates

Data

  • Snowflake tables
  • Views
  • Schemas
  • External tables
  • Historical data

SQL

  • SQL transformations
  • Stored procedures
  • UDFs
  • Queries

Pipelines

  • ETL/ELT
  • Airflow
  • dbt
  • ADF
  • Custom pipelines

Security

  • Roles
  • Permissions
  • Access policies
  • Data governance

Analytics

  • BI workloads
  • Dashboards
  • Reporting queries

Methodology

Our migration methodology

01

Discovery

Understand business goals, current systems, and constraints before proposing anything.

02

Assessment

Inventory databases, tables, pipelines, workloads, dependencies and usage across the estate.

03

Architecture

Design the target Databricks Lakehouse architecture, Unity Catalog, and governance model.

04

Design

Define detailed technical design — data models, pipeline patterns, and platform standards.

05

Migration

Move data, SQL, transformations and pipelines in planned, testable phases.

06

Engineering

Build and re-engineer pipelines, jobs, and integrations on the target platform.

07

Validation

Compare record counts, aggregations, business rules, data quality and performance against source.

08

Optimization

Tune file sizes, partitioning/clustering, SQL, compute, and Delta table configuration.

09

Deployment

Move production traffic with a rollback plan and minimal disruption to downstream consumers.

10

Operate

Hand over with documentation and training, or continue under a managed-services partnership.

Architecture

Target architecture: Snowflake → Databricks

Snowflake
Snowflake
Tables
Views
Pipelines
SQL Translation & Warehouse Migration
DatabricksDatabricks Lakehouse
Bronze
Silver
Gold
Unity Catalog · Data Quality · Observability
SQL · BI · AI · GenAI
Applications
Snowflake
SQL
Delta Lake
Unity Catalog
dbt

What to expect

Migration challenges we plan for

1

Snowflake SQL to Databricks SQL differences

2

Stored procedure conversion

3

UDF migration

4

Data type differences

5

Incremental loading & CDC

6

Pipeline orchestration changes

7

Security model & role/permission mapping

8

Performance differences and query re-tuning

9

Data validation across both platforms

10

BI tool connectivity & cutover strategy

Why Karsient

Why Karsient for Snowflake → Databricks

We don't simply move workloads — we redesign the platform for Databricks, combining architecture expertise, data engineering delivery, migration engineering, and AI/MLOps integration under one team.

Architecture expertise

Data engineering

Migration engineering

AI/ML integration

Migration assessment

Is Snowflake → Databricks right for your organization?

Get a technical assessment covering architecture, workloads, migration complexity, dependencies, risks and optimization opportunities.

Get a Free Migration Assessment

FAQ

Common questions

Yes — tables, views, schemas, external tables and historical data are all in scope, migrated into Delta format on the Lakehouse.

Related

Other Databricks solutions

Talend

Talend → Databricks

Re-platform Talend ETL jobs onto native Databricks pipelines.

Learn more →

Vertica → Databricks

Move Vertica analytical workloads to the Databricks Lakehouse.

Learn more →
Microsoft Azure

Azure Databricks

Deploy and operate Databricks natively on Microsoft Azure.

Learn more →
AWS

AWS Databricks

Deploy and operate Databricks natively on Amazon Web Services.

Learn more →

Ready when you are

Planning a Snowflake → Databricks migration?

Let's assess your current platform, identify migration risks and design your target Databricks architecture.

Schedule a Migration Assessment