How to Migrate from Snowflake to Databricks: Step-by-Step Guide

Snowflake has delivered well for traditional analytics, offering strong performance in SQL-heavy environments. But today’s data teams demand more, real-time processing, advanced machine learning, and seamless governance within one unified system. 

As data volumes grow and AI becomes central to operations, older architectures begin to strain under complexity and cost.

Businesses are no longer choosing between tools; they’re choosing between agility and fragmentation. In this context, unified lakehouse platforms offer a way forward. 

The case for Snowflake to Databricks migration is not just technical. It is a question of long-term sustainability, speed, and strategic control.

Why Fragmented Architectures No Longer Work

Traditional architectures centered around Snowflake were never built to handle the modern explosion of real-time data, AI workloads, and fine-grained governance demands. 

Snowflake’s rigid, SQL-first design becomes a bottleneck the moment teams attempt to scale machine learning models, build streaming pipelines, or operate across open file formats like Parquet or Delta. 

What begins as an efficient warehouse quickly turns into a performance and cost trap, especially when complex ETL workloads are involved.  A recent technical study found that Databricks outperformed Snowflake in both execution time and cost efficiency when tested on large-scale predictive modeling due to its ability to process data directly on open storage layers without transformation overhead.

Tool fragmentation only adds to the problem: multiple orchestration tools, siloed security policies, duplicated data copies. The result is more than inefficiency, it’s a system that becomes harder to maintain, scale, or secure. That’s why Snowflake to Databricks migration is no longer a luxury; it’s a technical necessity for building resilient, unified data platforms.

The Agitation of Delay: Why Waiting Makes It Worse

It rarely starts with a disaster. Most teams live with a few slow dashboards, failed sync jobs, or schema mismatches here and there. Tolerable — until it snowballs.

Each tool in a fragmented stack introduces invisible friction: delays in reporting, query retries, repeated transformations. 

Over time, these delays stack up. In multi-tool environments, 84% of data warehouse costs can stem from running business intelligence queries alone, with no added analytical value.

Maintaining two platforms means every logic change, every column permission, and every schema update must be aligned twice. This doubles engineering effort and halves delivery speed.

Performance also degrades; some queries miss SLAs even after optimization. Governance gets trickier — audit trails scatter, access control misaligns, and risks multiply. And while the dashboards crawl and teams firefight sync issues, operational costs silently inflate.

Organizations delaying Snowflake to Databricks migration not only absorb higher infrastructure costs but also sacrifice agility and accuracy, every single day.

Entrans’ Experience: Centralizing BI Workloads During Migration

For Entrans, managing business intelligence workloads across a dual-system setup created inefficiencies that could no longer be ignored. Their BI dashboards ran on top of Snowflake, while data transformation and storage were already managed within a Databricks lakehouse. 

This disconnect forced the team to duplicate tables, introduce unnecessary ETL processes, and juggle query syntax differences across tools. The solution emerged through a complete Snowflake to Databricks migration designed to centralize the architecture and reduce costs.

The migration was executed with precision. Entrans followed an incremental rollout strategy where SQL queries from BI tools were refactored and pointed directly to Delta tables.

Automated scripts adjusted syntax differences and validated output across environments. Tuning the performance of Delta tables using techniques like liquid clustering, optimized file sizes, and intelligent partitioning helped improve dashboard responsiveness. Validation was data-driven.

Over 20,000 SQL queries were tested, monitored, and gradually transitioned without interrupting reporting workflows.

Post-migration, the BI layer became lighter, faster, and more stable. More than 98 percent of reports ran without errors, and over 70 percent executed within the performance target window of 10 seconds. 

Most notably, Entrans reported a 20 percent reduction in compute and storage costs. The Snowflake to Databricks migration not only simplified the tech stack but unlocked long-term performance and financial gains.

The Shift: What Transformation Looks Like in Real Terms

Change doesn’t always come with a bang. Sometimes, it’s the slow grind of inefficiency that finally forces a decision. In traditional environments, teams operate across multiple disconnected systems. 

One for storage, another for transformation, a third for analytics. The result is friction. Data is copied more than it’s queried. Queries are delayed. Engineers spend more time fixing pipelines than improving outcomes. And while the system may not be broken, it becomes a bottleneck. That’s when the switch happens. And that’s when things start to move.

A unified platform consolidates all operations — data ingestion, transformation, analytics, and machine learning — in one ecosystem. In a controlled benchmark study, switching from a traditional Snowflake setup to Databricks improved machine learning query performance by over 60% and reduced overall latency by up to 40% for streaming workloads.

With Delta Lake handling real-time data natively, the need to export, transform, and re-import vanishes. Queries that once ran in minutes now return in seconds.

Pipelines become more resilient. Dashboards stay live. And data lives in one place, offering a single, consistent view across the organization.

This is not just a shift in speed. It’s a shift in how teams work. The duplication ends. The complexity fades. The stack becomes more predictable, easier to maintain, and built for scale.

That is the real power of Snowflake to Databricks migration — not just in what changes, but in what it unlocks.

When It’s Time to Move, Move with a Team That’s Done It Before

Migration is never just about shifting data. It’s about architecture, governance, performance, and trust. A successful Snowflake to Databricks migration demands more than technical know-how — it requires judgment, repeatable processes, and an understanding of what breaks when things move too fast.

Entrans brings all of that.

With proven capability in modern data stack transformations, they support teams from planning to post-migration optimization, ensuring the transition is not only successful but sustainable.

Every migration has a window. The question is whether it’s seized — or missed.

→ Talk to Entrans About Your Migration