Databricks vs. Snowflake

It's Not a Feature Fight, It's a

Strategy Call








Scroll to Learn More ↓

Databricks vs. Snowflake

It's Not a Feature Fight

It's a Strategy Call








Scroll to Learn More ↓

James Reid

Data Consultant

Meet James Reid


With over seven years in data engineering, James thrives at the intersection of architecture and analytics. From decommissioning legacy systems to designing modern transformation layers, he brings hands-on expertise with Snowflake, Databricks, Redshift, Talend, and Spark. Known for bridging technical depth with business outcomes, he’s passionate about building scalable, future-ready data platforms.

James Reid

Data Consultant

Meet James Reid


With over seven years in data engineering, James thrives at the intersection of architecture and analytics. From decommissioning legacy systems to designing modern transformation layers, he brings hands-on expertise with Snowflake, Databricks Redshift, Talend, and Spark. Known for bridging technical depth with business outcomes, he’s passionate about building scalable, future-ready data platforms.

If you’re still deep in spreadsheets comparing Snowflake and Databricks feature-by-feature, you might be missing the point.


The smartest organisations aren’t obsessing over who’s winning the feature race — they’re asking a better question: Which platform fits our people, our workloads, and where we want to go?



Choosing a data platform isn’t just a technical decision. It’s a strategic one that impacts ROI, compliance, team enablement, and how quickly you can operationalise AI.


If you’re still deep in spreadsheets comparing Snowflake and Databricks feature-by-feature, you might be missing the point.


The smartest organisations aren’t obsessing over who’s winning the feature race — they’re asking a better question: Which platform fits our people, our workloads, and where we want to go?


Choosing a data platform isn’t just a technical decision. It’s a strategic one that impacts ROI, compliance, team enablement, and how quickly you can operationalise AI.

The Real Challenge: Strategy Before Selection

Modern enterprises are drowning in data but starving for insight. The demand for real-time dashboards, predictive models, and AI-driven decisions is growing but infrastructure and skills are often the bottleneck.



Snowflake and Databricks approach this challenge differently: one optimises for simplicity and governance; the other for flexibility and engineering control.


Snowflake: Analytics Without the Agony

Snowflake is the choice for teams who want fast results without building a mini engineering department. It’s clean, scalable, and excels with structured and semi-structured data.


Best for:

  • SQL-heavy teams with limited engineering headcount
  • Regulated industries where governance and audit readiness are non-negotiable
  • Organisations that value predictable consumption-based pricing


Business outcomes we’ve seen:

  • Regulatory reporting live in weeks, not months
  • Immediate BI integration with Tableau, Power BI, and others
  • Minimal maintenance freeing up resources for higher-value work


Snowflake is expanding into ML and AI with Snowpark and Cortex — making it increasingly viable for organisations who want a unified platform for analytics and AI.

Snowflake: Analytics Without the Agony

Snowflake is the choice for teams who want fast results without building a mini engineering department. It’s clean, scalable, and excels with structured and semi-structured data.


Best for:

  • SQL-heavy teams with limited engineering headcount
  • Regulated industries where governance and audit readiness are non-negotiable
  • Organisations that value predictable consumption-based pricing


Business outcomes we’ve seen:

  • Regulatory reporting live in weeks, not months
  • Immediate BI integration with Tableau, Power BI, and others
  • Minimal maintenance freeing up resources for higher-value work


Snowflake is expanding into ML and AI with Snowpark and Cortex — making it increasingly viable for organisations who want a unified platform for analytics and AI.



Databricks: The Engineer’s Playground

If your data team thrives on Spark, Python, and building end-to-end ML pipelines, Databricks is a powerhouse. It’s a full-blown engineering ecosystem with deep AI capabilities.


Best for:

  • Engineering-heavy organisations
  • AI and ML workloads needing native support for frameworks like PyTorch and TensorFlow
  • Complex, high-throughput streaming use cases

Business outcomes we’ve seen:

  • Real-time operational insights for utilities and logistics
  • Collaborative data science at scale
  • Fine-grained tuning for cost optimisation (if you have the skills to manage it)


With tools like Genie (Conversational AI) and Mosaic (Generative AI management), Databrickscontinues to lead in applied AI innovation.


Cost: Predictability vs. Flexibility

Snowflake makes cost forecasting easy. Databricks can be more cost-effective but only if actively managed. If your CFO values ‘no surprises’, Snowflake’s predictability wins. If your engineers thrive on tuning and optimisation, Databricks can deliver strong ROI.




Governance: Out-of-the-Box vs. Fully Configurable


Snowflake provides straightforward, centralised governance. Databricks offers Unity Catalog and schema evolution, but with greater complexity. The right choice depends on your compliance obligations and your team’s appetite for configuration.



Cost: Predictability vs. Flexibility

Snowflake makes cost forecasting easy. Databricks can be more cost-effective but only if actively managed. If your CFO values ‘no surprises’, Snowflake’s predictability wins.

If your engineers thrive on tuning and optimisation, Databricks can deliver strong ROI.




Governance: Out-of-the-Box vs. Fully Configurable


Snowflake provides straightforward, centralised governance. Databricks offers Unity Catalog and schema evolution, but with greater complexity. The right choice depends on your compliance obligations and your team’s appetite for configuration.

My Take

When I’m thinking of our clients and their decision making process, I weigh three things first:


  1. People: What skills do you already have in-house?
  2. Priorities: Is your focus on governed analytics or engineering flexibility?
  3. Path: Where do you want to be in 3 years and how fast do you need to get there?


And my personal stance? Running both adds unnecessary complexity unless there’s a very specific, high-value use case.


The organisations who get this right don’t just pick a tool, they pick the tool that amplifies their team’s strengths, meets their regulatory needs, and accelerates their AI ambitions.


And if you want true long-term flexibility? Build with open table formats like Apache Iceberg so you can move between platforms if your strategy shifts.