Databricks and Snowflake: Where Things Stand Today

Disclaimer: This post was written in May 2026. Both platforms are evolving at a rapid pace - specific features and tooling may have changed by the time you read this.

Another Databricks vs Snowflake post? Yes. But hear me out.

Databricks and Snowflake are two of the most capable data platforms available today - but most of what's written about them is either outdated, sponsored, or written by someone who's only used one of them. Both platforms look very different today than they did two years ago, and the usual "Snowflake is SQL, Databricks is Spark" narrative doesn't quite hold anymore.

We work with both regularly. Here's where things stand today, and when we'd reach for one over the other.

Where things stand

Both platforms have been on an aggressive feature release cycle, and they've been converging in interesting ways. Snowflake has been investing heavily in Python and AI/ML capabilities - though Python in Snowflake has historically been fragmented across UDFs, Snowpark, and stored procedures. Snowflake Workspaces have improved the iterative development experience, but it's still not as natural a primary Python environment as Databricks. Databricks has been doubling down on SQL, governance, and business-facing tooling. They're not the same product, but the gap has narrowed.

That said - they still feel fundamentally different to work with. The simplest mental model that holds up under scrutiny: Snowflake is a platform-first experience, Databricks is an engineering-first platform. That distinction runs through everything - from how you debug failures to how you onboard new team members.

When I'd choose Snowflake

If your team lives in SQL - Snowflake is your home.

Everything in Snowflake is SQL-first - and that shows up in how predictable and debuggable it is for non-engineers. Your data engineers, analysts, and even some business users can operate in the same environment without needing to context-switch into Python or notebooks. Layer some orchestration on top and you've got yourself a solid data platform that's genuinely easy to maintain. Databricks is increasingly SQL-capable, but it's not SQL-native in its ergonomics - you feel that difference in debugging, observability, and onboarding analysts.

Coming from a traditional warehousing background? Snowflake will feel familiar in the best way.

If you want AI augmentation without building a model.

Snowflake Intelligence is genuinely accessible. Sentiment analysis, document extraction, image and video analysis - all available as SQL functions. For teams that want to experiment with AI without a dedicated ML team, it lowers the bar significantly.

That said - call it what it is: an AI augmentation layer, not an ML platform. You don't get fine-grained control over model behaviour, costs can spike fast at higher volumes, and it won't take you far beyond lightweight use cases. If you need production-grade AI, you still need a data scientist and a proper ML workflow.

Snowflake also offers Cortex Code - an AI coding assistant for SQL and Python available in both the UI and CLI (we tested it previously). It's natively aware of your Snowflake environment: schemas, objects, and governance included. Worth knowing about if developer productivity tooling is on your radar.

If you need transactional-style workloads alongside analytics.

This one can be surprising. Snowflake's hybrid tables and interactive tables are genuinely usable in practice - I've used them in production scenarios. Snowflake is moving into HTAP (Hybrid Transactional/Analytical Processing) territory - blending transactional and analytical workloads in a single system - and it's a credible direction.

But it's not a clean win yet. These features are promising, not widely proven at scale. Databricks also has a story here - Delta Lake with streaming pipelines and derived serving layers covers a lot of the same ground, just framed differently. If your workloads skew toward streaming-derived serving rather than strict transactional semantics, Databricks is a reasonable option too.

When I'd choose Databricks

If you're building or fine-tuning ML models.

Databricks was purpose-built for this. The experimentation workflows, MLflow integration, model serving, feature engineering - it's all there and it's cohesive. If your team includes data scientists who want to experiment with different models, compare runs, and push things to production, Databricks is the right environment. Snowflake is catching up but it's not there yet.

If you want to build and ship data apps without leaving the platform.

Databricks Apps lets you build and deploy data and AI applications directly inside Databricks. Framework support is broader than people expect - Streamlit, Dash, Flask, FastAPI on the Python side, and Node.js frameworks including React, Angular, Svelte, and Express on the frontend. They run serverless, inherit Unity Catalog's access controls automatically, and Databricks Genie can help you scaffold them.

This is a strong emerging direction - but be clear-eyed about where it is today: Databricks Apps isn't yet mainstream or battle-tested, and the developer experience isn't yet comparable to dedicated app platforms. It's also worth noting that Snowflake's Streamlit integration is already widely used. The vision is compelling; just don't treat it as a mature platform yet.

If business users need self-service analytics inside the platform.

Snowflake is currently decommissioning their native dashboards, with Streamlit as the recommended path forward - genuinely capable, but requires Python. That raises the bar for teams expecting a no-code analytics experience. Databricks, by contrast, does have native drag-and-drop dashboards that are straightforward to create. They've also been investing heavily in Genie AI as a natural language interface for business users. On dashboarding specifically though, the gap is real - if keeping analytics inside the platform matters, Databricks has the clearer answer today.

That qualifier matters: most enterprises are already standardised on Power BI, Tableau, or Looker. If you're in that camp, native dashboard capability barely enters the equation.

Engineering considerations: both platforms

Deployment and DevOps

Whichever platform you're on, you need real deployment pipelines with version control, environment promotion, and change management. Clicking your way to prod isn't a workflow.

For Databricks, Declarative Automation Bundles (or DABs) paired with Terraform is a match made in heaven. Define everything as code, deploy to dev from your laptop, promote through environments via CI/CD. It's one of the most complete deployment stories I've encountered in practice.

Snowflake's story here is more complicated - and that's partly historical. The platform evolved feature by feature without a coherent deployment-first philosophy, and the fragmentation shows. Terraform works but historically lagged behind features (if you've ever written a snowflake_execute resource, you know the pain). Schemachange is a solid option and we've used it successfully on multiple projects.

Snowflake also launched Snowflake DevOps - a native declarative option with Git integration and CREATE OR ALTER syntax - and pushed it heavily for a while. We put it through its paces: production-ready, but missing a few things schemachange handles better, and Snowflake has since gone noticeably quiet on it. DCM projects (currently in preview) looks like the direction they're actually heading - declarative pipelines for Snowflake. Haven't tried it yet, so watch out for another analysis coming up soon. For now: Terraform + schemachange is the safe bet.

The multi-developer problem

How do multiple developers work on the same platform without overwriting each other? We get asked this constantly, and it applies to both platforms equally.

First rule: separate your infrastructure code from your application code. Infrastructure changes infrequently - your pipelines and transformations do. Don't mix them.

Beyond that:

• Databricks: DABs dev mode handles this natively. Username-prefixed resources per developer, no extra config needed.
• Snowflake: dbt's schema isolation is the cleanest solution. Without dbt, Snowflake multi-dev setups degrade fast - pointing Terraform or schemachange at a different target schema per developer is doable, but requires discipline the tooling won't enforce for you.

Neither is painless at scale, but Databricks has the more elegant built-in answer here.

Day-2 reality: debugging and observability

The operational differences matter most when things go wrong.

Snowflake's warehouse model makes failures relatively predictable - queries fail cleanly, errors surface clearly, and the execution model is familiar to anyone who's debugged SQL before. Databricks is more flexible, but that flexibility means more moving parts: distributed compute failures, notebook state, cluster configuration. Lineage and observability tooling has improved significantly, but you're still operating a distributed system with all the debugging complexity that implies.

Neither platform is hard to debug once you know it well. But Snowflake's learning curve is shallower for teams coming from a SQL background - and that has real cost when something breaks at 2am.

Do you even need to choose?

Here's something that doesn't get said enough: you might not have to pick one and stick to it.

Open table formats - Apache Iceberg in particular - change the equation. Your data can live in S3 (or ADLS, or GCS) in an open format, and both Databricks and Snowflake can read and write to the same tables. We've written about running Iceberg natively on AWS - the same data accessible to Athena, Databricks, and Snowflake without copying or converting anything.

There's also an underlying asymmetry worth naming here: Databricks is built on open standards - Delta Lake, Apache Spark, MLflow - while Snowflake's ecosystem is more proprietary. For senior engineers who care about avoiding lock-in, that's a real decision driver independent of the Iceberg question.

In theory, open formats mean you could use Snowflake for your SQL-heavy analytics workflows and Databricks for your ML workloads - both working off the same underlying data. No vendor lock-in, no duplication, no sync jobs.

In practice, it's messier than that. Catalog fragmentation is real - two platforms with overlapping governance creates coordination overhead. Write coordination across engines requires careful design, or you'll end up with conflicts or stale reads. Performance characteristics differ between engines on the same table. It's worth understanding before you commit long-term - but go in expecting real operational complexity, not a clean architecture slide.

The real question

We've seen plenty of clients agonise over this decision. Ultimately it comes down to a few things:

1. What does your team look like? SQL-heavy team? Snowflake. Python and ML people? Databricks. This is a good starting heuristic - but teams evolve. Optimising purely for today's skillset can box you in later. The deeper question is whether you want a platform-first experience (Snowflake) or an engineering-first platform (Databricks) - that shapes everything from hiring to debugging to long-term architectural decisions.

2. What are you trying to achieve? Pure analytics and BI? Snowflake. Advanced ML, real-time model serving, AI experimentation? Databricks. Somewhere in between? See point 1.

3. What's your appetite for operational overhead? Snowflake has a lower operational burden - it manages more for you, which is genuinely valuable. Databricks gives you more flexibility, but you own more of the responsibility. That trade-off is often the deciding factor in practice, especially for smaller teams.

4. What does cost predictability look like for you? Snowflake is generally easier to reason about costs upfront. Databricks can be cheaper at scale, but it's easier to misuse - compute costs in particular can surprise you if your workloads aren't tuned. Neither platform is cheap if you're not paying attention.

5. Who's already paying the bills? Let's be honest - the most common outcome is that a client is already on one platform and wants to get more out of it, not start from scratch. Both platforms are genuinely capable. If you're already on Snowflake and running well, you don't need Databricks. The inverse is equally true.

If you are genuinely starting from scratch with no constraints - we'd default to Databricks for data-product and ML-heavy workloads, and Snowflake for analytics-first organisations that want something approachable and low-maintenance.

Both are good platforms. The right choice depends on where you're coming from and where you want to go.

Let's chat!

Here at Mechanical Rock, we work with both Snowflake and Databricks on real client engagements. If you're trying to figure out which platform is right for you, or getting more out of the one you're on - let's have a chat.