Why Databricks May Not be the Right Fit for Many Research & Trading Teams

Databricks is a powerful data engineering and analytics platform, well suited for large-scale data processing and centralized lakehouse architectures. However, for many research-driven teams, portfolio managers, and quant groups, Databricks introduces friction that slows experimentation, increases cost, and creates operational dependency.

Datatailr was built to address a different problem: how to let small, high-impact teams build, test, and deploy research, backtesting, and AI workloads quickly—without becoming data engineers or platform operators.

This post outlines why Databricks is often not enough and how Datatailr could be an accelerator for trading teams.

The Core Mismatch

The fundamental difference lies in the fact that Databricks is a data engineering platform, whereas Datatailr is a research and production compute platform. This distinction drives nearly every difference outlined below.

Here are the key limitations of Databricks for research and trading workflows, and the alternative Datatailr offers:

1. Databricks Is Data Engineering-Focused

Databricks is optimized for ETL pipelines, data lakehouse management, and centralized analytics platforms. For PMs, quants, and researchers, this means utilizing tooling and abstractions optimized for engineers rather than end users, leaving research workflows feeling "forced" into an engineering-first model.

Datatailr is designed for: Research, backtesting, experimentation, and iteration by non-engineers.

2. Mandatory Data Migration into a Lakehouse

Databricks assumes that data should be ingested, transformed, and stored in a Databricks-managed lakehouse. This introduces significant upfront migration effort, architectural lock-in, and ongoing data duplication and governance overhead.

Datatailr: Runs compute on top of existing data without migration, meaning no lakehouse is required and there is no forced data movement. You just connect to 40+ data sources including Databricks, Snowflake, mySQL, etc and query your data in a single SQL syntax using our Data Engine. 

3. Continued Dependency on Engineers & DevOps

Even after setup, Databricks typically requires engineers to manage clusters and optimize jobs, as well as DevOps to control environments, permissions, and costs. This creates a bottleneck for PMs, researchers, and small teams without dedicated infrastructure support.

Datatailr: Is designed to be self-serve, automated and usable without infrastructure expertise.

4. Burst Compute & Elastic Scaling

Built on Apache Spark and the JVM, Databricks is not designed for true burst compute. Scaling up to tens of thousands of containers in minutes — and scaling back down just as fast — is difficult and inefficient. This limits its suitability for modern, highly elastic AI workloads that require rapid, on-demand compute.

Designed for burst compute from the ground up. Datatailr can rapidly scale to 50k+ containers in minutes and scale back down seamlessly, enabling highly elastic AI and trading workloads. This capability has been running in production for an energy trading fund two years, supporting real-world, large-scale use cases.

5. No Native Dev / Pre-Prod / Prod Workflow

Databricks does not come with clear research-to-production workflows out of the box, nor does it have opinionated promotion paths for non-engineers. Teams are expected to design and maintain these workflows themselves.

Datatailr: Provides Dev, Test, and Production flows out of the box with clear separation between research and deployment, allowing for easier governance without custom frameworks.

6. Research & Backtesting Are Not PM-Friendly

For many PMs and quants, backtesting in Databricks is cumbersome, iteration cycles are slow, and onboarding new users takes time.

Datatailr: Is optimized for fast research iteration, backtesting workflows, and rapid onboarding of PMs and analysts. This is one of the primary reasons clients choose Datatailr.

7. Cost Is High and Unpredictable

Databricks pricing is based on usage-based DBUs (often a 2X markup on underlying cloud costs like EC2) and is difficult to predict or control. Teams frequently experience cost overruns and limited cost visibility per user or job.

Datatailr: Uses predictable licensing, includes built-in cost observability, and supports automated cost controls and safeguards.

8. IDEs and Environments Are Not Turnkey

In Databricks, IDEs require setup, Python packages must be managed manually, and environment consistency is a recurring issue.

Datatailr: Comes with pre-configured IDEs and notebooks, common packages installed by default, and consistent environments across users.

Summary Comparison

Conclusion

Databricks is a strong platform for organizations building centralized data infrastructure.

However, for teams whose priority is speed of research, ease of backtesting, rapid onboarding, and controlled production deployment, it introduces unnecessary complexity.

Datatailr was built specifically for these teams—providing a lighter, faster, and more controlled path from idea to production, which works smoothly alongside Databricks if that’s what teams chose to opt for.