Snowflake vs. Databricks

A guide to choosing your data platform

When it comes to choosing a data platform, we come across two big names time and time again: Snowflake and Databricks. Which provider should data-driven companies choose?

In today's data landscape, decision makers are constantly faced with the challenge of choosing from a variety of available technologies to make the best choice for your team. Not only is the amount of data tools overwhelming, but also the task of choosing exactly the product whose features best suit the company's use cases.

Which data platform really helps a company move forward depends on its specific requirements. That is why we make it our task, together with decision makers and their data team, to analyze the specific interests and goals of the individual stakeholders in order to bring them together with the best product. We then analyse the question from the following points of view: Snowflake or Databricks? In this article, we show which platform is the best choice under which conditions — depending on roadmap, team skills, implementation time, and costs.

Cloud Data Warehouse vs. Data Lakehouse: The Evolution of Snowflake and Databricks

The Snowflake vs. Databricks question would have been even easier to answer a few years ago. Snowflake began as a native cloud data warehouse, which scored points primarily due to its decoupled storage and compute performance. The focus was still heavily on SQL queries, their optimization and the quick answer of analytical questions. Databricks was developed for machine leaning and data science based on Apache Spark and follows the Data Lakehouse approach.

But both companies have developed their data platforms immensely and want to present a holistic solution to their customers. Databricks is now widely used as a data warehousing platform, also supports SQL queries and continues to develop for classic BI. Snowflake has made major investments and developments towards AI/LLM and today calls itself the AI Data Cloud. Both competitors are offering ever more similar features and are fighting tough battles in marketing.

Team skills and engineering: Who works with the platform?

When we, as a taod, work together with customers Data warehouse We usually do not design and set up the engineering and BI role alone. We usually work with the company's data team, which has worked with a legacy data warehouse so far, to work out all the advantages and disadvantages of the targeted data platforms. Our goal is to recommend the best possible product with which all employees can then continue working effectively.

With our projects, we are creating something that can be used productively for many years and can be further developed by the customer team.

SQL focus or Python expertise? The influence of team origin

That is precisely why the capabilities of the data team must also be considered when choosing the platform. The “origin” of the engineers who are to work with the program is decisive: Was their focus on development with SQL or Python?

Databricks is based on Apache Spark and can therefore be used most effectively with PySpark. Snowflake, with roots as a data warehouse, is well suited for people with an SQL background. Training will be quick here, especially if team members come from SQL professionals from classic on-premise environments. When it comes to a greenfield approach, however, it should be borne in mind that SQL is easy to learn, Snowflake also optimizes poorly written SQL queries, and teams with different backgrounds can work well together there.

DevOps effort and governance: zero maintenance vs. full control

The general affinity of the data team for DevOps role tasks can also be decisive. Snowflake is easy to use and manage, but offers less granularity. This means that many processes such as scaling, performance optimization and clustering are automatically controlled, which reduces operational effort — but also allows less control over individual parameters. In contrast, Databricks requires more team attention to calibrate individual processes, but offers more flexibility for complex workloads. Here, specific compute resources, storage strategies, and optimization mechanisms can be fine-tuned, which can be particularly important for data-intensive and performance-relevant applications.

A team with a strong SQL background can use Snowflake productively in just a few days, while Databricks requires an initial setup for clusters and permissions that ties up DevOps resources. Even during ongoing operations, support costs remain variable: Snowflake pursues a “zero-maintenance approach,” in which scaling, performance tuning, and infrastructure management are largely automated. This reduces administrative overhead and makes the platform low-maintenance in the long term. Databricks, on the other hand, while offering more control and customization options, also requires continuous optimization of clusters, storage strategies, and permissions, which can require more DevOps capacity in the long term. It can therefore be helpful to use the data team's existing capabilities and thus strengthen it. This relieves the team of tasks that they already have.

Implementation and usability: use “out of the box”

When it comes to setup, Snowflake and Databricks are contrasted with a completely different tool: dbt. In our opinion, every project that is built in Databricks or Snowflake — although essential in different ways — benefits from dbt as a transformation tool.

Databricks requires the support of experienced DevOps staff at the outset in order to be used effectively. However, once the setup is complete, the platform makes it much easier to organize, version and link work steps. A key advantage over Snowflake is the ability to create and execute jobs flexibly — an important aspect for data teams that don't want to use additional tools.

It is often described as a “lakehouse platform” — i.e. a combination of data lake and data warehouse.

However, Databricks lacks built-in test functionality, and dependencies between work packages are not automatically recognized. The success of a project depends heavily on data engineers knowing all dependencies, being familiar with the relevant data points and ensuring high data quality even before it is read in. Without considerable manual effort, neither data quality tests nor job sequences tailored to dependencies can be efficiently configured. Especially in the early stages, this can mean that a project is in its infancy longer than necessary.

The Snowflake UI is clearer than Databricks and allows faster familiarization. Snowflake can also provide better compute performance in certain scenarios and integrate data from external sources more efficiently.

A decisive factor for a successful start with a tool is well-structured and understandable documentation. According to many users, Snowflake documentation is particularly clear and easy to access. In order to acquire knowledge, data engineers often draw on communities and forums, exchange ideas and continue their education. Both Snowflake and Databricks have extensive resources, active discussion platforms, and official documentation that provide comprehensive support to developers and architects.

Strategic roadmap: BI focus or advanced analytics & AI?

In order to take into account not only current use cases, but also future requirements — such as setting up a data warehouse that both platforms support — the entire company roadmap should be included in the decision. What tasks should the platform take on in the long term? And which questions should be answered with the available data?

If the focus is primarily on business intelligence (BI) and analytical workloads, Snowflake is the better choice. However, even though the use of data for Artificial intelligence (AI) And machine learning (ML) plays a role, it's worth comparing the two platforms again. A data scientist can use familiar tools on both systems, but Databricks is more sophisticated in this area and has more experience working with ML and AI workloads.

Snowflake, however, scores points with a stronger partner ecosystem. It makes it easier to integrate external tools and use third-party solutions to further process data in a variety of ways. Snowflake also supports teams and companies that have little experience with data science by making complex issues more accessible.

Cost structures: Comparing transparency and efficiency

Both platforms follow a pay-as-you-go model that primarily relates to compute costs. There are also storage costs: With Databricks, these are caused by external storage at the selected cloud provider, while Snowflake manages the storage internally and bills it directly.

Snowflake can be and remain cheaper due to low start-up costs and easy-to-use cost optimization tools such as Auto-Suspend for compute clusters. Databricks, on the other hand, is often more efficient with large amounts of data, particularly with ML workflows, and can therefore be cheaper under certain conditions.

However, which tool is actually cheaper depends heavily on the individual use case. Snowflake offers a more transparent pricing model, as it automatically provides cost optimization and monitoring tools — an area where Databricks is still lagging behind. By combining different features, varying pricing models, and cloud-dependent billing, Databricks can make it more difficult to overview and optimize actual costs. However, with experienced Databricks professionals in the team, the cost level can be specifically controlled — for example by using Spot instances or dynamically adjusting the cluster size.

As mentioned earlier, both Snowflake and Databricks may require you to use third-party tools for BI, data transformation, or data governance — which comes at an additional cost. Snowflake scores points here with a strong partner ecosystem, but often requires the use of such tools. For data engineers, for example, using a tool like dbt is almost essential when choosing Snowflake.

Both Snowflake and Databricks are more expensive than traditional databases if you're just looking for a simple database solution. However, if a holistic data platform is to be set up, the decision against a modern tool can become more expensive in the long term — not only in terms of direct costs, but also in terms of missed opportunities and insights. In our opinion, choosing one of these tools is an investment in the future of a data-driven company — one that can pay off quickly in the long term.

Conclusion: Which platform is right for your company?

Snowflake or Databricks? The answer to this question is highly individual and depends on how your company answers and prioritizes the above questions. One important aspect should be mentioned at the end: Both platforms can be made suitable for most use cases. However, the skills of your data team should play a decisive role in the election, otherwise time and money must be invested in continuing education. It is also important: A combination of both tools always remains an option.

This article was first published in our magazine data! Issue 5. Read now for free.

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