At Mammoth, we’ve worked with many teams who are evaluating tools to manage and analyze their data.
A common question that comes up is whether to choose Databricks or Snowflake. Both are established platforms with strong capabilities, but they serve different purposes and are designed for different types of teams.
This article explains the practical differences between the two. Rather than listing features, we focus on how each tool fits into real workflows, how much support they require from your team, and what kind of return you can expect.
If neither option feels like the right fit, we will also introduce an alternative that helps teams move faster, with less setup and lower overhead.
What is Databricks?
Official Databricks documentation
Databricks is a platform built to help teams work with large and complex data. It was originally developed by the creators of Apache Spark, and it’s designed for advanced use cases like machine learning, real-time data processing, and building custom data pipelines.
Rather than using a traditional data warehouse model, Databricks combines data storage and processing into a single environment, often called a “lakehouse.” This makes it possible to work with both structured and unstructured data in one place.
Most teams that choose Databricks have strong engineering resources. The platform is flexible and powerful, but it often requires custom setup, coding, and ongoing development work to get the most from it. It’s commonly used by data scientists, machine learning engineers, and analytics teams working with very large datasets.
What is Snowflake?
Official Snowflake documentation
Snowflake is a cloud-based data warehouse designed to make it easy for teams to store, manage, and analyze large volumes of structured data. Unlike platforms that require managing your own infrastructure, Snowflake handles the underlying setup for you. This allows teams to focus on querying and reporting, rather than maintaining systems.
One of Snowflake’s key strengths is its simplicity. It separates storage and compute, which means you can scale each independently based on your needs. The platform is widely used by data analysts, business intelligence teams, and engineers who need a reliable and efficient way to work with data.
Because of its ease of use and broad integration with common tools, Snowflake is often chosen by organizations that want strong performance without having to build and manage a complex setup.
Learn more about how Mammoth integrates with BI tools
Databricks vs Snowflake: Key Differences
While both Databricks and Snowflake are built for working with data at scale, they approach the problem in very different ways. Knowing how they differ can help you choose the platform that best fits your team, use case, and goals.
Architecture
Databricks is built around the concept of a “lakehouse,” which combines a data lake and a data warehouse. This allows teams to store raw data in its original format and process it flexibly using code.
Snowflake is a more traditional data warehouse, focused on structured data and designed to run fast, reliable SQL queries. It separates storage and compute, which can make scaling and cost control easier for many teams.
Use Cases
Databricks is commonly used for advanced analytics, data science, and machine learning. It’s well suited for processing large, complex datasets and building custom data workflows.
Snowflake is more often used for business intelligence, reporting, and dashboarding. It works especially well when your data is already structured and your goal is to make it available across teams.
Performance
Both platforms perform well at scale, but they are optimized for different types of work. Databricks shines when you need to run complex computations or train machine learning models. Snowflake is faster and more efficient when it comes to running standard SQL queries and powering dashboards.
Pricing
Databricks uses a consumption-based model where you pay based on compute usage. The flexibility is useful, but it can be difficult to estimate costs up front, especially if you are building custom workflows.
Snowflake also uses a pay-per-use model, but it’s more predictable for teams running standard analytics. Pricing is often easier to forecast for teams with consistent usage patterns.
Learning Curve and Setup
Databricks is powerful, but it often requires a team with strong engineering skills to set up and manage. Teams typically write code in Python or Scala, and the learning curve can be steep for those unfamiliar with these tools.
Snowflake is easier to get started with. It has a clean interface, supports standard SQL, and integrates well with tools many teams already use. For organizations without a dedicated data engineering team, this ease of use can be a major advantage.
Mammoth in Action: Fast Wins from Global Teams
- Starbucks: From 20 days to 4 hours for cross-country sales reports
- Arla Foods: 1 billion rows/month handled with no-code setup, saving $50K+ annually
- Everest Detection: Cancer research teams gained full autonomy over clinical data, saving 30+ hours/month
- Bacardi: Unified on-trade and off-trade sales into a single report—no IT, just minutes
- Rethink First: Automated 6 reports with a 94% reduction in maintenance time
Databricks vs Snowflake: Comparison Table
Feature | Databricks | Snowflake | Mammoth |
|---|---|---|---|
Platform Type | Lakehouse | Cloud data warehouse | No-code data workspace |
Best For | Machine learning, custom pipelines | Business intelligence, dashboards | Data cleaning, automation, fast analysis |
Ease of Use | Requires coding and setup | SQL-focused, easier to start | Visual interface, no coding needed |
Setup Time | Weeks or more | Days to weeks | Same day |
Language Used | Python, Scala, SQL | SQL | No code |
Performance | High for custom compute tasks | High for structured queries | Fast for everyday workflows |
Pricing Model | Consumption-based, harder to predict | Consumption-based, more predictable | Fixed monthly pricing (starts at $119/mo) |
Integrations | Strong for custom pipelines | Broad support for analytics tools | Built-in connectors, ready to use |
Team Fit | Data scientists, engineers | Analysts, business users | Ops teams, marketers, non-technical users |
Time to Value | Weeks to months | Days to weeks | Same day setup, first insights in hours |
Limitations of Both Platforms
Databricks and Snowflake are both strong choices for working with data at scale, but they are not always the right fit. For many teams, the problem is not what these platforms can do, but what they require in order to be useful.
Complexity
Databricks often requires writing code, managing environments, and connecting multiple services to get everything running. Snowflake is easier to use, but still expects you to bring in additional tools for tasks like data cleaning, automation, or reporting. In both cases, teams end up building and maintaining a full data stack.
Cost
Both tools use a pay-as-you-go pricing model, which sounds flexible but can lead to unexpected bills. With Databricks, compute costs can grow quickly when jobs become more complex. With Snowflake, costs tend to rise as more users run queries or access large volumes of data.
Time to Value
Databricks and Snowflake are built for long-term flexibility, not for fast results. Most teams spend weeks or months before seeing real value. For smaller teams without dedicated engineering support, this can be a major obstacle.
Mammoth has enabled companies like Bacardi and Rethink First to automate monthly reporting processes that previously took 40+ hours—now completed in minutes, with zero IT involvement
💡 Want to see how fast Mammoth gets you results? Book a demo
When a Lightweight Alternative Makes More Sense
Databricks and Snowflake are both impressive platforms, but not every team needs that level of complexity. In many cases, the goal is not to build a large-scale data system. It is to answer questions, clean up messy data, or automate reports without spending months setting it all up.
We often hear from teams who started with one of these platforms but ran into the same problems. The setup took too long. The tools were overbuilt for their needs. The cost kept rising, even though they were only using a fraction of the features.
Here are some signs that a simpler platform might serve you better:
- You are not running machine learning models or processing massive volumes of data
- You want to clean, combine, and analyze data without writing code
- You need answers quickly, without hiring a team of engineers
- You are already paying for other tools and want something that connects easily
- You’re tired of Excel and Power Query limitations
- You’re merging 10+ data sources with no central dashboard
- You rely on agencies or vendors who send fragmented data
- You’ve outgrown manual reports but can’t justify a full data engineering hire
For teams like these, a more focused and accessible platform can get them to value faster, with far less effort.
Where Mammoth Fits In
Mammoth is designed for teams that want to work with data without the complexity of enterprise platforms. Instead of relying on code or custom pipelines, Mammoth provides a clean interface that lets you transform, clean, and automate your data with minimal setup.
You can connect your sources, apply changes, and build repeatable workflows without writing scripts or managing infrastructure. It works immediately and supports the kinds of tasks that come up every day. That includes fixing messy CRM records, preparing campaign reports, or enriching spreadsheets with extra details.
While Databricks and Snowflake focus on scale and flexibility, Mammoth focuses on speed, clarity, and ease of use. You do not need a team of engineers. You do not need weeks of setup. You just open the app, load your data, and get to work.
For teams that want results without the cost and complexity, Mammoth offers a more practical path forward.
Mammoth is already helping global teams, from Starbucks to Arla Foods, replace weeks of manual data prep with fully automated, real-time reporting. Starbucks cut reporting time from 20 days to 4 hours across 17 countries, while Arla saved 1200+ hours annually by processing over 1 billion rows monthly without engineering support
Final Recommendation
Databricks and Snowflake are both strong platforms, but they are built for different purposes.
If your team is focused on advanced analytics, machine learning, or large-scale custom data pipelines, Databricks is likely the better fit. If you are looking for a powerful data warehouse that integrates easily with business intelligence tools and supports structured data at scale, Snowflake may be the right choice.
But if your team does not need that level of complexity, or if you are looking for a faster way to clean, automate, and analyze your data, consider an alternative.
Mammoth gives you the ability to work with your data immediately, without coding, setup delays, or surprise costs. It is built for teams that want to move quickly, solve real problems, and spend more time getting answers and less time managing tools.
Try Mammoth free for 14 days. No credit card required. No calls to schedule. Just connect your data and see what you can do.
Frequently Asked Questions
What is the difference between Databricks and Snowflake?
Databricks is designed for big data processing and machine learning, while Snowflake is focused on structured data and business intelligence. Databricks is more flexible, but requires engineering resources. Snowflake is easier to use but better suited for traditional analytics.
Is Databricks better for machine learning?
Yes, Databricks is built with machine learning and advanced analytics in mind. It supports tools like Apache Spark and provides flexibility for custom workflows.
Can Snowflake be used without a data engineer?
Yes, Snowflake is known for being accessible to analysts and non-engineers. It uses standard SQL and has a clean interface that most teams can learn quickly.
What is a good alternative to Databricks and Snowflake?
If your team does not need complex infrastructure or custom pipelines, Mammoth can be a better fit. It allows you to clean, transform, and automate your data without code or long setup times.
