Databricks vs Fabric vs Snowflake : The Battle for the Cloud Data Crown

As per my experience, I believe Databricks generally takes the crown in most cases. With this blog, my goal is to provide an unbiased comparison of Databricks, Fabric, and Snowflake to help businesses and vendors make informed decisions about their data stack. Drawing from both my experience and research, I aim to offer valuable insights into when and why each platform excels in different scenarios. While I have a personal preference for Databricks, this blog is designed to present a balanced view, highlighting the strengths and weaknesses of each platform. It's not an exhaustive analysis, but rather a guide to help you consider your data stack choices from a technical perspective.

User-Friendly Pricing Model

Snowflake: Snowflake has a fixed pricing model, charging a set number of credits per hour for on-demand computing. It is very to the point.
Fabric: Fabric also has simple fixed pricing, which is a big plus. However, features like bursting and smoothing can make it hard to fully understand the actual costs and what you’ll need.
Databricks: Databricks charges with DBUs (Databricks Units) or DBUs with virtual machine compute per hour. While this is mostly simple, the different prices for DBUs based on compute type can be confusing. I see why it’s set up this way, but it’s not very user-friendly. For newcomers to Databricks, this can feel overwhelming and may turn them off from using the platform.
Think of renting a car:
  • Option 1 is a basic car with a fixed hourly rate, regardless of its power.
  • Option 2 offers customizable models, each with different rates based on features.

Databricks is like Option 2. You can choose from various compute resources, each with its own cost per DBU. For instance, a Standard DBU is good for general tasks, while a High Memory DBU suits memory-intensive jobs, and a High Core DBU is best for processing-heavy tasks. This flexibility helps you optimize costs but can also make pricing more complex.

Compute Resource Pricing                                                                                                                                         

Databricks: Features a highly flexible pricing structure, allowing you to select from a wide range of cost options, from very low to very high. No matter what you choose, the compute performance is consistently strong.
Snowflake: Offers a pricing model similar to Databricks but with fewer budget-friendly options available. However, it still delivers solid performance.
Fabric: Charges you for compute resources at all times, even if you’re not actively using them. Although you can implement an auto-suspend feature, it's not its main design focus, and aspects like bursting and smoothing can complicate your overall costs.

Analytical Workload Performance

Snowflake: Provides excellent SQL performance and consistently excels in benchmarks. In my recent tests with analytical queries (1B rows), it outperformed Databricks, making it quite fast overall!
Databricks: Also offers strong SQL performance. I use it daily, but in my comparisons, Snowflake won the race.
Fabric: Delivers decent performance, but many users report issues with larger datasets, especially at the terabyte scale. Unlike Databricks and Snowflake, there are few benchmarks comparing Fabric’s performance, and the lack of strong marketing claims suggests it may not compete as effectively.

ETL Workload Performance

Databricks: Clearly the top choice in this area. As data sizes grow, it consistently outperforms its competitors. While I take benchmarks with caution, both my tests and general opinions agree on this.
Snowflake: Provides solid performance, but in my tests against Databricks, it fell behind in nearly every case except one.
Fabric: See the notes on Analytical Workload Performance. Many Fabric users strongly recommend against using it for ETL tasks, except for loading data into the gold or analytical layer. Discussions online often advise against moving workloads from Databricks to Fabric if you're looking for reliable and fast performance.

Ready-to-Use Package

Databricks Data Platform that offers:
  • Orchestration & ETL/ELT: Robust capabilities for data management.
  • Governance: Managed through Unity Catalog.
  • AI/ML Integration: Tools for advanced analytics and coding assistance.
  • Business Features: Basic dashboarding and AI/BI Genie for insights.

Considerations:

  • Some ingestion connectors are still in development.
  • Compatibility Issues: DLTs (Delta Live Tables) have had difficulties working seamlessly with the Unity Catalog in the past.

Overall, Databricks offers exceptional quality and value, with the majority of its tools rated 'B' or above, highlighting their strong performance and reliability across key features.

Snowflake Data Platform that offers:

  • Powerful Engine: Snowflake’s standout feature is its robust engine.
  • Dependency on Tools: Relies on external tools like Airflow, dbt, and Fivetran for core data engineering needs.
  • AI Capabilities: Has made investments in in-house AI, with potential for growth in this area.
  • Strategic Acquisitions: May pursue acquisitions to enhance out-of-the-box capabilities compared to Databricks.

Considerations:

  • The basic version of Databricks often provides a superior experience compared to vanilla Snowflake.

Overall, while Snowflake has strong fundamentals, its reliance on additional tools may impact its standalone effectiveness.

Fabric Data Platform that offers:

  • Mature Orchestrator: Data Factory is a well-developed orchestrator that comes with built-in ingestion connectors, unlike Databricks and Snowflake.
  • Power BI Integration: It features Power BI, which is one of the strongest BI tools on the market.
  • Strong Analytics: If we were looking only at analytics, Fabric would surpass Snowflake.

Considerations:

  • The tools aren’t as robust as those in Databricks (except for Power BI).
  • Fabric lacks some specialized business features that Snowflake does well and that Databricks handles sufficiently.
  • The experience is similar to what Synapse offered, with a user interface that resembles Power BI.

Overall, Fabric is a solid option, especially for orchestration and analytics, but it may not have the specialized features found in its competitors.

Integrated Ecosystem for Partners

Snowflake has adopted the partner ecosystem more than any other data platform provider. Although this can make it less ready to use right away, many established third-party tools often prioritize their relationship with Snowflake, adding new features for it before they do for other vendors. Examples include Fivetran, Dbt(Data Build Tool), Looker, and Tableau.

Databricks has made significant progress in getting third-party vendors to make their tools compatible with the platform. Many established tools already support Databricks, while some are cautious. Others are careful not to upset Snowflake, and many new developers are opting to create tools that fully integrate with Databricks, viewing the platform's growth as a valuable market opportunity. Examples include Power BI, Matplotlib, Fivetran, and Dbt (Data Build Tool).

Microsoft Fabric is a relatively new platform, it is still growing its ecosystem. While it integrates with other Microsoft tools, its third-party tool support might be less extensive compared to Databricks and Snowflake.

User-Friendliness

Snowflake is known for being easy to use for several reasons:

User-Friendly Interface: Its simple design makes it easy to navigate for everyone, regardless of experience level.
SQL Focus: Snowflake uses SQL, a language many users are already familiar with, making data tasks straightforward.
Clear Pricing: The pricing is to the point and clear, helping users understand how their compute choices affect costs.
Great Documentation: Snowflake provides thorough documentation that is easy to follow, helping users make the most of the platform.
Integration with Popular Tools: Snowflake works well with tools like dbt, Airflow, and Fivetran. These connections improve the user experience, even though those tools can be used with other platforms.

Fabric is an excellent choice for small businesses, particularly those without heavily technical teams and focused primarily on dashboards. Here are some key points:

Quick Dashboard Creation: Fabric allows users to easily build data pipelines and develop dashboards quickly, making it a user-friendly choice.
Better than Excel: While it may not produce the most advanced or resilient systems, Fabric offers a significant improvement over creating dashboards in Excel.
User-Friendly Tools: The built-in pipeline features and UI-based tools are inspired by Power BI's Power Query, making them easier to use compared to what Databricks and Snowflake currently provide.
Less Adaptable: Although it is easier to use, Fabric may not be as adaptable or robust as other platforms.
Caution with Power BI: While Power BI integrates well, it can be complex to master. Users can inadvertently create large models that become costly over time, and skilled Power BI developers can be expensive to hire.

Databricks is known for its flexibility and focus on engineering and AI, but it can be challenging to learn. Here are the key points:

Challenging Reputation: Its complexity and limited documentation have made it seem difficult for users.
Improved SQL Features: The introduction of Databricks SQL and the SQL Editor has made it easier to use.
Growth Potential: If documentation improves, text-to-SQL continues to develop, and more practical resources are available, Databricks could become a top choice.
Serverless Capabilities: The move to serverless technology will make things simpler by removing the need to manage clusters, which has been a major pain point.

Key Analysis: Business Impact

Databricks

Robust Capabilities: Databricks excels in data engineering and AI, effectively competing with Snowflake in analytics.
Serverless Computing: The Serverless option simplifies resource management, enhancing user accessibility.
Innovative Features: Tools like AI/BI Dashboards, AI/BI Genie, and Lakeflows position Databricks for enhanced user experiences.
Path to Leadership: These advancements create a strong opportunity for Databricks to become the leading data platform.
Implementation Challenge: The main challenge is proving ease of use compared to Fabric while maintaining flexibility for advanced users.

Snowflake

Top-notch Analytics: Snowflake boasts a best-in-class SQL engine, making it excellent for analytics.
Transparent Pricing: Its simple pricing model is easy for users to understand.
Strong Ecosystem: A robust partner network supports a wide range of tools and integrations.
Dependency on Third Parties: Heavy reliance on external vendors can limit its overall value, especially as competitors like Databricks offer similar tools.
AI Catch-up: Snowflake is striving to enhance its AI capabilities to compete with Databricks, making it an interesting area to watch.

Fabric

Value Assessment: The fabric’s overall value is difficult to gauge due to mixed perceptions.
Power BI Integration: Power BI is a strong BI tool, easy to start with but challenging to master, raising concerns about long-term sustainability.
Simplicity vs. Depth: While Fabric is perceived as simple, this can lead to poor practices that complicate solution maintenance, resulting in higher costs for capacity and consulting.
Past Concerns: Similar optimistic projections about improvements in platforms like Synapse raise skepticism about Fabric's future.
Competition Boost: Despite its challenges, the presence of Fabric as a third major player adds valuable competition, and there's hope it will elevate the standards in the market.
Fabric offers potential but currently lacks the robustness needed to compete effectively as a standalone platform.

Scenarios Where Each Platform Shines

   Category

                   Databricks

                   Snowflake

                   Fabric

   Ideal Teams

- Experienced SQL developers
- Mid-level and above Python developers
- Scala developers of any level

- Experienced SQL developers
- People new to SQL but willing to learn

- Experienced Power BI developers with limited or no SQL experience

   Ideal Use Cases

- Processing and storing data for BI tools, ad-hoc reporting, and web applications
- Machine learning and AI applications
- Handling structured, semi-structured, and unstructured data

- Storing and processing data for BI tools, ad-hoc reporting, and web applications
- Selling or sharing data and applications through Snowflake's Data Marketplace

- Internal reporting
- Customer-facing reporting
- Reporting-centric applications

   Scalability

- Suitable for any scale
- Especially good for growing data workloads

- Can handle over 10B rows but may not be cost-effective for very large datasets. Best for smaller to mid-scale data workloads.

- Works well with datasets from thousands to 1B rows. May not be cost-effective at larger scales.

   Why Choose It?

- Versatile Platform: Excels in AI/ML and data engineering
- Strong in analytics with SQL engine, AI/BI Genie, and built-in Dashboards
- Handles small and large workloads with any data type

- SQL Focused: Snowflake does SQL very well. Offers a simple UI, good cost management, and a strong partner ecosystem.
- Great for SQL teams, especially beginners, thanks to its easy-to-use interface.

- Power BI Integration: Built around Power BI, making it an ideal choice for Power BI developers. Simplifies the reporting process and leverages Power BI’s powerful features for internal and customer-facing reports.

   Considerations

1. Flexibility vs. Complexity: More flexible but can be harder to learn, and the documentation may not always be clear.
2. Cost Controls: Historically weaker than Snowflake but improving.
3. Performance: Snowflake outperforms Databricks in analytical queries at a low billion-row scale, though Databricks' results are still acceptable.

1. Basic Development Features: Often requires other tools for advanced features, increasing costs.
2. AI/ML Capabilities: Snowflake isn’t an industry leader in AI/ML, though it supports Python, Scala, and Java.
3. ETL Performance: Databricks outperforms Snowflake in ETL tasks at scale, but Snowflake is still suitable for most use cases.

1. Power BI’s Learning Curve: Power BI is easy to start with but hard to master. Inefficient data models can hurt performance and increase costs.
2. Limited Flexibility: Not ideal for use cases beyond reporting.
3. Architecture Challenges: Power BI developers without architecture experience may create complicated setups that are hard to maintain.

Happy Exploring! Happy Learning!      

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