Microsoft Fabric vs Databricks vs (Snowflake)：compared their individual strengths and differences

This video explains the content of this slide.
https://youtu.be/8po-2dvJogU
（This video has been translated from Japanese audio using AI. Some parts of the audio may sound a bit strange, so please refer to the text on the slides when that happens.）

I joined the company about a year ago as a data engineer with no prior experience. As I gradually became accustomed to my work, I've summarized my real-life experiences using Fabric and Databricks (and occasionally Snowflake).

I'm somewhere between "business-oriented" and "engineer-oriented," and I feel I've come to understand both perspectives to a certain extent.
That's why I've come to see how the "strengths" of Fabric and Databricks can also be their "weaknesses."

Fabric's intuitive GUI is, on the flip side, a "black box."

Databricks's flexible codebase makes it "highly accessible."

We haven't talked much about Snowflake yet, but perhaps this review, from a beginner's perspective, may be helpful.

In this article, we provide a straightforward comparison of the three tools, looking at their features, philosophies, and ease of use in practice. While we're still learning, we hope this article will be helpful to others struggling with similar issues!

[Timetable]
Which Cloud Platform Should You Choose?
Warehouse vs. Lakehouse
Fabric vs. Databricks
Which is Beginner-Friendly?
Differences in Design Philosophy
Differences in Compute
Differences in Big Data Processing
Differences in Cost
Fabric Shortcut Mirroring
Summary
Next Episode Preview

👉 References
Comprehensive Data Operations with Microsoft Fabric and Databricks — Storing Data in Delta Lake Format on Hub Storage：
https://youtu.be/A9aIcowJn1I

The Latest Techniques for Data Interoperability between Databricks and Snowflake：
https://qiita.com/manabian/items/ce4ce26563d4648ca2e7

Interoperability between Delta Lake and Iceberg through Microsoft Fabric OneLake：
https://speakerdeck.com/ryomaru0825/microsoft-fabric-onelake-wotong-zita-delta-lake-and-iceberg-noxiang-hu-yun-yong-xing

Transcript

1. Microsoft Fabric vs Databricks vs (Snowflake) -A young engineer compares the strengths and differences of each.- Microsoft Data Analytics Day (Online) 2025.4.28 Reiji Otake
2. Introduction
3. Which cloud data platform should we select? There are too many options… Which one should I go for? Unify your teams and data to accelerate AI innovation with a complete data platform. Maximize your organization’s potential for data and AI with data intelligence Analytics. AI. Data engineering. Apps and collaboration. Power them all in the AI Data Cloud. Microsoft Fabric Databricks Snowflake
4. Warehouse vs Lakehouse ▽ Reference Databricks と同等の価格性能を持つという競合 Snowflake の主張に対する反論 | Databricks SQLDB、KQL、ウェアハウス、レイクハウスの違いと特徴-レイクハウススタンダード世代がどうしてSQLDBがまだ必要か考えた- #Databricks - Qiita Lakehouse Warehouse ・Structured data can now we handled faster and more cost-effectively in the LH ・We may soon see the Lakehouse leading the AI era ??
5. Fabric ＆ Databricks Strengths and weaknesses are two side of the same coin I’ve started to see things from both the business side and technical side. ・While Fabric’s GUI makes it easey to use, it also hides what’s really happening inside. ・While Databricks offers great flexbilility through code, it can feel intimidating for beginners ス テ ッ プ ア ッ プ （ 男 性 ・ ビ ジ ネ ス ） パ ソ コ ン を 打 つ ビ ジ ネ ス ウ ー マ ン
6. Microsoft Fabric vs Databricks
7. Fabric GUI interface is very friendly and intuitive for beginners In fact, I want to develop in a code-based way. Databricks is the best for me because it can do anything. Fabric is easy to use, but when erros happen, it’s hard to see what’s going on. and clicking around the GUI can be a bit troublesome. Using Databricks is challenging for me because it’s code-based. It’s a bit more difficult for me than Power Point and Excel, But it’s still easy to use. ---like an app. With Fabric, I feel I can do data analysis too!
8. Fabric has a lot of feature, while Databricks is simple -the difference in architectural philosophy- 図：Fabric has so many items. 図： Databricks UI is simple. There are so many items — which one should I use? What’s the difference between WH and LH? In the end, the simpler, Notebook-based design feels easier to use. I can use both GUI and Notebooks. There are so many items and lots of feature too.
9. What’s a cluster… And how should I set it up… Configure clusters with the right specifications for each workload, and tune the environment for large-scale processing or special needs. Which one is easier to use for computing? -Fabric has fewer options vs Databricks offers more flexible configrations- It’s nice to that I don’t have to do any initial setup, But sometimes I wish I could tweak the settings in more detail Image : There are no computing settings you just start a notebook. Image : Cluster Settings Page ※Note(Update) Now, Databricks also allows you to run computing in a serverless way Business users don’t need to think much about computing settings The limited options actually make it simpler and easier to use.
10. Databricks has the advantage when it comes to large-scale batch processing -Be aware of Fabric’s limitations- ▽references Introducing Autoscale Billing for Spark in Microsoft Fabric | Microsoft Fabric ブログ | Microsoft Fabric Image：Example of Fabric throttling Job：Copy activity for a terabyte-scale table using pipeline Fabric capacity：F64 The job succeeded, but throttling occurred, making the capacity unusable for a few days. You can configure an appropriate cluster size for each workload. Databricks is great when you want to run large-scale processing Throttling occurs when running large-scale process If that happens, you won’t be able to view reports in the same workspace because of throttled capacity
11. Cost Fabric：Subscription plan vs Databricks：Usage-based pricing 社 長 2 （ 男 性 ） Microsoft Fabric - 価格 | Microsoft Azure Databricksの料金プランとサービス概要 | Databricks image： Fabric pricing model image： Databricks pricing model Simple and clear — but a bit rough The pricing model is easy to understand Good fir budgeting with a fixed monthly cost But it can get a bit expensive when we use it extensively… Flexible but complex Pay for what you use If we set clear rules, it can be cost-effective But managing them can be troublesome…
12. Databricks has an external table feature, but if you have a lot of them, managing them can be troublesome. Fabric’s Shortcut and Mirroring are extremely useful 社 長 2 （ 男 性 ） Image：Shortcut Even if a large amount of external data, it’s convenient that you don’t need to copy or reload it Vendor-agnostic There is no duplicated storage cost We can consolidate all the data in Fabric In addition, it seems easy to use, so it might reduce development costs
13. Conclusion
14. Summary：”Easy of use” depends on the user’s perspective Microsoft Fabric vs Databricks vs (Snowflake)：若手エンジニアがそれぞれの強みと違いを比較してみた #fabric - Qiita Each tool has its own kind of “easy of use” but what that means can vary greatly depending on user’s perspective. →When comparing tools, focus not on which is “better” , but on which is “better for whom” That perspective allows for a more realistic judgment A clear UI and simple architecture make it a DWH anyone can use If you need is a powerful, easy-to-use DWH — go with Snowflake! Outstanding cost performance and usability in one An all-in-one platform open to business users. Even though it comes with some cost, its fixed pricing makes budgeting predictable if you want to truly democratize data analytics for the business side — go with Fabric! If you want to maximum flexibility and performance to unleash your engineer’s full potential — go with Databricks! For advanced analytics, it’s the top choice And with proper management, it can even be cost efficient! ー マ ン
15. Next up: Fabric and Databricks interoperability ▽参考 FabricとDatabricksの相互運用性①：hubストレージの目的 -Databricks で作成したテーブルをFabricで利用する、Fabricで作成した テーブルをDatabricksで利用する- #Azure - Qiita ▽参考 DatabricksとSnowflakeをつなぐ最新データ相互利用術 #Databricks - Qiita ▽参考 Microsoft Fabric OneLake を通じた Delta Lake & Iceberg の相互運用性 - Speaker Deck