Advocate IMicrosoft Fabric vs Databricks vs (Snowflake)：compared their individual strengths and differences
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!
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.
👉 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