𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 𝗼𝗿 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲? 𝗔 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿’𝘀 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗚𝘂𝗶𝗱𝗲

Since Microsoft Fabric entered our lives, the rules of the data game have changed. Your data now lives as a Single Copy in OneLake, stored in open Delta Parquet format.

But here’s the truth:

👉 The storage layer is unified.

👉 The compute engine is the real strategic choice.

As a Data Engineer, how do you choose the right architecture? Let’s break it down.

🏛️ 𝟭. 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲: 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗧-𝗦𝗤𝗟 𝗮𝗻𝗱 𝗦𝘁𝗿𝗶𝗰𝘁 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲

If your project demands high discipline, transactional integrity, and a fully structured environment — this is your domain.

𝙒𝙝𝙮 𝘾𝙝𝙤𝙤𝙨𝙚 𝙄𝙩?

Full DML support directly via SQL: 𝗜𝗡𝗦𝗘𝗥𝗧, 𝗨𝗣𝗗𝗔𝗧𝗘, 𝗗𝗘𝗟𝗘𝗧𝗘, 𝗠𝗘𝗥𝗚𝗘. You can build controlled, deterministic data pipelines entirely in T-SQL.

🔐 𝙏𝙝𝙚 𝙎𝙚𝙘𝙧𝙚𝙩 𝙒𝙚𝙖𝙥𝙤𝙣: 𝙈𝙪𝙡𝙩𝙞-𝙩𝙖𝙗𝙡𝙚 𝙏𝙧𝙖𝙣𝙨𝙖𝙘𝙩𝙞𝙤𝙣𝙨 Execute complex business logic via:

Stored Procedures

Explicit Transactions (BEGIN TRAN, COMMIT)

Enterprise-grade schema enforcement

Perfect for finance, ERP, and systems that demand strict consistency.

🌊 𝟮. 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲: 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗦𝗽𝗮𝗿𝗸 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 If you’re dealing with massive datasets, semi-structured data (JSON, Logs), or ML-heavy workloads — the Lakehouse shines.

𝙒𝙝𝙮 𝘾𝙝𝙤𝙤𝙨𝙚 𝙄𝙩?

Process unstructured/semi-structured data easily.

Use Spark + Python for scalable engineering.

Leverage distributed compute for heavy transformations.

⚠️ 𝗧𝗵𝗲 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗗𝗶𝘀𝘁𝗶𝗻𝗰𝘁𝗶𝗼𝗻

You can query Lakehouse tables using the SQL Analytics Endpoint, but it is Read-Only. Writes and transformations happen through:

Spark Notebooks

Spark Job Definitions

Dataflows Gen2

SQL here is strictly for analytics and verification, not for data manipulation pipelines.

⚡ 𝗧𝗵𝗲 𝗦𝗵𝗮𝗿𝗲𝗱 𝗣𝗼𝘄𝗲𝗿: Direct Lake Mode Both Warehouse and Lakehouse support Direct Lake. Power BI reads directly from OneLake Delta files—no import, no refresh cycles, near real-time performance.

🚀 𝗧𝗵𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗠𝗮𝘁𝗿𝗶𝘅

Make your decision based on three pillars:

1️⃣ Team Skillset

T-SQL heavy team ➔ #Warehouse

Spark / Python engineers ➔ #Lakehouse

2️⃣ Data Manipulation Strategy

SQL-based Stored Procs & DML pipelines ➔ 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲

Spark-first ETL / ELT & Notebooks ➔ 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲

3️⃣ Transaction Requirements

Complex multi-table ACID logic (SQL-style) ➔ 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲

Table-level Delta ACID (Spark-style) is sufficient ➔ 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲

🔄 The Most Important Insight In Microsoft Fabric, this is not a binary decision. Thanks to Shortcuts and Cross-Database Querying: