If you think Snowflake is a Data Warehouse, you don’t get it.

Every few days, LinkedIn erupts with yet another Databricks vs. Snowflake debate.

🔹 Which one is better?

🔹 Which one is faster?

🔹 Which one will “win” the Data Platform war?

Honestly? It’s a pointless discussion.

Why? Because these comparisons are never fair or objective. They are always framed to favor one over the other. Instead of obsessing over which platform is “better,” a far more valuable discussion is:

✅ What are the strengths of the Data Platform you’re using?

✅ How do you maximize the value of your Data on that platform?

✅ What use cases align best with your Data Platform’s capabilities?

Focus on what matters: Maximizing your Data Platform

Instead of tearing down one Data Platform to promote the other, here’s a more productive approach:

1️⃣ Understand your needs

Are you building a Data Warehouse? Or are you working with large-scale AI/ML workflows? Probably a combination of both?

2️⃣ Leverage your Data Platform’s strengths

Explore the various features and functionalities of the platform of your choice and take advantage of these.

3️⃣ Get the most out of your choice

Whichever platform you choose or already have, learn it inside and out. Try to focus on performance tuning, cost optimization, and best practices for your environment.

Make your Data Platform work for you

Instead of arguing which is better, let’s shift the conversation: How do we make the most of the Data Platform we chose?

“If you think Snowflake ❄️ is a Data Warehouse, you don’t get it.”

So, let’s clear up this confusion.

🔹 What is a Data Warehouse?

🔹 Is Snowflake a Data Platform?

🔹 Why does this salesperson have it completely wrong?

What is a Data Warehouse? (and what it’s not)

A Data Warehouse (DWH) is not a product you buy, it’s an architectural concept. It is a structured, integrated system designed for analytical processing. A DWH enables organizations to consolidate historical and current data for Business Intelligence (BI), reporting, and decision-making purposes.

Key Characteristics of a Data Warehouse:

1️⃣ Subject-Oriented — Organized around key business domains (e.g., sales, finance, customer data) rather than operational processes.

2️⃣ Integrated — Data from various sources (databases, applications, external systems) is standardized and stored in a consistent format.

3️⃣ Time-Variant — Stores historical data to track changes and trends over time.

4️⃣ Non-Volatile — Once data is stored, it is not modified or deleted, ensuring stable and reliable reporting.

A Data Warehouse typically includes:

✅ ETL/ EL-T (Extract, Transform, Load) — Process for extracting data from sources, transforming it into a usable format, and loading it into the warehouse.

✅ Data Storage — A structured repository optimized for querying (e.g., relational databases, cloud-based solutions).

✅ Metadata — Data about the data, including definitions, structures, and lineage.

✅ Query & Analysis Tools — Used for generating reports, dashboards, and Analytics (e.g., SQL or BI tools like Tableau, Power BI).

A Data Warehouse is something you build, not something you buy. You don’t just log into an account and get a fully functional Data Warehouse. It requires architecture, modeling, and Data Governance.

If you buy Snowflake, Databricks, Microsoft Fabric, BigQuery, or any other tool, you are buying the platform on which you can build a Data Warehouse.

Is Snowflake a Data Platform? Absolutely.

If Snowflake is “just a data warehouse”, then explain this:

A Data Platform is a comprehensive ecosystem that provides the infrastructure, tools, and services for managing an organization’s entire data lifecycle. This spans from data ingestion, storage and processing to analysis, and consumption. It is designed to handle various data types (structured, semi-structured, unstructured) and supports multiple use cases, including Analytics, AI/ ML, real-time processing, and Data Governance.

A Data Warehouse, on the other hand, is a specific type of data storage solution optimized for analytical querying and reporting. It typically stores structured data in a highly organized manner and is used for Business Intelligence (BI) and decision-making.

How They Work Together?

A modern Data Platform often integrates a Data Warehouse as part of its architecture. For example, Snowflake can function both as a Data Warehouse (for structured analytics) and as part of a larger Data Platform that includes (external) semi-/ un-structured Data, AI/ ML workloads, Data Governance, and Real-time Analytics.

So, what did this salesperson get wrong?

A Data Warehouse is an architectural concept. Snowflake is a platform where you can build a Data Warehouse, but out of the box, it’s an empty platform. It waits for you to define its architecture.

This Databricks salesperson wants to reduce Snowflake to a Data Warehouse, but let’s be clear:

✅ A Data Warehouse is an architectural design, not a product.

✅ Snowflake is a cloud-based Data Platform, not just a Data Warehouse.

✅ With Snowflake, you get an empty but powerful Data Platform, to build anything from a Data Warehouse to an AI-powered Data Application.

Are these debates between Databricks & Snowflake and statements from this Databricks person helpful or just noise? Let’s discuss.

Snowflake Data Superhero and Chapter Lead for the Dutch Snowflake ❄️ User Group. Consulting Partner at Bravinci. Online also known as; DaAnalytics