Snowflake vs Databricks: how Australian data teams should actually choose
Published 23 June 2026
Somewhere right now an Australian data team is running a bake-off between Snowflake and Databricks, and the deciding factor will be which vendor's sales engineer gave the better demo. There is a better way to make this decision, and it starts with an uncomfortable truth: for a meaningful share of workloads, both platforms will do the job well. The differences that matter live at the edges of your workload, your team's skills and your cost model.
What each platform is actually for
Snowflake grew up as a cloud data warehouse and it still shows in the best way. SQL-first analytics, straightforward administration, clean separation of storage and compute, and a governance model that business-facing teams can operate without a platform engineering group behind them. If your world is dashboards, reporting, ELT pipelines and analysts who live in SQL, Snowflake's centre of gravity matches yours.
Databricks grew up as a data engineering and machine learning platform built around Spark, and that heritage shows equally. Notebooks, Python and Scala workloads, streaming, and the tooling for training and deploying models. If your world includes data science teams shipping ML into production, large-scale transformation jobs or streaming ingestion, Databricks' centre of gravity matches that.
Both have spent years expanding into each other's territory, and the marketing now overlaps almost completely. The centres of gravity have moved less than the slideware suggests.
The questions that actually decide it
Start with your team. A SQL-strong analytics team will be productive on Snowflake in weeks; the same team handed Databricks often spends a quarter learning the platform instead of delivering. A Python-native engineering team frequently finds Snowflake constraining in the other direction. The platform that fits the team you actually have beats the platform that fits the team you plan to hire.
Then your workloads, honestly categorised. Count what you run today and what is genuinely funded for next year, not the aspirational roadmap. Paying for ML platform capability because machine learning is on a strategy slide is one of the most common ways Australian data budgets leak.
Then the cost model. Snowflake's consumption pricing is easy to start with and easy to lose control of without warehouse governance; ungoverned environments routinely surprise their owners at quarter end. Databricks costs are more configurable and more complex, which rewards teams with the engineering maturity to tune them. Either platform can be economical or expensive. The variable is governance, not the logo.
The answer nobody wants
Plenty of larger Australian organisations end up with both, deliberately: Snowflake serving analytics and reporting, Databricks running engineering and ML workloads, with well-defined data flows between them. That is not indecision. It is putting each workload where it runs best, and it is often cheaper than forcing everything onto one platform and paying the mismatch tax.
Waverton designs, builds, migrates and manages both platforms for Australian and New Zealand businesses, so we have no stake in which logo wins your bake-off. Request a quote and we will assess the decision against your actual workloads, including telling you if the platform you already have is the right one.
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