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Platform guide
Databricks vs Snowflake: key differences and when to use each
Both platforms now do warehousing, data engineering and AI, so the feature lists look alike. The real differences are in how each one is run, billed and used day to day. This guide compares them plainly, including when using both makes sense.
Quick answer
What is the difference between Databricks and Snowflake?
Snowflake is a fully managed, SQL-first cloud data platform built for analytics, BI and data sharing, billed in credits for warehouse running time. Databricks is a lakehouse platform built on Apache Spark and Delta Lake, stronger for data engineering, data science and machine learning, billed in DBUs plus cloud compute. Many companies use Snowflake for analytics and Databricks for ML, connected through Apache Iceberg tables.
Disclosure: NTech is a Snowflake partner. We also work alongside Databricks in client estates, and we tell clients when Databricks or a mix of both is the better fit.
Side by side
Databricks vs Snowflake at a glance
| Snowflake | Databricks | |
|---|---|---|
| Started as | Cloud data warehouse, SQL first | Managed Apache Spark, code and notebooks first |
| Architecture | Fully managed service; storage separate from compute (virtual warehouses) | Lakehouse on your cloud storage; clusters or serverless compute |
| Main users | Analysts, analytics engineers, BI teams | Data engineers, data scientists, ML engineers |
| Languages | SQL first; Python, Java and Scala through Snowpark | Python, SQL, Scala and R in notebooks and jobs |
| Storage format | Native managed tables, plus Apache Iceberg tables | Delta Lake, readable as Iceberg through UniForm |
| Governance | Snowflake Horizon: roles, masking and row access policies, lineage | Unity Catalog: permissions, lineage and discovery across data and AI assets |
| AI and ML | Cortex AI functions, Snowpark ML, notebooks | MLflow, Mosaic AI, model serving; the deeper ML toolset |
| BI concurrency | Multi-cluster warehouses scale for many dashboard users | Databricks SQL serverless warehouses |
| Admin effort | Low; few settings to manage | Higher; more choices on clusters, runtimes and policies |
| Billing unit | Credits per second of warehouse time, plus storage | DBUs by compute type, plus cloud VM cost on classic compute |
Both vendors ship features quickly. Check current documentation for anything your decision depends on.
Choose Snowflake when
- Most of the work is SQL, reporting and dashboards
- You want a platform a small team can run without tuning clusters
- Many business users query the same data at the same time
- You are moving off Teradata, Oracle, Netezza or SQL Server warehouses
- You share data with partners or customers through secure data sharing
Choose Databricks when
- Data science and machine learning are the main workloads
- You process large volumes of semi-structured or streaming data in Spark
- Your engineers prefer notebooks and Python over SQL
- You want data kept in open formats in your own cloud storage
- You already run Spark jobs and want them managed
Cost
How the pricing models differ
Snowflake bills credits for the time a virtual warehouse runs, per second after a 60-second minimum, and each size step doubles the credit rate. Storage is billed separately. Cost is easy to predict once warehouses are sized well and set to auto-suspend, and easy to waste when they are oversized.
Databricks bills Databricks Units (DBUs) at different rates for jobs, all-purpose, SQL and serverless compute. On classic compute you also pay your cloud provider for the virtual machines. Well-tuned batch jobs can be cheap; idle all-purpose clusters are a common source of waste.
Neither is cheaper in general. Cost depends on workload shape and on how well the platform is run. See Snowflake cost optimization.
Using both
Many companies run Databricks for engineering and ML and Snowflake for analytics and BI. Open table formats make this easier than it used to be: Snowflake can read and write Apache Iceberg tables, and Delta Lake tables can be exposed as Iceberg. The cost is two platforms to govern, secure and pay for, so use both only when each carries real workloads.
Before you decide
- List your top 10 workloads by cost and by users
- Note who will build on it: analysts or engineers
- Run a proof of concept on 2 or 3 real workloads, not a demo
- Compare a month of projected cost on both
Contact us
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FAQ
Questions buyers ask us
Is Databricks better than Snowflake?
Neither is better for every case. Snowflake is usually simpler for SQL analytics and BI with many users. Databricks is usually stronger for data science, machine learning and large Spark processing.
Is Snowflake cheaper than Databricks?
It depends on the workload. Snowflake bills credits for warehouse running time; Databricks bills DBUs plus, on classic compute, the cloud VMs. Comparing a month of your real workloads on both is the reliable way to know.
Can you use Databricks and Snowflake together?
Yes. A common pattern uses Databricks for data engineering and ML and Snowflake for analytics and BI, connected through shared storage and Apache Iceberg tables.
Does Snowflake support Apache Iceberg?
Yes. Snowflake supports Apache Iceberg tables stored in your own cloud storage, which other engines, including Spark, can also read.
Which is easier to learn, Databricks or Snowflake?
Snowflake is easier for teams that know SQL. Databricks suits teams comfortable with Python, Spark and notebooks, though Databricks SQL has narrowed the gap.
How long does a migration between them take?
It depends on the number of pipelines and how much engine-specific code they contain. An assessment of objects and jobs gives a reliable estimate before you commit.
Next step
Tell us what you need to build or who you need to hire.
A 30-minute call with a senior architect. You leave with a scoped plan or a role profile, whether or not you work with us.