Databricks vs Snowflake Compared

The architectural difference is where the data and the engine live. In Databricks, tables are open-source Delta Lake: Parquet data files with a file-based transaction log, stored in object storage, and Delta Lake is the default format for every table. Compute is separate. For classic compute, Databricks documents that the resources run in your own cloud account; serverless compute runs in a plane that Databricks manages in the same region.

Snowflake describes a different split. It has three layers: database storage, compute in the form of virtual warehouses (each its own compute cluster, so one warehouse does not slow another), and a cloud services layer that handles authentication, metadata and access control. Native tables are stored in compressed, columnar micro-partitions that Snowflake manages for you. You do not choose a file format or run a cluster, which is the product's selling point.

Both vendors now meet in the middle. Snowflake supports Iceberg tables over cloud storage you manage, and Databricks offers serverless SQL warehouses and recommends them where available. The remaining question is how much of the stack you want to see and control.

This section is AceMQ's reading, not a vendor claim. Databricks fits when the same data has to serve pipelines, streaming, feature engineering and model training as well as dashboards. Delta Lake is built to work with Apache Spark and Structured Streaming, so batch and streaming code can share one copy of a table. Teams that write Python or Scala for transformation, or that need notebooks, jobs and ML tooling beside the SQL, are working with the grain of the platform.

Snowflake fits when the centre of the workload is SQL analytics for a broad group of business users and BI tools, and the data team would rather tune queries than manage clusters. Snowpark lets engineers process data inside Snowflake in Python, Java or Scala without moving it out, which covers more transformation work than it did a few years ago.

Neither platform is the wrong answer for a mixed workload. The cost of choosing badly shows up as a second platform bolted on later, so list the real workloads first: how much is ELT in SQL, how much is streaming, how much is model training, who consumes the output.

Both vendors publish list prices, which vary by cloud, region, edition and contract, so this page compares how each one bills rather than quoting numbers that will age.

Databricks bills for Databricks Units, a normalised unit of processing power, with the number consumed driven by the compute used and the data processed. Billing is per second, and commitment contracts lower the rate. On classic compute the instances run in your account, so the cloud bill and the Databricks bill both move with the workload; Databricks notes that storage, networking and related costs depend on the services you choose and your cloud provider. The practical consequence is that cost follows cluster configuration: idle all-purpose clusters, oversized job clusters and unmanaged autoscaling are where spend leaks, which is the work of a Databricks cost review.

Snowflake bills credits for compute, a flat rate per terabyte for storage, and a fee for moving data out to another region or cloud. Warehouse compute is billed per second with a 60-second minimum each time a warehouse starts, so a warehouse that resumes constantly for tiny queries costs more than its running time suggests. Serverless features and the cloud services layer consume credits as well, with cloud services charged only when daily use exceeds 10 percent of daily warehouse usage. Cost follows warehouse size, how long warehouses stay running and how many clusters a multi-cluster warehouse scales out to.

Ask each vendor the same three questions: what is the billing unit, what happens to cost when the workload doubles, and what do I pay when nothing is running.

Both ship a native catalog. Unity Catalog is Databricks' governance layer for data and AI assets. It enforces access control when a table is queried or a model is called, tracks lineage, logs activity for audit, and classifies sensitive data; it is enabled by default for workspaces created after 8 November 2023 and also exists as an open-source project. Horizon Catalog is Snowflake's equivalent, covering sensitive-data protection, data quality, lineage and AI governance for data inside and outside Snowflake.

The real governance risk is the half-adopted catalog. A Databricks estate where Unity Catalog covers some workspaces and legacy Hive metastore permissions cover others has two permission systems that agree in neither. The same is true of a Snowflake account with some data in native tables and some in externally catalogued Iceberg tables. Whichever platform you choose, decide where the single source of truth for permissions lives before the second team onboards.

Format lock-in is smaller than it used to be. Databricks can generate Iceberg metadata for Delta tables, so a single copy of the data files serves Delta and Iceberg clients, and Snowflake can read Delta files in object storage through Iceberg tables. Data in open formats on storage you control can be read by the other platform without a bulk export.

The lock-in that remains is in the code and the operating model. SQL dialect differences, stored procedures, Snowpark code, Spark jobs, notebooks, job schedules, permission models and the BI layer all take time to move, and none of that travels with the data files. When you evaluate lock-in, count the lines of platform-specific code and the number of downstream consumers, not just the table format.

Moving a Snowflake estate to Databricks is mostly a SQL, consumer and operations exercise, because the data is the easy part. A typical sequence:

1. Inventory. Tables, views, tasks, stored procedures, shares and every BI connection and downstream job that reads them.
2. Connect without moving. Databricks Lakehouse Federation exposes Snowflake as a read-only foreign catalog in Unity Catalog, so consumers can start querying from Databricks while data is copied.
3. Copy and convert. Land tables as Delta in Unity Catalog and convert Snowflake SQL to Databricks SQL, then test each converted script. Snowflake-only features, such as stored procedure logic, need redesign rather than translation.
4. Run in parallel. Reconcile outputs row by row against the Snowflake source before any consumer switches.
5. Cut over in waves. Re-point BI and jobs workload by workload, and keep Snowflake read-only until the last consumer has moved.

The effort scales with the number of consumers and the amount of custom SQL, not with data volume, so a written assessment before a quote is the sensible first step. AceMQ's Databricks migration services start there, and the practice is led by a former Databricks professional-services resident solution architect.

The reverse move has the same shape and a different bottleneck. Snowflake can read Delta table files that sit in your object storage through Iceberg tables, and Databricks can expose Iceberg metadata for Delta tables, so the data can be shared while consumers move. The work is in the compute code: Spark jobs, notebooks, Lakeflow pipelines and ML workflows have no direct equivalent in a SQL warehouse and have to be rewritten for Snowpark or kept running somewhere else. If most of your Databricks usage is SQL reporting that happens to run on Spark, the move is cheaper than it looks. If it is engineering and ML, expect most of the effort there.

AceMQ has a Databricks practice. If the answer for your estate turns out to be Snowflake, our Snowflake consulting and support page covers that platform separately.

A common end state is both platforms with a clear boundary: engineering and ML on Databricks, governed SQL analytics on Snowflake, with open table formats in between. It works when someone owns the boundary. It fails when the same table is transformed on both sides, when two governance models disagree about who may see a column, or when each team's bill is attributed differently so nobody can compare them. If you are heading that way, write down which platform is the system of record for each dataset before building anything.

1. What does the team write today? Mostly SQL points to Snowflake. Spark, Python and Scala, or a plan for ML in production, points to Databricks.
2. How much is streaming and ML? If it is a large share, the lakehouse keeps it on one copy of the data.
3. Which bill can you forecast? Warehouse hours and credits, or cluster configuration and DBUs. Model your actual workloads against both billing models.
4. How open must the storage be? If other engines must read the data in place, favour open table formats whichever platform you pick.
5. What would a migration cost later? Count consumers and platform-specific code, not terabytes.

Databricks: pricing, architecture overview, Delta Lake, Iceberg reads for Delta, Unity Catalog, cost management and query and catalog federation. Snowflake: key concepts, cost, Iceberg tables, Horizon Catalog and Delta Lake support. Checked on 15 October 2026. Features, editions and billing terms change; confirm against current documentation and your own contract.