When does data lakehouse architecture call for Databricks vs Snowflake?

Most data organizations don’t need to pick one or the other. They need to know which workloads belong where. The data lakehouse architecture Databricks vs Snowflake decision comes down to one question: are you running machine learning pipelines, or answering business questions at scale?

Databricks is built for ML/AI engineering and streaming. Snowflake is built for SQL analytics, high-concurrency BI, and governed data sharing. As of June 2025, 52% of Snowflake customers also run Databricks, according to theCUBE Research. Hybrid isn’t a compromise. It’s the default pattern.

What is a data lakehouse?

A data lakehouse combines ACID transactions and schema enforcement from traditional data warehouses with the open, low-cost object storage of data lakes.

The architecture runs on top of cloud object storage — Amazon S3, Azure Data Lake Storage, or Google Cloud Storage — with an open table format layer (Delta Lake, Apache Iceberg, or Apache Hudi) providing transaction guarantees, versioning, and query performance. The result: one storage layer that serves both data engineers running Spark pipelines and analysts running SQL queries. No redundant data copies between a warehouse and a lake. The concept was formalized in the 2020 VLDB paper “Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores.”

What is Databricks built for?

Databricks is a Spark-native platform built for ML engineering, data transformation at scale, and streaming pipelines using Delta Lake, MLflow, and Unity Catalog.

At its core, Databricks runs Apache Spark with multi-language support — Python, Scala, R, and SQL. Unity Catalog provides fine-grained access control, column-level lineage, and a single metadata layer across Delta Lake, Apache Iceberg, Apache Hudi, and Parquet. MLflow 3.0 (GA 2025) handles experiment tracking, model observability, and evaluation for both ML models and GenAI agents. Mosaic AI includes a Vector Search engine supporting over 1 billion vectors. Lakebase (GA February 2026) adds a serverless PostgreSQL OLTP database for AI applications. Forrester named Databricks a Leader in The Forrester Wave: Data Lakehouses, Q2 2024, with top scores across 19 criteria.

What is Snowflake built for?

Snowflake is a SQL-first data platform built for high-concurrency analytics, governed data sharing, and BI workloads using a fully managed, compute-storage separated architecture.

Snowflake holds approximately 35% of the cloud data warehouse market, with $3.63B in product revenue in FY2024. Its virtual warehouse model scales compute independently of storage. Snowpark adds Python, Java, and Scala execution for non-SQL workloads. Cortex AI brings LLM-powered SQL functions. Cortex AISQL (public preview) supports multimodal processing — documents, images, and unstructured data — via standard SQL syntax. Snowflake Marketplace connects over 3,000 live data sets. Native Apache Iceberg table support reached GA in April 2025, and Snowflake Open Catalog (formerly Apache Polaris) makes its Iceberg implementation interoperable across engines.

Databricks vs Snowflake: how do they compare?

Databricks and Snowflake overlap on storage format support and AI tooling, but differ sharply on native query engine, streaming capabilities, and governance maturity.

Both platforms run on AWS, Azure, and GCP. Enterprise contract pricing differs significantly from list rates. Snowflake’s compliance-focused controls are more battle-tested in regulated industries. Unity Catalog has improved rapidly but may warrant closer review for highly regulated environments.

How do Delta Lake, Apache Iceberg, and Apache Hudi compare?

Delta Lake offers the deepest Spark integration, Apache Iceberg has the broadest multi-engine and multi-cloud support, and Apache Hudi excels at record-level upserts and CDC workloads.

Delta Lake’s UniForm compatibility layer lets Iceberg-native readers consume Delta tables without conversion. Apache XTable enables interoperability across all three formats, reducing forced lock-in. For new architectures without an existing Databricks-heavy footprint, Apache Iceberg is the emerging industry default. It’s the format Snowflake went native on, and it has the widest support across engines including Apache Flink, Apache Spark, Trino, and Dremio. The table format you choose affects which engines can read your data without a copy.

For teams building real-time event pipelines, see: Real-Time Data Streaming for Operational AI Use Cases

When should you use Databricks, Snowflake, or both?

Choose Databricks when ML training, feature engineering, or high-volume streaming pipelines are the primary workload. Choose Snowflake when the priority is governed SQL analytics, cross-organization data sharing, or high-concurrency BI with strict compliance requirements. Run both when your organization has distinct ML engineering and BI analytics teams with different tooling needs.

The common hybrid pattern: Databricks handles ingestion, transformation, and ML; Snowflake handles governed BI and data sharing. Open formats — particularly Apache Iceberg — make cross-platform reads practical without copying data. Gartner’s 2025 document “Databricks and Snowflake Convergence” notes that both vendors are closing the gap on each other’s core strengths, so this decision increasingly comes down to team skills and existing toolchain fit, not capability gaps.

For governance and lineage requirements across either platform, see: Data Governance for AI Training Sets: Lineage, Access, and Compliance

And for keeping data clean before it reaches your models: Data Quality Pipelines: Preventing Bad Data from Reaching AI Models

What to do next

If you’re evaluating Databricks, Snowflake, or a hybrid architecture for an enterprise AI data platform, map your current workloads to a platform pattern before committing. The right choice depends on your primary workload type, team skills, and how open format support fits your existing toolchain.

Read next: Building a Modern Data Platform for Enterprise AI

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