Best Snowflake Alternatives in 2026: Data Warehouses, Lakehouses and Databricks Alternatives

Which Snowflake alternatives fit depends on whether you want a managed warehouse, an open-source engine you run yourself, or something embedded. Google BigQuery and Amazon Redshift are the hosted warehouses closest to Snowflake, ClickHouse is a widely used open-source analytical database, and DuckDB covers smaller data with no server at all. Teams replacing Databricks should look at Apache Spark, Trino and Starburst for the lakehouse side.

Snowflake and Databricks are both closed-source, hosted data platforms, so this guide covers alternatives to each. The twelve picks are grouped into hosted warehouses, open-source analytics databases, lakehouse engines and embedded analytics. The table shows live licence, hosting and GitHub data.

Why people look for Snowflake and Databricks alternatives

Snowflake is a cloud data platform for data warehousing, sharing, engineering and analytics, and Databricks is a lakehouse platform for data engineering, SQL analytics, governance and machine learning. Teams compare them with other tools for these reasons:

• Consumption pricing is hard to forecast. Snowflake's pricing page describes a consumption-based model with credits, plus a monthly storage fee calculated on average stored data after compression. Databricks bills by Databricks Units with per-second granularity, and it also sells committed-use contracts. In both cases the bill follows how much compute you run.
• Both are hosted only. Enlisted lists neither as self-hostable, so data and processing live on the vendor's platform, running on AWS, Azure or Google Cloud.
• They are built for large enterprises. Both are aimed at organizations consolidating data and AI workloads. A small team with modest data may find a full platform heavier than it needs.
• Proprietary platforms can mean lock-in. Open engines and table formats such as Apache Spark, Trino and Delta Lake let you keep data in open files and change the query layer later.
• You may need a different workload. Real-time dashboards for customers, embedded analytics and local analysis on files each suit engines tuned for them.

How we picked

A tool made this list if it can run analytical SQL over large datasets, which is the core job of both Snowflake and Databricks. Open-source projects needed a clear licence and recent GitHub activity, and Enlisted notes how each can be deployed. Hosted products are included for their pricing model and the clouds they run on. Each entry reads from catalogue data and vendor documentation, and no benchmark was run for this guide. See how Enlisted ranks tools.

Warehouse, lakehouse or embedded: which do you need?

These words describe three different shapes of tool, and mixing them up is the most common way to pick wrong:

• Warehouse: a managed or self-run SQL engine that stores data in its own optimized format and serves reports and dashboards. BigQuery, Redshift, ClickHouse, Apache Doris, StarRocks and Firebolt fit here.
• Lakehouse: warehouse-style SQL, transactions and governance over open files in object storage. Databricks describes its approach as combining a data lake's flexibility with a warehouse's structure. Apache Spark, Trino, Starburst and the Delta Lake table format belong to this side.
• Embedded analytics: an engine that runs inside your application, notebook or command line instead of as a separate server. DuckDB is the example, and MotherDuck is its managed cloud form.

If your BI tool and analysts need a shared database, start with a warehouse. If you need to process raw files and train models, start with a lakehouse engine. If one person is analyzing files on a laptop, an embedded engine may be all you need.

How do the pricing models differ?

The alternatives meter usage differently, so compare the model rather than a headline price:

• Snowflake charges for compute in credits and bills stored data monthly. Databricks meters compute in Databricks Units per second and also offers committed-use discounts.
• Google BigQuery is billed on usage, with separate charges for storage and query processing.
• Amazon Redshift offers a serverless option billed by compute used, or provisioned clusters billed by node.
• Firebolt and ClickHouse Cloud are usage-based managed services, and Firebolt bills compute by engine size and time.
• MotherDuck has a free plan and a business plan with usage on top.
• Self-hosted open-source engines have no licence fee, but you pay for servers, storage and the people who run them.

Estimate your own mix of data size, query volume and concurrency before comparing vendors, because each model favors a different pattern.

1. Google BigQuery: best serverless warehouse on Google Cloud

Google BigQuery is a fully managed serverless data warehouse that lets analysts run SQL over very large datasets without provisioning servers or planning capacity. Google's documentation describes separate storage and compute layers that scale independently, the same separation of storage and compute that Snowflake lists, delivered inside Google Cloud.

Data can be loaded in batches or streamed, queried alongside external sources and connected to dashboards and notebooks. BigQuery ML trains and runs models with SQL statements, including regression, classification, clustering and time series forecasting. It is billed on usage, with storage and query processing charged separately, and Enlisted lists a free tier.

• Best for: teams already on Google Cloud that want a warehouse with no infrastructure to manage.
• Watch out for: it is closed source and runs only on Google Cloud, so it ties your analytics to that provider.

2. Amazon Redshift: best managed warehouse for AWS-centered teams

Amazon Redshift is a managed cloud data warehouse from AWS that stores data in columnar format and spreads queries across nodes. It can run as provisioned clusters or as a serverless option.

It connects to Amazon S3 for data lake storage and works with BI tools through JDBC and ODBC drivers. It suits reporting, dashboards and ad hoc analysis on large datasets, and it is pay-as-you-go with no free plan listed in Enlisted's data.

• Best for: organizations whose data and applications already live on AWS.
• Watch out for: it is AWS only and closed source, and you must decide between serverless and provisioned capacity.

3. Firebolt: best for low-latency analytics behind applications

Firebolt is an analytical database for engineers who need fast SQL on large datasets, real-time analytics and efficient ELT. The vendor describes it as a Postgres SQL dialect database built for object storage, with ACID transactions and snapshot isolation.

It runs as a managed service on AWS and Google Cloud, with Azure in preview, and the vendor's site also describes a self-hosted engine called Firebolt Core in preview, which runs as a single binary and scales out to many nodes. Enlisted lists Firebolt as closed source while the vendor describes the engine as open source, so confirm the licence terms before you adopt the self-hosted route. Managed compute is billed by engine size and time, and new accounts receive free credits.

• Best for: data teams building customer-facing analytics or data applications that need low-latency queries.
• Watch out for: the self-hosted engine is a preview, and licence terms differ between Enlisted's catalogue and the vendor's description.

4. ClickHouse: best open-source columnar database for real-time analytics

ClickHouse is an open-source column-oriented database management system built to produce analytical reports in real time. Snowflake is a hosted platform for general warehousing, while ClickHouse is an engine you can run yourself, tuned for fast scans and aggregations over very large tables of events, logs, metrics and product analytics.

It is written in C++, queried with SQL and licensed under Apache-2.0. You can install it on Linux, macOS or FreeBSD, or use ClickHouse Cloud, a managed service from its creators with usage-based plans and a trial. Releases arrive monthly.

• Best for: product, event and observability analytics where query latency matters.
• Watch out for: self-hosting a distributed cluster takes real operations work, and it is tuned for analytical reads rather than transactional workloads.

5. Apache Doris: best open-source MPP database for customer-facing analytics

Apache Doris is an open-source massively parallel analytics database that offers fast SQL, acceleration of queries over lakehouse data and hybrid search across structured, text and vector data. It is an Apache Software Foundation project licensed under Apache-2.0.

Its README lists customer-facing analytics, data warehousing, observability on logs and events analyzed with SQL, and AI search among its uses. It supports Iceberg, Hudi and Delta Lake formats, and Enlisted lists no hosted version in its data, so you run it yourself.

• Best for: teams that want a self-hosted warehouse with real-time ingestion and a lakehouse query layer.
• Watch out for: there is no vendor-hosted option in the catalogue, so plan for cluster operations.

6. StarRocks: best for fast joins and ad hoc queries over a lakehouse

StarRocks is a Linux Foundation analytical SQL engine for real-time and ad hoc queries that runs on its own storage or directly over data in a lake or lakehouse. It uses a vectorized engine and a cost-based optimizer, supports ANSI SQL and the MySQL protocol, and is licensed under Apache-2.0.

Primary-key tables support upserts and deletes while queries run, and materialized views refresh during data import and are chosen automatically at query time. It can query Hive, Iceberg, Delta Lake and Hudi tables directly.

• Best for: teams that need low-latency dashboards and want existing MySQL-compatible clients to connect.
• Watch out for: Enlisted lists no hosted version, so you operate the cluster.

7. Databend: best Rust-based warehouse on object storage

Databend is an open-source cloud data warehouse written in Rust that unifies analytics, vector search and full-text search on object storage, with compute that scales elastically. It stores data on S3, Azure Blob Storage or Google Cloud Storage and now positions itself for AI agents, with sandboxed Python user-defined functions.

Databend Cloud offers a free start, and the repository lists the licence as Other, so read its licence files before deployment.

• Best for: teams that want a warehouse on object storage with vector and full-text search in the same engine.
• Watch out for: the licence is listed as Other, and it is a younger ecosystem than ClickHouse or Spark.

8. Apache Spark: best open-source engine for Databricks-style workloads

Apache Spark is an open-source analytics engine for batch, SQL, machine learning, graph and streaming workloads at scale. Databricks is built around Spark, so running Spark yourself or through another provider is the most direct way to replace its engine without a Databricks contract.

It offers APIs in Scala, Java and Python, and includes Spark SQL, MLlib, GraphX, Structured Streaming and a pandas-style API. It is maintained by the Apache Software Foundation under Apache-2.0.

• Best for: data engineering and machine learning teams that want to run large ETL and streaming jobs on an open engine.
• Watch out for: it is an engine, not a managed platform, so notebooks, governance and cluster management come from other tools or a vendor.

9. Trino: best for SQL across many data sources and a data lake

Trino is a distributed SQL query engine, formerly PrestoSQL, that lets analysts run interactive SQL against data where it lives instead of moving it into one warehouse first. It is licensed under Apache-2.0 and runs as a cluster of servers with a web UI.

It has connectors for Hive, Hadoop, Iceberg and Delta Lake, a JDBC driver and a plugin architecture for custom connectors. It is a query engine, so you still need storage and a table format underneath.

• Best for: data platform teams querying a lake or several systems with one SQL layer.
• Watch out for: it does not store data itself, so pair it with object storage and a table format such as Iceberg or Delta Lake.

10. Starburst: best hosted lakehouse platform built on Trino and Iceberg

Starburst is a data analytics platform built on the open-source Trino engine and Apache Iceberg, with the pitch that you query data where it already lives rather than building copy pipelines. It adds ingestion into Iceberg from Kafka or batch files such as CSV, JSON and Avro, and queries that can span systems in a single SQL statement.

An AI layer lets analysts and agents ask questions of governed data, and the platform runs in the cloud, on premises or in a hybrid setup. It has a free tier and paid tiers billed per compute credit, with a trial.

• Best for: organizations that want Trino with vendor support, governance and managed operations.
• Watch out for: it is proprietary, so you trade the pure open-source route for a vendor platform.

11. DuckDB: best embedded engine for analysis without a server

DuckDB is an in-process analytical SQL database that runs inside your application, notebook or command line rather than as a separate server. It is MIT licensed, written in C++ and designed for speed and ease of use on analytical queries.

CSV and Parquet files can be queried by naming them in a FROM clause, and it handles window functions, nested subqueries and complex types such as arrays, structs and maps. Clients exist for Python, R, Java and WebAssembly, with integration into pandas and dplyr.

• Best for: analysts and data scientists working with files on one machine, and apps that embed analytics.
• Watch out for: it runs inside one process, so by itself it is not a shared, multi-user warehouse.

12. MotherDuck: best managed DuckDB for small teams

MotherDuck is a managed cloud service built around DuckDB that adds shared cloud storage and collaboration. Its hybrid execution lets a query use both the local machine and the cloud, so small files on a laptop and large shared tables can be combined in one SQL statement.

It targets analysts, data engineers and application developers who find a full warehouse too heavy for their data. There is a free plan for small use and a business plan with usage on top, and it is hosted only.

• Best for: small teams that want a lightweight shared analytics warehouse built on DuckDB.
• Watch out for: it is a closed, hosted product, positioned for datasets where a full warehouse feels too heavy rather than for the largest enterprise estates.

Other options worth a look

• Lakehouse formats and engines: Delta Lake is an open storage layer that adds ACID transactions to data lakes and works with Spark, Flink and Trino, and Dremio OSS runs SQL analytics directly on lake and warehouse data. Apache Hive is older SQL-on-Hadoop warehouse software.
• Real-time and operational analytics: SingleStore combines transactional and analytical queries in one distributed database, and Materialize keeps SQL views over live data up to date.
• Greenplum-style MPP: Apache Cloudberry is an open-source MPP database built on a PostgreSQL kernel.
• Other platforms: Microsoft Fabric and Azure Synapse Analytics are Microsoft's analytics services, and Teradata is an enterprise warehouse with no published prices.
• Pipelines around the warehouse: Airflow, dbt and Airbyte are common open-source tools for orchestration, transformation and data movement.

Which Snowflake alternative should you choose?

More Snowflake alternatives and Databricks alternatives are listed on Enlisted, the BigQuery alternatives page covers the other hosted option, and the data engineering category has the rest.

Moving off Snowflake or Databricks: what to check first

Start with where the data lives. If your data is already in open files such as Parquet on object storage, engines like Trino, Spark and DuckDB can read it without a copy, and the same is true of lakehouse tables that follow Iceberg or Delta Lake. If it sits in Snowflake's internal storage, plan an export and decide on the table format before you pick the new engine.

Then check the SQL itself. Dialects differ in functions, semi-structured data handling and stored logic, so list your most important queries and views and test them against a shortlist. Confirm that your BI tools can connect, for example by reading the Tableau and Power BI alternatives guide for the dashboard side, and decide who will own upgrades, security and cost monitoring. Self-hosting an open-source engine removes the vendor meter but moves the operations work onto your team, which only pays off if you have people ready to take it on.