Teams that want to keep their data in open table formats and only swap the compute should shortlist the open lakehouse and query engines. Teams happy to move into one managed platform should look at the big data and AI platforms. If you mainly need very fast analytics queries, an open source OLAP database is the lighter option. Ten options follow in no particular order.
The lakehouse market has been reshuffling quietly. Names that used to stand alone now sit inside larger vendors, and nearly every big platform has renamed itself around AI.
The ten tools below are listed alphabetically, and every description comes from each vendor's own website, read as of Oct 2026. If you're mainly moving data in and out, my guide to ETL tools covers pipelines.
Databricks alternatives side by side
I checked the free plan column on each vendor's own website, as of Oct 2026. Yes means the page names a free plan, a free version or an open source edition. Trial only means it names a time-limited free trial. Not stated means the page I read named neither, so check the vendor's own site before you sign up.
| Tool | Type | Good for | Free plan |
|---|---|---|---|
| ClickHouse | Open source OLAP database | Real-time analytics on very large tables | Yes |
| Cloudera | Hybrid data and AI platform | Data and AI across cloud and on-premises | Not stated |
| Dremio | Agentic lakehouse platform | Querying Apache Iceberg data in place | Yes |
| e6data | Lakehouse compute engine | SQL, streaming and AI on open tables | Not stated |
| Google BigQuery | AI data platform and warehouse | Analytics and AI across the whole data life cycle | Not stated |
| Microsoft Fabric | Unified data analytics platform | Data engineering, warehousing and Power BI together | Trial only |
| Onehouse | Open data infrastructure | A managed lakehouse that runs in your own account | Not stated |
| Snowflake | AI Data Cloud | Managed analytics, data engineering and AI | Not stated |
| Starburst | Trino-based lakehouse platform | Querying data across many sources without moving it | Trial only |
| Teradata | Enterprise data and AI platform | Enterprise analytics in the cloud and on-premises | Not stated |
Ten lakehouse engines and data platforms
ClickHouse
Best for: Teams that need very fast real-time analytics, from an open source OLAP database you can self-host or run as a managed cloud service.
Cloudera
Best for: Large enterprises that need an open data lakehouse, streaming and data engineering across hybrid estates and any cloud, not only one public cloud.
Dremio
Best for: Teams standardising on Apache Iceberg who want a lakehouse query engine with an open catalog, fully managed or self-hosted on Kubernetes.
e6data
Best for: Engineers with data already in Iceberg, Delta or Hudi tables who want a compute engine for SQL, streaming and AI that scales one vCPU at a time.
Google BigQuery
Best for: Google Cloud users who want one platform automating the data life cycle from ingestion to AI-driven insights, with streaming and open source interoperability.
Microsoft Fabric
Best for: Organisations already using Power BI who want data engineering, warehousing, data science and real-time intelligence in one platform built on OneLake.
Onehouse
Best for: Companies that want an open lakehouse running inside their own cloud account, with managed engines and the open source Apache Hudi project behind it.
Snowflake
Best for: Enterprises wanting one managed platform for analytics, continuous data pipelines and agentic AI across all their data, with near-zero maintenance.
Starburst
Best for: Data teams that want to query warehouses, lakes, databases and SaaS apps in place with one federated query, built around Trino and Iceberg.
Teradata
Best for: Established enterprises wanting analytics and AI that run in the cloud and on-premises, with autonomous AI turning data into decisions and action.
Want AI doing the heavy lifting in your marketing?
I build the systems that handle the boring 80 percent, so you get your week back. Done properly, with the human kept in.
Who owns which lakehouse now
- Dremio's home page now says it's part of SAP. If you pick it, you're buying into SAP's data plans as well.
- BigQuery's page is titled 'AI data platform', and Snowflake calls itself the 'AI Data Cloud'. The warehouse label is fading even at the biggest names.
- e6data calls itself a lakehouse compute engine, and Onehouse sells open data infrastructure that runs in your account. Both pitch leaving your data where it sits and swapping the compute.
Open lakehouse or managed data platform?
This is the real fork in the road. An open lakehouse keeps your data in open table formats such as Iceberg, Delta or Hudi in your own storage, and lets you point different engines at it. A managed platform moves your data into one vendor's service and handles more for you. The first gives you choice and less lock-in; the second gives you fewer moving parts.
Questions to settle before a lakehouse migration
- Which table format is your data in today, and will the new tool read it in place?
- Do you need notebooks and ML, or mainly SQL analytics and dashboards?
- Must anything stay on-premises or inside your own cloud account?
- Which cloud are you already committed to? Several platforms here lean toward one.
- Who owns the vendor, and has that changed recently? It affects support and the roadmap.
Moving the data is often the hardest part of a switch. My guide to data integration tools covers connectors and replication.
Do you make a lakehouse or data platform? Tool makers can ask to be considered for a featured place on this list.
My plain advice on leaving Databricks
Keep your data in an open table format if you can, because that turns every later decision into a choice of engine rather than a migration. Then try two very different options on the same real workload: one open engine that reads your tables in place and one managed platform. Measure query speed, how much your team has to look after and how easily your BI tools connect. That one test will tell you more than any vendor benchmark. Microsoft shops weighing Databricks against its own platform should read this Databricks and Fabric comparison.
And for the people reading dashboards at the end, my roundup of Metabase alternatives covers lighter BI tools.
Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.
Frequently asked questions
What's the closest alternative to Databricks?
It depends on what you use it for. For lakehouse workloads on open tables, an open lakehouse engine is closest. For a single managed platform covering analytics and AI, the big cloud data platforms are the nearer match.
Is there an open source Databricks alternative?
Partly. One OLAP database on this list is open source, one lakehouse engine offers a free community edition and another is built on the open source Apache Hudi project.
What is a lakehouse?
A lakehouse stores data in open table formats and lets you run warehouse-style SQL and AI workloads on it, without copying it into a separate warehouse first.
Can I query data without moving it?
Yes. Federated query engines run one query across warehouses, lakes, databases and SaaS apps in place, and lakehouse engines read open tables where they sit.
Do I need Spark to run a lakehouse?
No. Several engines here run SQL directly on open tables, though managed Spark is still on offer from some vendors if your jobs depend on it.
