7 Best Open Source Lakehouse Tools and Free Platforms

Modern data teams increasingly want the flexibility of a data lake without giving up the reliability, performance, and data management capabilities traditionally associated with a warehouse. This has made lakehouse architecture a major part of modern data architecture, particularly for organizations managing large volumes of structured, semi-structured, batch, and streaming data across cloud environments.

Open source lakehouse tools give teams another advantage: they can build around open technologies instead of relying entirely on a single proprietary platform. Depending on the architecture, a free and open-source lakehouse stack can provide capabilities such as ACID transactions, schema evolution, time travel, streaming ingestion, table management, and multi-engine data access while allowing organizations to choose their preferred storage and compute infrastructure.

However, the best open-source lakehouse tool depends heavily on what a team is actually trying to build. Some organizations need a flexible open table format that works across multiple query engines, while others prioritize real-time updates, change data capture, transactional data management, or lakehouse metadata. This guide covers the top and best open source lakehouse tools, along with relevant non-open-source tools and platforms for teams evaluating different approaches to building a modern data stack.

What is a Lakehouse Tool?

A lakehouse tool helps organizations build, manage, or operate a data architecture that combines the flexible, scalable storage of a data lake with capabilities traditionally associated with data warehouses. Depending on the technology, this can include transactional table management, schema evolution, time travel, streaming and batch processing, and access to data through multiple analytics engines.

Open source lakehouse tools can be used as individual components or combined to build a broader lakehouse architecture. Some focus on managing analytical tables, while others support real-time data updates, metadata, versioning, or catalog management. The right choice depends on the existing data stack, workload requirements, and how much control a team wants over its infrastructure.

Open Source Lakehouse Tools Comparison

Also read: Best Open Source Data Lake Tools

The 7 Best Open Source Lakehouse Tools

The best open source lakehouse tools help data teams build flexible, scalable architectures without relying entirely on a single proprietary platform. From open table formats to real-time lakehouse technologies and metadata layers, these free tools support different parts of a modern lakehouse stack.

#1 Apache Iceberg

Apache Iceberg is one of the leading open source lakehouse tools for teams building a flexible data architecture across multiple query and processing engines. It is an open table format designed to bring database-like reliability to large analytical datasets stored in object storage and other data lake environments.

Rather than tying data to a single analytics platform, Apache Iceberg allows the same tables to work across engines such as Apache Spark, Trino, Apache Flink, Presto, and others. This interoperability is a major reason Iceberg has become a popular choice for organizations building vendor-neutral lakehouse architectures.

For data teams, Iceberg can simplify several challenges associated with traditional data lakes, including partition management, schema changes, concurrent writes, and maintaining historical versions of datasets. Its architecture separates table metadata from the underlying data files, making it easier to manage large and continuously evolving datasets.

Key Features

• Multi-engine interoperability: Apache Iceberg tables can be accessed by multiple analytics and processing engines, helping teams avoid building their lakehouse around a single compute platform.
• Schema evolution: Teams can add, rename, reorder, or remove columns without relying on complex manual migration processes, making the open source lakehouse easier to maintain as data requirements change.
• Hidden partitioning: Iceberg manages partition information through table metadata, allowing users to query data without needing to understand the physical partition structure.
• Time travel and snapshots: Historical versions of a table can be accessed for auditing, debugging, reproducibility, and data recovery.
• ACID transactions: Supports reliable concurrent reads and writes, which is important when multiple pipelines or users interact with the same analytical data.
• Scalable table metadata: Designed to manage very large tables without creating the metadata and file-management problems often associated with traditional data lake architectures.

Best For

Apache Iceberg is best for data teams building a multi-engine, open source lakehouse architecture where interoperability, vendor flexibility, and long-term table management are more important than using a tightly integrated proprietary platform. It is particularly suitable for organizations working across Spark, Trino, Flink, or multiple analytics engines.

#2 Delta Lake

Delta Lake is an open source lakehouse technology designed to add stronger reliability and data management capabilities to data stored in cloud object storage and data lakes. It is widely associated with the Spark ecosystem and is one of the best-known technologies behind the modern lakehouse approach.

Its core value comes from bringing features such as ACID transactions, schema enforcement, schema evolution, and time travel to analytical datasets. These capabilities help data teams manage data more reliably than traditional file-based data lakes, particularly when multiple pipelines need to read from or write to the same tables.

Delta Lake is a strong option for organizations already using Apache Spark or building around a Databricks-oriented ecosystem. While the technology has expanded beyond its original ecosystem, its strongest fit is still often found in environments where Spark-based processing plays a central role.

Key Features

• ACID transactions: Helps maintain consistent table states when multiple data operations and pipelines interact with the same datasets.
• Schema enforcement: Prevents unexpected or incompatible data from entering a table, reducing the risk of downstream data quality issues.
• Schema evolution: Allows table structures to change as business and technical requirements evolve without requiring teams to rebuild datasets from scratch.
• Time travel: Supports access to previous table versions, which can help with auditing, debugging, recovery, and reproducibility.
• Merge, update, and delete operations: Supports more flexible data modifications than traditional append-only data lake approaches.
• Strong Spark integration: Works naturally with Spark-based data engineering and analytics workflows.

Best For

Delta Lake is best for organizations already using Apache Spark or Databricks-style data architectures that want an open source lakehouse storage layer with mature transactional capabilities and strong support for large-scale analytical workloads.

#3 Apache Hudi

Apache Hudi is an open source lakehouse platform designed for data environments where records are constantly changing. While many analytical architectures primarily focus on adding new data, Hudi is particularly well suited to workloads involving frequent updates, deletes, change data capture, and incremental processing.

This makes it a strong choice for organizations collecting data from operational systems, databases, applications, or event streams where datasets need to reflect changes over time. Instead of repeatedly processing an entire dataset, Hudi can help downstream workloads focus on the records that have changed.

Compared with some open source lakehouse tools that primarily focus on table interoperability, Apache Hudi places greater emphasis on managing mutable data and maintaining large datasets through services such as compaction, clustering, indexing, and cleaning.

Key Features

• Incremental processing: Allows pipelines and downstream consumers to process changed data rather than repeatedly scanning entire datasets.
• Update and delete support: Handles mutable datasets where existing records regularly need to be modified or removed.
• Built-in table services: Supports maintenance operations such as compaction, clustering, cleaning, and indexing to help manage table performance.
• CDC support: Fits naturally into change data capture architectures that continuously synchronize changes from operational systems.
• Streaming and batch processing: Supports both real-time and batch-oriented lakehouse workloads.
• Multiple storage and engine integrations: Can work across major cloud storage platforms and commonly used data processing technologies.

Best For

Apache Hudi is best for teams building an open source lakehouse around CDC, incremental processing, streaming ingestion, and update-heavy datasets where efficiently handling changing records is a core requirement.

#4 Apache Paimon

Apache Paimon is an open source lakehouse technology built with real-time and streaming workloads in mind. It is particularly relevant for data teams that need to manage continuously changing datasets while supporting both streaming and batch processing.

The project has strong connections to the Apache Flink ecosystem and uses an architecture designed to support high-performance updates and primary-key-based tables. This makes Paimon useful for use cases where data arrives continuously and needs to remain available for downstream analytics without repeatedly rebuilding entire datasets.

Among the top open source lakehouse tools, Apache Paimon stands out most clearly when streaming-first architecture and frequent data updates are central to the decision.

Key Features

• Real-time data updates: Supports primary-key tables designed to efficiently manage frequently changing records.
• Streaming and batch support: Allows teams to work with the same data across both real-time and batch processing environments.
• Changelog generation: Captures changes in datasets that can be consumed by downstream systems and analytics workflows.
• Schema evolution: Enables teams to adapt table structures as requirements change.
• Time travel: Supports access to previous table states for troubleshooting and data recovery.
• Flexible merge strategies: Provides different approaches for deduplication, partial updates, aggregation, and record-level changes.

Best For

Apache Paimon is best for organizations building streaming-first or real-time open source lakehouse architectures, particularly those already using Apache Flink and working with high volumes of updates or change events.

#5 Project Nessie

Project Nessie approaches the lakehouse from a different angle. Instead of acting as a table format or storage framework, it focuses on how data teams manage changes to data and metadata across shared environments.

Nessie introduces Git-like concepts such as branches, commits, and tags into data workflows. This can allow teams to create isolated environments for experimentation and development before applying changes to shared production data. For organizations with multiple data engineers, analytics teams, or development environments, this approach can help make changes easier to manage.

As a result, Nessie is not a direct replacement for Apache Iceberg, Delta Lake, or Apache Hudi. It is better understood as a complementary open source lakehouse component for teams that need stronger data versioning and transactional catalog capabilities.

Key Features

• Git-like data versioning: Applies concepts such as branches, commits, and tags to data and metadata workflows.
• Isolated development environments: Allows teams to test changes without immediately affecting shared production environments.
• Transactional catalog capabilities: Helps coordinate changes across data assets and metadata.
• Branch-based workflows: Supports parallel experimentation and development for teams working on shared data environments.
• Version-aware data management: Makes it easier to track and manage changes across evolving lakehouse datasets.
• Flexible ecosystem integration: Can support broader lakehouse architectures where versioning and catalog management are important.

Best For

Project Nessie is best for data engineering teams that need version-controlled data workflows, isolated development environments, and safer collaboration across shared lakehouse data.

#6 Lakekeeper

Lakekeeper is an open source catalog designed for Apache Iceberg environments. It implements the Iceberg REST Catalog approach and provides a separate catalog layer for organizations that want to manage Iceberg tables without tightly coupling that responsibility to a single query engine or cloud platform.

The project is particularly relevant as open source lakehouse architectures become more modular. A team may choose Iceberg for table management, one or more compute engines for processing, and a dedicated catalog for metadata and access management.

Lakekeeper also takes a relatively lightweight approach to deployment and is built in Rust, making it an interesting option for teams looking for a modern, self-hosted catalog component within an open source lakehouse stack.

Key Features

• Iceberg REST Catalog support: Provides a standardized catalog interface for connecting Iceberg-compatible tools and engines.
• Centralized table management: Helps manage namespaces, tables, and related metadata through a dedicated catalog layer.
• Access-control capabilities: Supports managing permissions and access across lakehouse environments.
• Cloud and on-premises support: Can fit architectures running on major cloud providers or S3-compatible storage.
• Cloud-native deployment: Supports containerized and Kubernetes-based environments.
• Lightweight architecture: Uses a Rust-based implementation designed for modern lakehouse deployments.

Best For

Lakekeeper is best for organizations standardizing on Apache Iceberg that want an open source, self-hosted catalog with a modular architecture and support for modern cloud-native deployments.

#7 Apache Gravitino

Apache Gravitino is an open source metadata lake designed for organizations working across multiple data systems, engines, and environments. Rather than focusing only on one table format, it aims to provide a unified way to manage metadata and governance across a broader data architecture.

This makes Gravitino particularly relevant for larger organizations where the data environment includes multiple databases, storage systems, analytics engines, and cloud platforms. Instead of treating each system as an entirely separate metadata environment, teams can use a federated approach to improve consistency.

For organizations building a distributed or multi-engine open source lakehouse architecture, Gravitino can address challenges that extend beyond individual tables and into metadata management, federation, and governance.

Key Features

• Unified metadata management: Provides a common layer for managing metadata across different data systems and storage environments.
• Multi-engine support: Designed to work across data processing and query technologies used in modern architectures.
• Data federation capabilities: Helps connect distributed data environments rather than requiring all assets to live in one platform.
• Centralized governance: Supports more consistent management of metadata and data access across multiple systems.
• Lakehouse-oriented architecture: Can work alongside open table formats and other components of a modern data stack.
• Distributed environment support: Fits organizations operating across multiple clouds, engines, or geographically distributed systems.

Best For

Apache Gravitino is best for larger data teams building a federated open source data architecture across multiple engines, storage systems, and environments where centralized metadata management is a growing requirement.

Non-Open-Source Lakehouse Tools and Platforms

Open source lakehouse tools offer flexibility and architectural control, but some teams may prefer a commercial platform that manages more of the underlying infrastructure. These non-open-source lakehouse tools and platforms can be a better fit when faster deployment, integrated governance, enterprise support, and reduced operational complexity are more important than building and maintaining every layer independently.

Databricks

Databricks is one of the most established lakehouse platforms and provides an integrated environment for data engineering, analytics, AI, machine learning, governance, and data management. It is particularly relevant for teams that want to use lakehouse concepts without assembling and operating every component of an open source stack themselves.

Snowflake

Snowflake provides a managed cloud data platform that increasingly supports open data architectures, including Apache Iceberg. It can be a strong alternative for organizations that want managed infrastructure and SQL-based analytics while still working with data stored outside a traditional proprietary warehouse environment.

Onehouse

Onehouse is a managed lakehouse platform built around open technologies and is particularly relevant to organizations interested in technologies such as Apache Hudi. It can reduce the operational burden of managing lakehouse infrastructure while retaining access to an open data architecture.

How to Choose the Right Open Source Lakehouse Tool

Choosing between open source lakehouse tools depends less on finding the most popular project and more on understanding how the technology will fit into your existing data architecture.

• Start with your primary workload: Consider whether your environment is primarily batch-oriented, append-heavy, update-heavy, or built around real-time streaming and change data capture. A tool that works well for analytical batch workloads may not be the best fit for continuously changing datasets.
• Check compatibility with your existing engines: If your team already relies on Spark, Trino, Flink, or multiple query engines, evaluate how well each open source lakehouse tool integrates with those technologies. Multi-engine interoperability can become increasingly important as the data stack grows.
• Decide how much architectural flexibility you need: Some teams prefer to assemble an open-source lakehouse from separate components for storage, compute, catalog management, and governance. Others may prefer a more tightly integrated platform that reduces architectural decisions and operational overhead.
• Evaluate table management requirements: Look closely at capabilities such as ACID transactions, schema evolution, time travel, partitioning, concurrent writes, and support for updates or deletes. These requirements can significantly influence whether Iceberg, Delta Lake, Hudi, or another approach is the better fit.
• Consider real-time and CDC requirements: For teams processing frequent updates, streaming events, or change data capture workloads, incremental processing and efficient handling of mutable data may be more important than broad interoperability alone.
• Review catalog and metadata needs: A lakehouse architecture may require more than an open table format. Consider whether you need centralized metadata management, an Iceberg REST catalog, data versioning, access control, or federation across multiple environments.
• Be realistic about operational complexity: Free and open source lakehouse tools can provide significant flexibility, but they may also require your team to manage infrastructure, upgrades, performance tuning, security, and table maintenance. Make sure the level of operational ownership matches your team’s capabilities.

Conclusion

The best open source lakehouse tool depends on the architecture you are trying to build rather than on a single feature or popularity metric. Apache Iceberg is a strong option for multi-engine interoperability, Delta Lake fits naturally into many Spark-based environments, while Apache Hudi and Apache Paimon are worth considering for update-heavy and streaming workloads.

Teams should also look beyond the table format itself. Catalogs, metadata, data versioning, governance, and operational requirements can all influence the final architecture. Comparing both free and open-source options with non-open-source tools and platforms can help determine whether greater flexibility or lower operational complexity is the better choice for your data team.

Frequently Asked Questions

1. What are open source lakehouse tools?

Open source lakehouse tools are technologies used to build or manage lakehouse architectures using openly available software. Depending on the tool, they can support table management, ACID transactions, schema evolution, time travel, streaming, metadata, catalogs, and other data management requirements.

2. What are the best open source lakehouse tools?

Apache Iceberg, Delta Lake, Apache Hudi, Apache Paimon, Project Nessie, Lakekeeper, and Apache Gravitino are among the leading open source technologies used across modern lakehouse architectures.

3. Is Apache Iceberg a lakehouse platform?

Apache Iceberg is not a complete lakehouse platform. It is an open table format that provides capabilities such as schema evolution, snapshots, time travel, and reliable table management. Teams typically combine it with storage, compute engines, catalogs, and other components to build a complete lakehouse architecture.

4. What is the difference between Apache Iceberg and Delta Lake?

Both technologies provide table management and transactional capabilities for lakehouse architectures. Apache Iceberg is widely used for multi-engine interoperability, while Delta Lake has historically been closely associated with Apache Spark and the Databricks ecosystem.

5. Is Apache Hudi good for real-time data?

Yes. Apache Hudi is particularly well suited to update-heavy, incremental, streaming, and CDC workloads. Its architecture focuses on efficiently managing mutable datasets and processing changes over time.

What is Apache Paimon used for?

Apache Paimon is used for real-time lakehouse architectures that need to support both streaming and batch processing. It is particularly relevant for environments handling frequent updates, primary-key tables, and continuously changing data.

What is a lakehouse catalog?

A lakehouse catalog manages metadata about tables, namespaces, storage locations, and related data assets. Catalog technologies can help multiple engines and tools discover, access, and manage the same lakehouse data.

Do open source lakehouse tools work with cloud storage?

Yes. Many open source lakehouse tools are designed to work with cloud object storage and can be deployed across public cloud, private cloud, on-premises, and hybrid environments depending on the specific technology.

Are free lakehouse tools suitable for enterprise use?

Yes. Open source and free lakehouse tools are widely used as part of enterprise data architectures. However, enterprise adoption also requires planning for infrastructure management, security, governance, observability, performance, and ongoing maintenance.

Do you need Apache Spark to build a lakehouse?

No. Although Spark is commonly used in lakehouse architectures, many open source technologies also support other engines such as Trino and Flink. The best choice depends on the existing data stack and workload requirements.

What is the difference between a data lake and a lakehouse?

A data lake primarily provides flexible and scalable storage for large volumes of data. A lakehouse builds additional data management capabilities on top of that storage, such as transactions, schema evolution, historical data access, and more reliable table management.

Should I choose an open source or managed lakehouse platform?

Choose an open source lakehouse approach when architectural flexibility, interoperability, and control are priorities and your team can manage the operational complexity. A managed platform may be a better choice when faster deployment, integrated services, and reduced infrastructure management are more important.