# 10 Best Data Lakehouse Solutions in 2026 Compared

> The best data lakehouse solution depends on which parts of your data platform you want a vendor to operate. Some platforms combine ingestion, SQL analytics, machine learning, and governance. Others concentrate on querying existing tables or managing their ingestion and maintenance. Those differences affect deployment effort, interoperability, and the bill as much as query performance does.

- Canonical URL: https://www.puppygraph.com/blog/best-data-lakehouse-solutions
- Published: 2026-11-13
- Author: Hao Wu

The best data lakehouse solution depends on which parts of your data platform you want a vendor to operate. Some platforms combine ingestion, SQL analytics, machine learning, and governance. Others concentrate on querying existing tables or managing their ingestion and maintenance. Those differences affect deployment effort, interoperability, and the bill as much as query performance does.

This guide compares ten options for 2026 by architecture, features, workload fit, pricing model, and the trade-offs to test before adopting one. It starts with how a lakehouse works, then separates the capabilities each solution supplies from the responsibilities your team keeps.

## What is a data lakehouse solution?

A data lakehouse solution combines the flexible storage of a data lake with database capabilities such as transactional table updates, schema management, and governed analytical access. Its purpose is to let data engineering, business intelligence, and machine learning work from shared datasets with reliable table semantics. The [Databricks lakehouse overview](https://docs.databricks.com/aws/en/lakehouse/) describes this combination of an optimized metadata layer, standard data formats, and analytical compute.

In a common implementation, object storage holds data files, an open table format tracks the contents and versions of each table, and a catalog makes those tables discoverable. Compute engines read and update them through supported interfaces. The platform adds the services needed to ingest, secure, optimize, and monitor that data.

A table format is one component of this architecture. Apache Iceberg, Delta Lake, and Apache Hudi do not individually provide a complete managed analytics platform. Similarly, a SQL engine can supply a lakehouse's query layer while leaving ingestion, storage, and other operations to separate services. A product evaluation needs to account for that scope.

The practical benefit is reuse: a curated dataset can serve reports, transformations, and model development without requiring a separately maintained copy for every engine. An [open lakehouse](https://www.puppygraph.com/blog/open-lakehouse) makes that engine choice more flexible, although compatible file formats alone do not guarantee interchangeable catalogs, permissions, or write behavior.

## How do data lakehouse platforms work?

A lakehouse coordinates five responsibilities. Vendors package them differently, but each responsibility needs an owner.

**Storage and ingestion.** Files land in object storage through batch loads, streaming pipelines, or change data capture. A platform may also reference tables that already exist. Ingestion still needs to handle schema changes, duplicate events, and late data; adopting a lakehouse does not settle those application-level decisions.

**Transactional tables.** A table format records which files belong to a valid table state. For example, Iceberg commits replace the current metadata reference atomically, so readers see a consistent snapshot while writers make changes. Its [reliability documentation](https://iceberg.apache.org/docs/latest/reliability/) also explains how concurrent writers validate changes against the current table state and retry compatible commits. This coordination is what makes a collection of files behave like a table.

**Catalogs and access control.** The catalog resolves table names and metadata. Platform services connect that metadata to identities, permissions, discovery, and lineage. When a second engine accesses the same table, verify both its catalog integration and its authorization path. A successful read proves connectivity; it does not prove that the engine enforces every policy configured elsewhere.

**Compute engines.** SQL engines serve reporting and interactive analysis; processing engines handle transformations and model preparation. Separating compute from storage lets teams allocate resources by workload. Sharing a table does not require a dashboard query and a large backfill to share the same compute pool.

**Maintenance and recovery.** Tables need compaction, metadata maintenance, and retention policies as writes accumulate. Platforms automate different amounts of this work. Snapshot history supports recovery only while the required metadata and files remain available, so retention and cleanup must match the recovery window the business needs.

![How do data lakehouse platforms work?](https://o6xswp5m6ilbks2z.public.blob.vercel-storage.com/blog-learn-eca86ff0762ffcca76460a18.png)

Figure: Compare platforms by which lakehouse responsibilities they operate and which your team must own.

During evaluation, follow one dataset through this entire lifecycle: ingest it, update it, query it from a second engine, enforce a restricted user's access, and recover from a bad write. That exercise reveals more about the operating model than a query against a static sample table.

## 10 best data lakehouse solutions

The following shortlist covers integrated platforms, cloud-service architectures, and products centered on lakehouse SQL or table operations. The recommendations are based on their documented capabilities and architectural fit, rather than a benchmark ranking. Pricing describes the billing model because a vendor's compute unit is not directly comparable with another vendor's credit or capacity unit.

### 1\. Databricks

Databricks is a strong fit when data engineering, SQL analytics, and machine learning need to share a platform. Its [platform architecture](https://docs.databricks.com/aws/en/lakehouse/) combines Spark-based processing, Delta Lake, Unity Catalog, and SQL analytics, giving engineering and analytics teams a common environment for producing and consuming tables.

Delta Lake is central to that architecture, and Databricks also [supports Iceberg tables](https://docs.databricks.com/aws/en/iceberg/) managed by Unity Catalog or accessed through foreign catalogs. The distinction matters: table management, lifecycle automation, and the operations available through external engines depend on the chosen arrangement.

**Evaluation focus.** Test the same table through your intended writer, Databricks SQL, and an external reader. Include schema changes and permissions. A successful file read does not establish that a platform-specific view or policy will work through another engine.

**Pricing.** [Databricks pricing](https://www.databricks.com/product/pricing) meters many services in Databricks Units (DBUs), with rates varying by workload and compute type. Budget for the selected services and applicable cloud infrastructure and storage, including pipeline execution and background maintenance.

### 2\. Snowflake

Snowflake fits organizations that want managed SQL analytics while extending access to open lakehouse tables. Its [Iceberg table support](https://docs.snowflake.com/en/user-guide/tables-iceberg) lets teams choose catalog and storage arrangements alongside their existing Snowflake workloads.

Snowflake can manage the Iceberg catalog or connect to an external catalog. With Snowflake catalog management, the [storage options](https://docs.snowflake.com/en/user-guide/tables-iceberg-storage) include Snowflake-provided storage and customer-managed cloud storage. Tables using an external catalog require customer-managed cloud storage through an external volume. Catalog ownership and storage ownership are distinct, but the supported combinations depend on the configuration.

**Evaluation focus.** Decide who owns table maintenance and recovery, then test the required read and write paths from other engines. Do not assume that every Iceberg configuration behaves like a native Snowflake table. The right configuration depends on whether Snowflake is the main writer or one consumer of a shared table.

**Pricing.** [Compute consumption](https://docs.snowflake.com/en/user-guide/cost-understanding-compute) is credit-based, covering virtual warehouses and separately metered services. Include storage, table-management work, and data transfer in the estimate. Customer-managed Iceberg storage also creates costs with the cloud storage provider.

### 3\. Microsoft Fabric

Microsoft Fabric is a natural shortlist candidate for teams already using Power BI and Microsoft analytics tooling. Its [lakehouse experience](https://learn.microsoft.com/en-us/fabric/data-engineering/lakehouse-overview) combines files and Delta tables in OneLake with Spark processing, a SQL analytics endpoint, and integration with reporting and pipelines.

The main benefit is the integrated workflow: engineers prepare data in the lakehouse, and analysts consume it through familiar tools. [OneLake shortcuts](https://learn.microsoft.com/en-us/fabric/onelake/onelake-shortcuts) can also reference supported external data without making another ingestion copy. This makes Fabric useful when organizational adoption matters as much as assembling individual infrastructure components.

**Evaluation focus.** Test the intended split between Spark, the lakehouse SQL endpoint, and Fabric Warehouse. They serve different development patterns. Also exercise concurrent pipeline and reporting activity to understand the capacity your workload needs.

**Pricing.** [Fabric pricing](https://azure.microsoft.com/en-us/pricing/details/microsoft-fabric/) includes compute capacity and OneLake storage. Power BI licensing requirements depend on how reports are published and consumed. Include those requirements when comparing Fabric with products that supply only a query engine.

### 4\. AWS: the lakehouse architecture of Amazon SageMaker

AWS is a strong fit when substantial data already lives in Amazon S3 and Amazon Redshift. The [lakehouse architecture of Amazon SageMaker](https://docs.aws.amazon.com/sagemaker-lakehouse-architecture/latest/userguide/) provides a unified way to access that data through Iceberg-compatible interfaces and AWS analytics services.

The architecture builds on AWS Glue Data Catalog for metadata and AWS Lake Formation for access management. It connects to engines such as Athena, Redshift, and Spark through AWS processing services. Amazon S3 Tables supplies another storage option with managed Iceberg table capabilities. AWS documents these relationships in its [architecture guide](https://docs.aws.amazon.com/sagemaker-lakehouse-architecture/latest/userguide/lakehouse-how.html).

**Evaluation focus.** Treat this as an architecture assembled from services. Assign ownership for catalog setup, IAM and Lake Formation permissions, table maintenance, and workload execution. Test those boundaries with the actual engines and accounts that will consume the data.

**Pricing.** [AWS charges for the services used](https://aws.amazon.com/sagemaker/pricing/). The estimate includes storage, query and processing compute, catalog activity, and any applicable governance, networking, or maintenance charges. A single service's unit price does not represent the lakehouse's total cost.

### 5\. Google Cloud: BigQuery and Lakehouse

Google Cloud fits teams that want BigQuery analytics together with shared Iceberg tables. Google renamed BigLake to Lakehouse in its [2026 product updates](https://docs.cloud.google.com/lakehouse/docs/release-notes); some APIs and documentation paths retain the earlier name.

The [Lakehouse architecture](https://docs.cloud.google.com/lakehouse/docs/introduction) uses Iceberg and a managed runtime catalog to coordinate access from BigQuery and other engines. Separately, BigQuery's [Iceberg managed tables](https://docs.cloud.google.com/bigquery/docs/biglake-iceberg-tables-in-bigquery) store data in customer-owned Cloud Storage buckets while BigQuery manages writes and storage optimization.

**Evaluation focus.** Choose the table-management arrangement before choosing the writer. External access and ownership of updates depend on that arrangement. For BigQuery-managed Iceberg tables, directly changing the underlying files outside the supported interface can leave BigQuery's metadata inconsistent with storage.

**Pricing.** [BigQuery query pricing](https://cloud.google.com/bigquery/pricing) offers on-demand billing by data processed or capacity billing by allocated slot capacity over time. An Iceberg deployment also needs an estimate for Cloud Storage, optimization, and the other services that ingest or process its data.

### 6\. Dremio

Dremio is a strong candidate for interactive SQL and BI over lakehouse data, particularly when teams also need to combine it with external databases. Its [documented capabilities](https://docs.dremio.com/current/what-is-dremio/) include querying, ingestion, and transformations on the lake, with Apache Iceberg central to its open-table approach.

A distinctive feature is [Reflections](https://docs.dremio.com/current/acceleration/): precomputed representations of source data or query results that Dremio can use to accelerate queries transparently. They can be useful for repeated dashboard workloads where many queries reuse the same joins or aggregations.

**Evaluation focus.** Compare performance and total resource use with and without the intended Reflections. Their storage, refresh work, and freshness requirements belong in the design. A benchmark that excludes maintenance gives an incomplete picture of the workload's cost.

**Pricing.** Dremio Cloud uses [consumption billing measured in DCUs](https://www.dremio.com/pricing/). Enterprise also uses consumption pricing, with terms arranged with the vendor. Include the selected deployment's infrastructure, storage, and support costs when comparing it with a managed service.

### 7\. Starburst

Starburst fits teams that need SQL across lakehouse tables and other data sources. Its products build on Trino: [Galaxy is the managed service, while Enterprise is the commercial distribution for customer-controlled deployments](https://docs.starburst.io/introduction/choose-your-starburst-product.html).

Federation is the main architectural attraction. A query can combine data across connected systems, allowing a team to retain useful existing sources while building its lakehouse. Galaxy supports several [lakehouse table formats](https://docs.starburst.io/starburst-galaxy/data-engineering/working-with-data-lakes/table-formats/), with data-management operations varying by format.

**Evaluation focus.** Test joins that cross source boundaries, including the impact on operational databases and cross-region traffic. Federation provides access; the query plan, available pushdown, network path, and source capacity determine how well a particular query runs.

**Pricing.** [Galaxy billing](https://docs.starburst.io/starburst-galaxy/cluster-administration/monitor-and-manage-cost-and-performance/usage-billing.html) tracks compute credits and applicable additional services, including ingestion and data transfer. For Enterprise, budget for infrastructure and any [licensed features and support](https://docs.starburst.io/latest/license-requirements.html) the deployment needs. Compare the managed and customer-operated options separately.

### 8\. Cloudera

Cloudera belongs on the shortlist when a lakehouse must span cloud environments and existing data centers. Its [platform portfolio](https://docs.cloudera.com/products.html) includes data engineering, warehousing, AI, and shared security and governance, with deployment options that accommodate on-premises infrastructure.

Its Iceberg-based lakehouse approach lets organizations modernize table storage while retaining an established platform and operating model. Cloudera's [Iceberg integration with Shared Data Experience and Apache Ozone](https://docs.cloudera.com/cdp-private-cloud-base/7.1.9/runtime-release-notes/topics/rt-pvc-whats-new.html) illustrates how open tables fit into that environment.

**Evaluation focus.** Inventory the services and infrastructure that must remain on premises, then evaluate the lakehouse against that requirement. For customer-operated deployments, include upgrades, capacity planning, and operational staffing. That responsibility may be justified by deployment control, but it should be explicit in the comparison.

**Pricing.** [Cloudera's pricing model](https://www.cloudera.com/products/pricing.html) combines consumption-based cloud service charges and subscription-based on-premises offerings. Infrastructure and the chosen services affect the final cost. Compare a complete deployment estimate rather than equating a software rate with an all-inclusive managed platform.

### 9\. IBM watsonx.data

IBM watsonx.data fits hybrid environments that need multiple engines over an open lakehouse, particularly where the broader IBM ecosystem is already established. Its [architecture separates compute, metadata, and storage](https://cloud.ibm.com/docs/AnalyticsEngine?topic=AnalyticsEngine-spk_wxd), supports Iceberg, and includes Presto and Spark among its processing engines.

IBM offers [software, SaaS, and bring-your-own-cloud deployment options](https://www.ibm.com/products/watsonx-data/pricing). This makes deployment location and operational responsibility part of the product choice. The relevant configuration depends on which workloads need interactive SQL, large transformations, or integration with other enterprise services.

**Evaluation focus.** Confirm the engines, governance integrations, and supporting products required for the complete workflow. Multi-engine support does not mean identical SQL behavior or write capabilities across engines. Validate the operations your pipeline actually performs.

**Pricing.** [Service billing uses Resource Units](https://www.ibm.com/docs/en/watsonxdata/saas?topic=infrastructure-billing-usage), including compute and supporting infrastructure such as metadata storage and access control. Those supporting services can continue accruing charges after query compute is paused. Software and customer-cloud deployments have their own commercial and infrastructure arrangements, described in [IBM's pricing options](https://www.ibm.com/products/watsonx-data/pricing).

### 10\. Onehouse

Onehouse is a strong candidate when the main work is ingesting and transforming changes incrementally and maintaining open lakehouse tables in your own cloud account. Its [platform](https://docs.onehouse.ai/product/onehouse-intro/) provides managed ingestion, Spark jobs, SQL transformations, and table services such as compaction and clustering.

Its scope also includes query compute and [connections to external engines](https://docs.onehouse.ai/product/onehouse-intro/). Teams can therefore evaluate it as a managed foundation for table operations while retaining other tools for consumption. Its support for [multiple open formats and metadata synchronization](https://docs.onehouse.ai/product/manage-data/table-services/table-service-offerings/) makes the exact writer, reader, and synchronization arrangement an important design choice.

**Evaluation focus.** Test updates, deletes, and schema changes through the intended ingestion and query paths. Verify when each consuming engine sees those changes. Shared underlying files do not make every format-specific operation interchangeable.

**Pricing.** [Onehouse bills compute usage in Onehouse Compute Units](https://docs.onehouse.ai/product/project-settings/usage-and-billing/), plus support. Customers also pay their cloud infrastructure costs. Include those costs and external query-engine charges when comparing Onehouse with a service that bundles more of the infrastructure.

Across these options, similar feature names can hide different operating responsibilities. SQL support, an Iceberg integration, or a managed catalog becomes useful only when it covers the ingestion, update, security, and recovery paths your organization needs.

## Comparison of the best data lakehouse solutions

Use this table to narrow the shortlist by role and workload. The billing column summarizes the models discussed above, not an estimate of equivalent cost.

| **Solution** | **Main role and useful fit** | **Main issue to validate** | **Pricing basis** |
| --- | --- | --- | --- |
| Databricks | Integrated engineering, SQL, and ML platform | Table-management choices and external-engine interoperability | DBUs by service, plus applicable infrastructure and storage |
| Snowflake | Managed analytics with Iceberg options | Catalog ownership, storage ownership, and external write paths | Compute credits, storage, and additional services |
| Microsoft Fabric | Integrated analytics for Power BI and Microsoft teams | Workload capacity and the Spark/SQL development split | Compute capacity, storage, and applicable BI licenses |
| AWS lakehouse architecture | Shared access across an AWS data estate | Permissions and operations across constituent services | Separate AWS service consumption |
| Google Cloud BigQuery and Lakehouse | Managed analytics with shared Iceberg data | Table ownership and supported update paths | Query data processed or allocated slot capacity, plus storage and services |
| Dremio | Lakehouse SQL, BI, and external-source access | Reflection refresh, storage, and workload freshness | Cloud DCUs or Enterprise consumption agreement |
| Starburst | Federated SQL over lakehouse and other sources | Cross-source query plans, source load, and data movement | Galaxy credits and services; Enterprise infrastructure and applicable licenses |
| Cloudera | Hybrid platform spanning cloud and data centers | Deployment operations and upgrade responsibility | Cloud consumption or on-premises subscriptions |
| IBM watsonx.data | Hybrid lakehouse with multiple engines | Engine behavior, packaging, and persistent service costs | Resource Units or software arrangements |
| Onehouse | Managed ingestion, transformations, and table operations with query compute | Change propagation across the chosen format and engine paths | Compute units and support, plus customer cloud infrastructure |

The most useful first filter is the operating model. If the goal is one environment for engineering and analytics, evaluate the integrated platforms against that workflow. If useful data already spans several systems, federation deserves more weight. If data must remain in a company-operated data center, deployment support becomes a prerequisite. If maintaining tables consumes the team's time, test the automation of ingestion and table services first.

For the remaining candidates, run a small workload that includes both normal activity and failures. Measure dashboard concurrency while ingestion runs, the effect of a backfill, recovery after a bad write, and access from a second engine. Include maintenance jobs and idle services in the cost estimate. A configuration that meets a latency target only while ingestion is stopped has not met the requirement.

Finally, test whether the lakehouse can support additional query models over the same tables. That flexibility matters when the next analytical question concerns a chain of relationships rather than a grouped metric.

PuppyGraph is the first and only zero-ETL graph query engine that [queries existing tables as a graph](https://www.puppygraph.com/) without loading them into a separate graph database. Its distributed engine reads tables in place in SQL databases, data warehouses, and data lakes or lakehouses, including direct reads of Iceberg and Delta Lake. Connect the sources, define a graph schema mapping tables to nodes and edges, and query with openCypher or Gremlin while the same tables remain available to SQL.

![Comparison of the best data lakehouse solutions](https://o6xswp5m6ilbks2z.public.blob.vercel-storage.com/blog-learn-4b2587a755bbd5c9068284ee.png)

Figure: PuppyGraph queries the existing tables in place, so graph and SQL queries share one copy of the data and no pipeline keeps a graph copy in sync.

For example, a team could model package versions, deployed services, and customer subscriptions to trace which subscriptions depend on a vulnerable library through nested dependencies. That adds relationship analysis to the lakehouse while keeping its existing storage, ingestion, and SQL workflows.

![Comparison of the best data lakehouse solutions](https://o6xswp5m6ilbks2z.public.blob.vercel-storage.com/blog-learn-5820007cae8f617f5bb57a14.png)

Figure: Companies that use PuppyGraph.

![Comparison of the best data lakehouse solutions](https://o6xswp5m6ilbks2z.public.blob.vercel-storage.com/blog-learn-4492653ad4ba877e25923f1c.png)

Figure: Engineering teams at PuppyGraph customers on what they use it for.

## Conclusion

Choose a data lakehouse solution around the workloads it must support and the operations your team is prepared to own. Integrated platforms, federated query engines, and managed table services solve different parts of that problem. A useful evaluation proves the complete path from ingestion to queries and recovery, with permissions and total cost included.

Open tables expand the choice of engines that can use your data. Validate that flexibility with a second engine during the evaluation, so future analytical requirements can build on the lakehouse you already operate.

Try the forever-free [PuppyGraph Developer Edition](https://www.puppygraph.com/download-confirmation) and [book a demo](https://www.puppygraph.com/book-demo) with the team to see how openCypher and Gremlin queries trace relationships across warehouse and lakehouse tables, with no graph-specific ETL, as another workload on your existing data platform.