Data Lakehouse

Query your lakehouse
without copying the data.

Apache Iceberg made the lakehouse possible. PhoenixAI makes it fast. Query your lakehouse tables at sub-second latency — no ETL, no data movement, no separate warehouse to maintain.

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DW speed

On lakehouse data

Without copying data into a warehouse

ETL jobs needed

Query in place

Data movement

No lake-to-warehouse pipelines

The problem

The lakehouse is great for storage. Not always fast for queries.

The lakehouse query problem

Slow interactive queries force data movement

Complex analytical queries on Apache Iceberg take minutes, not seconds

Teams copy data into a warehouse just to get interactive performance

Two copies of data: one in the lake, one in the warehouse

Sync pipelines between lake and warehouse break and drift

With PhoenixAI

Data warehouse speed. Data lake economics.

Sub-second queries directly on Apache Iceberg tables

Intelligent tiered cache — no copy needed, warehouse-like speed

Single source of truth — no sync pipeline, no data drift

Query streaming and historical in the same query

Capabilities

The query layer your lakehouse needs.

What PhoenixAI adds to an Apache Iceberg architecture.

Native lakehouse integration

Native execution on lakehouse tables such as Apache Iceberg delivers data warehouse performance, directly on your lake tables — no copies required.

Intelligent tiered cache

Tiered cache across memory and local SSD delivers sub-second Apache Iceberg queries, with all your data persisted in the lake as a single source of truth.

Query streaming and historical in one query

Join or union real-time streaming data with historical lakehouse tables in the same SQL query. No federation overhead, no separate engine for each layer.

Async materialized views

Pre-compute hot data on Apache Iceberg tables. Queries automatically rewrite to hit the MV instead of scanning the full Iceberg table — sub-second answers on multi-petabyte fact tables.

Works with your lakehouse

PhoenixAI integrates with open table formats, object storage, catalogs, and the processing engines that feed your lakehouse.

Open table formats

Apache IcebergDelta LakeApache Hudi

Object storage

Amazon S3Google GCSAzure ADLSMinIO

Catalogs & platforms

AWS GlueHive MetastoreDatabricks Unity CatalogSnowflake Horizon CatalogAmazon S3 Tables

In production

What lakehouse teams say after switching.

We provide booking services for over 1.5 million hotels worldwide. By using PhoenixAI we realized high-speed data analysis with an average query response speed of 200ms. Thanks to the unified data analytical architecture, manpower and hardware costs are greatly reduced.

Trip.com Group

PhoenixAI customer

200ms

avg. query response

Query your lakehousewithout copying the data.