What is the Lakehouse SQL Query Engine?

It is a high-performance SQL analytics engine designed to run queries directly on lakehouse data with strong performance and cost efficiency.

What query formats are supported?

The engine supports standard SQL syntax and integrates with popular BI and analytics tools.

Who benefits from using this engine?

Data engineers, analytics teams, and businesses looking to optimize query performance and cost across large datasets.

Lakehouse Compute Engine for
Fast, Scalable SQL Analytics

Name: Great Analytics Tool
Rating: 4

Run low-latency, high-concurrency lakehouse queries with a Kubernetes-native lakehouse SQL engine built for performance, scale, and cost efficiency.

Get Started for Free

10x

faster

50%

lower costs

zero

migration

Architecture diagram showing a standard lakehouse query engine workflow with multiple applications, table formats, governance solutions, data catalogs, deployed across clouds, regions, and on-premises.

W/o e6data

Architecture diagram showing e6data’s lakehouse query engine workflow with multiple applications, table formats, governance solutions, data catalogs, deployed across clouds, regions, and on-premises, with zero migration from existing setup.

W/o e6data

Benchmarks

Vs. legacy lakehouse engine

3.09x

Faster

TPC-DS

Delta

8 QPS

Vs. legacy QUERY engine

11.02x

Faster

TPC-DS

Fabric

30 cores

Query type: comparison

1.58x

Faster

TPC-DS

Delta

AWS

Vs. legacy lakehouse engine

67.64%

Lower cost

TPC-DS

Delta

8QPS

Vs. legacy query engine

7.04x

Faster

TPC-DS

Iceberg

Query type: logical

1.80x

Faster

TPC-DS

Delta

AWS

Vs. legacy lakehouse engine

3.08x

Lower p99 latency

TPC-DS

Delta

8 QPS

e6data + Fabric

60.05%

Lower cost

TPC-DS

Fabric

30 cores

High Concurrency

1.20x

Faster

TPC-DS

Delta

AWS

Vs. legacy lakehouse engine

3.09x

Faster

TPC-DS

Delta

8 QPS

Vs. legacy QUERY engine

11.02x

Faster

TPC-DS

Fabric

30 cores

Query type: comparison

1.58x

Faster

TPC-DS

Delta

AWS

Vs. legacy lakehouse engine

67.64%

Lower cost

TPC-DS

Delta

8QPS

Vs. legacy query engine

7.04x

Faster

TPC-DS

Iceberg

Query type: logical

1.80x

Faster

TPC-DS

Delta

AWS

Vs. legacy lakehouse engine

3.08x

Lower p99 latency

TPC-DS

Delta

8 QPS

e6data + Fabric

60.05%

Lower cost

TPC-DS

Fabric

30 cores

High Concurrency

1.20x

Faster

TPC-DS

Delta

AWS

Why Typical Lakehouse Compute Engines Fall Short

Run low-latency, high-concurrency lakehouse queries with a Kubernetes-native lakehouse SQL engine built for performance, scale, and cost efficiency.

Slow SQL Query Performance

Legacy lakehouse SQL engines rely on shared clusters, causing slow query execution and frequent timeouts as workloads grow.

High Compute Costs

Coarse, cluster-based scaling forces teams to over-provision resources, driving unpredictable and inflated lakehouse compute costs.

High Latency in BI & Dashboards

Interactive dashboards suffer because traditional engines cannot deliver low-latency lakehouse queries under concurrent user load.

Concurrency & Throughput Bottlenecks

As more users and tools query the lakehouse, performance degrades due to limited concurrency and poor throughput management.

A Modern High-performance Lakehouse Compute Engine

A decentralized, Kubernetes-native architecture that scales compute granularly in 1-vCPU increments, designed for enterprises facing throttling, resource rationing, and vendor lock-in, while remaining compatible with all major table formats and data catalogs.

Atomic & Granular Scaling Compute Engine

Allocate resources per query, eliminating idle capacity while maintaining high performance at scale.

SQL query block depicting the ability to do vector search on top of unstructured data from any object-storage (S3, GCS, ADLS) through SQL language.

Kubernetes-Native by Design

Built to run efficiently across cloud, hybrid, and on-prem environments with built-in elasticity and resilience.

Product screenshot depicting e6data’s autoscaling feature which scales up and down automatically to accommodate peak and fluctuating workloads.

Runs with your data stack

Executes high-performance lakehouse queries directly on existing storage formats and BI tools, without data movement or re-architecture.

Product image depicting e6data’s query guardrails feature, which alerts users about any bad query (example with high number of table scans) to enable them to abort or redirect it and maintain SLAs.

Lakehouse SQL Query Engine for High-Concurrency & AI Workloads

Interactive SQL performance

Enables fast query responses for interactive dashboards and ad-hoc analysis, even under sustained load.

Built for high query concurrency

Supports hundreds to thousands of simultaneous users without query queuing or performance degradation.

High Throughput Analytics

Handles large scans, joins, and aggregations across lakehouse storage while maintaining consistent performance.

SQL meets AI, right in your lakehouse

Query structured and unstructured data with cosine similarity. No vector DBs. Just pure vector search.

Sub-second streaming of data in your lake

Stream directly to your lakehouse, query with sub-second latency- query with SQL/Python. No Flink, no ETL, no learning curve.

Enterprise-grade security and governance

Row/column-level control, IAM integration, and audit-ready logs. SOC 2, ISO, HIPAA, and GDPR—secure by design, with no slowdown.

Run your most resource-intensive SQL and AI workloads

Get predictable SLAs, instant query responses, and radically lower compute costs—all with no query rewrites or app changes.

Packaged Analytics

Deliver embedded, multi-tenant analytics seamlessly within your SaaS applications. Gain 10x faster performance at scale while reducing infrastructure costs by up to 60% and operational complexity.

Interactive Analytics

Enable real-time dashboards and dynamic data exploration at massive scale. Deliver sub-2-second response times for 1000+ QPS with consistent SLAs and UX and without any latency.

Ad-hoc Analytics

Run complex ad-hoc queries 10x faster across diverse data sources (object storage, OLAP, data streams, and more) from a unified engine. Achieve zero-failed SLAs due to poorly optimized queries and resource constraints.

Scheduled Analytics

Run frequent, high-volume scheduled analytics with 99.99% reliability for scheduled workflows—without downtime, data delays, or compute cost overruns, even with rapid refresh cycles. 

Real Time Ingest

Stream data into your lakehouse with sub-second latency. Skip Flink, ETL, and pipeline overhead. Query fresh events instantly using SQL or Python—no shuffle, no joins, no delay between ingestion and analysis.

Vector Search

Run semantic search on unstructured data using built-in cosine similarity. No vector DBs, no retrieval pipelines. Query text like structured rows with SQL—fast, scalable, and lakehouse-native for instant, AI-powered insights.

FAQs

What is a lakehouse SQL engine with a Kubernetes-native architecture?

A lakehouse SQL engine with Kubernetes-native architecture runs distributed SQL directly on cloud object storage, using containers for orchestration. It enables elastic scaling, workload isolation, and fine-grained resource management without fixed clusters.

Does GCP have a lakehouse?

GCP provides lakehouse building blocks like BigQuery, Dataproc, and Cloud Storage. However, it does not offer a fully Kubernetes-native, open lakehouse SQL engine with independent compute-storage separation out of the box.

What is a lakehouse vs data lake?

A data lake stores raw, unstructured data cheaply in object storage. A lakehouse adds transactional reliability, schema enforcement, and high-performance SQL analytics directly on that data without traditional data warehouses.

How does atomic and granular scaling improve lakehouse compute efficiency?

Atomic and granular scaling allocates compute at the query or operator level rather than scaling entire clusters. This reduces idle capacity, improves workload isolation, and maximizes utilization for unpredictable analytical workloads.

What is the difference between granular scaling and traditional cluster-based scaling?

Granular scaling adjusts resources per workload component dynamically. Traditional cluster-based scaling resizes entire clusters, often overprovisioning compute, increasing costs, and limiting elasticity for concurrent or variable analytical workloads.

Lakehouse Compute Engine for
Fast, Scalable SQL Analytics

10x

50%

zero

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