Lakehouse Compute Engine: Query / ETL / Real-time Ingest

Agentic-AI-ready • Audit-ready

Engineered for today’s most demanding workloads. Architected for what’s next.

Speed. Simplicity. Cost Efficiency. For the constants you rely on: across today's complexity and tomorrow’s uncertainty. Proven at enterprise scale.

Works On All Data Platforms And Architectures ->

10x faster

on production workloads

$1M-$10M

3 year cost savings per use case

Audit-grade

architected for full control

Principles

Key factors influencing e6data’s architecture and design

The next 15 years (2025–2040) will look nothing like the past 15 years (2010–2025). Most mainstream engines we know and love—Spark, Trino, Snowflake, Databricks, etc. Trace their architectures back to the early 2010s, built on primitives from that era. As with most things, architecture defines the frontier of possibilities.

How it was

How it is / will be

How it was

Most datasets were GB to 10 TB. Only big consumer internet firms reached PB scale.

How it is / will be

10 TB – 1 PB is normal. Exabyte data and large vector stores are routine.

How it was

People hand-built ETL, set up clusters, and wrote queries.

How it is / will be

AI agents create pipelines, launch databases, and fire tuned queries on demand.

How it was

Base traffic stayed below 1 query per second; peaks were only 50 % higher.

How it is / will be

Steady 1 000 QPS with elastic bursts 10 × higher for AI training and inference.

How it was

Queries took tens of seconds to minutes, and dashboards read from day-old extracts.

How it is / will be

Answers come in under a second on live data only seconds behind source.

How it was

Human analysts wrote SQL; dashboards and reports issued most queries

How it is / will be

AI agents and autonomous apps launch most queries; humans focus on oversight

w/o e6data

w/ e6data

w/o e6data

w/ e6data

w/o e6data

w/ e6data

Trusted by Data Teams at

‍“We achieved 1,000 QPS concurrencies with p95 SLAs of < 2s on near real-time data & complex queries. Other industry leaders couldn’t meet this even at a far higher TCO.”

Chief Operating Officer

“We’ve been impressed with e6data’s performance, concurrency, and granular scalability on our resource-intensive workloads.” 

Head of Platform Engineering

Technology

Why is e6data 10x faster at 60% lower cost?

You size the cluster or virtual warehouse (base size) based on query volume, complexity, and concurrency, as well as your target response time (e.g., p95 latency). Choose the size that achieves optimal cluster utilization for the given load and SLA.

w/o e6data

Legacy Centralized, VM-centric architectures

Depend on a single coordinator node — creating bottlenecks, single points of failure, and expensive step-jump scaling. Even slight increases in workloads trigger large cost spikes and SLA misses.

w/ e6data

e6data's Decentralized, k8s native architecture

Scales granularly with stateless services, with scaling granularity down to 1 vCPU increments. Result: 10x faster queries, consistently met SLAs, and a predictable 60% lower TCO at petabyte-scale.

Comparison

Atomic vs Step-Jump Scaling: Cost & QPS Under Production Load

Line graph comparing legacy step-jump scaling with e6data’s atomic scaling across fluctuating query loads; cost labels show steep jumps for legacy ($25 → $100) versus granular increments for e6data ($15 → $74).

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

XS

Vs. legacy lakehouse engine

67.64%

Lower cost

TPC-DS

Delta

8QPS

Vs. legacy query engine

7.04x

Faster

TPC-DS

Iceberg

XS

Query type: logical

1.80x

Faster

TPC-DS

Delta

AWS

XS

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

XS

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

XS

Vs. legacy lakehouse engine

67.64%

Lower cost

TPC-DS

Delta

8QPS

Vs. legacy query engine

7.04x

Faster

TPC-DS

Iceberg

XS

Query type: logical

1.80x

Faster

TPC-DS

Delta

AWS

XS

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

XS

Learn more in docs

Use Cases

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.