Built for querying high-concurrency, complex SQL & AI workloads in lakehouse
e6data’s decentralized, Kubernetes-native architecture granularly scales in 1-vCPU increments. Built for enterprises facing throttling, rationing, and vendor lock-in. Compatible with all table formats and catalogs.
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W/o e6data
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Query structured and unstructured data with cosine similarity. No vector DBs. Just pure vector search.
Auto-scaling that adapts to query load
Set min and max, we handle the rest. Executors scale with load with no latency spikes, no job failures, no manual tuning.
Guardrails to stop “bad” queries early
Set thresholds per cluster. Log, alert, or cancel in real time before bad queries waste compute.
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.
Head of Platform Engineering
B2B observability SaaS
“We’ve been looking to move our logs to S3 since the costs became super high. With e6data, it became possible faster as our p95 & p99 latencies were maintained. All our logs now ingest & query in S3. ”
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.
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
Which workloads does the e6data Query Engine accelerate?
The engine is purpose-built for high-concurrency SQL and AI workloads in a lakehouse, covering interactive dashboards, ad-hoc queries, packaged and scheduled analytics—without data movement or query rewrites.
Can I run e6data alongside Snowflake or Databricks?
We integrate with your existing data architecture—whether you’re using Amazon Sagemaker, Databricks, Snowflake, Trino, Athena, or any other engine—alongside your chosen catalog, governance framework, table format, and BI tools. You can deploy us anywhere: single or multi-cloud, multi-region, on-premises, or in a hybrid environment.
How does e6data scale to match demand?
Its decentralized, Kubernetes-native architecture scales executors in 1-vCPU steps (or per core). Auto-scaling adjusts between user-set min and max limits with no latency spikes, job failures, or manual tuning.
Does the platform guard against inefficient queries?
Yes. Per-cluster thresholds can log, alert, or cancel bad queries in real time, stopping wasteful jobs before they consume excess compute.
What security and compliance features are included?
Enterprise-grade controls include row- and column-level masking, IAM integration, audit-ready logs, and readiness for SOC 2, ISO, HIPAA, and GDPR, all delivered with no slowdown.
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