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AMAzoN Sagemaker COMPATIBILITY
Making your Amazon SageMaker Better:
New Capabilities. Extended Reach. Amplified ROl.
Keep what you love about Amazon SageMaker—add e6data where it matters. Save $1.5M–$10M (60% TCO), get 10x more queries, unlock real-time capabilities, extend governance to multi-region & cloud, hybrid—all SQL & Iceberg compatible.
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Single compute engine handling all SQL and AI workloads
Amazon SageMaker’s all-in-one platform makes it simple to ingest, transform, and query data from a single platform. Yet when workloads surge, users demand faster turnaround times, stricter SLAs, higher concurrency, and lower costs—needs a single cluster can’t always meet without extra operational overhead.
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e6data’s distributed k8s-native engine — built on atomic architecture
Keep what you love about Amazon SageMaker—add e6data’s engine into bottleneck workloads. Each workload (ingest, ETL, query, AI) —scale instantly, save $1.5M-10M (≤60 % TCO), run 10x more queries, get real-time streaming, extend catalog & governance securely to multi-cloud—no data movement or SQL rewrites.
USE CASES
Ingest, ETL, Query your most complex SQL and AI workloads
Add e6data to solve pressing pain points while staying on Amazon SageMaker, without any migration.
Dashboards that scale up to 1000 QPS
Power real-time dashboards with sub-2-second response times at high concurrency with consistent SLAs.
SQL meet AI in your Amazon SageMaker
Run semantic search on unstructured data using built-in cosine similarity in SQL. No vector DBs, no retrieval pipelines.
Deploy anywhere - cloud, on-prem, hybrid
The industry’s only Affinity and Locality-Aware Computing: 10x faster latency, 99% lower egress, 60% lower TCO.
Query on Apache Iceberg instantly
Query Iceberg on your Amazon SageMaker using our native-Iceberg support, extended across all table formats.
Stream and query your events data in Amazon SageMaker under one minute
Materialize Kafka topics to Iceberg tables in your Amazon S3, and query them in under 60s at object-store cost, without any migration, and ETL.
Performance at production scale: run e6data in Amazon SageMaker
Benchmarks
Up to 10x faster querying, sub-second p99 latencies
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Estimate your monthly cost
Up to 60% lower costs, granular, per vCPU scaling
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Deploy across multi-cloud, multi-region, and on-prem
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FAQs
How does e6data speed up Amazon SageMaker and cut costs?
Our query engine is kubernetes-native, disaggregated, and decentralised with stateless services. Our architecture is atomically-scalable, i.e., it scales granularly per vCPU increments. Hence, you only pay for cores consumed.
I use both Amazon SageMaker and Databricks. Can I adopt e6data alongside both of them? Do I have to move out?
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 do I secure the deployment (VPC, IAM, encryption)?
e6data is launched in your VPC with private subnets, inherits SageMaker IAM roles, and supports KMS-encrypted volumes plus TLS in-flight. Fine-grained table, column, and row-level access can be enforced via AWS Lake Formation or your existing data-catalog policies.
Which workloads will gain the most from e6data?
The largest gains come from latency-sensitive dashboards (p99 < 2 s), high-QPS SQL workloads, multi-TB batch analytics, high-cardinality joins that struggle with single-cluster bottlenecks. Customers typically see 5–10x faster queries and up to 60% lower compute cost on these patterns.
How is e6data licensed and how does it show up on my Amazon SageMaker bill?
e6data is priced per usage of cores and is invoiced directly by e6data, separate from your Amazon SageMaker charges.
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Available at
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