Powering enterprise deployments worldwide
Performance backed by benchmarks
Speed up iterative queries
Fastest class
5.29x
very workload class wins: iterative/interactive queries average 5.29x faster, data mining 4.57x, reporting 3.95x, and ad-hoc 3.16x faster than CPU-only, with Apache Arrow consistently adding another 20 to 25% on top of GPU-only acceleration. Performance claims are based on Dell and NVIDIA internal testing (TPC-DS SF1000, 99 queries, all-flash Dell ObjectScale Gen3 EXF900) using default, out-of-the-box configurations with no performance tuning applied to the engine or infrastructure. NVIDIA has reviewed and approved the claims as representative of current, unoptimized performance. Actual performance may vary based on system configuration, workload, and other factors.
Lower the cost of analytics
Up to
53%
Cloud Data Warehouse vs. Cloud Data Lakehouse: A Snowflake vs. Starburst TCO and Performance Comparison, published by GigaOm
Speed up vector indexing
Up to
12x
Elasticsearch GPU: vector indexing acceleration with NVIDIA - Elasticsearch Labs
Scale batch data mining
Peak
20.4x
GPU + Arrow delivered a 3.9x average speedup across all 99 TPC-DS queries versus the CPU-only AIDP Data Processing Engine (DPE) configuration, or 4.24x when averaged evenly across the four workload classes. Performance claims are based on Dell and NVIDIA internal testing (TPC-DS SF1000, 99 queries, all-flash Dell ObjectScale Gen3 EXF900) using default, out-of-the-box configurations with no performance tuning applied to the engine or infrastructure. NVIDIA has reviewed and approved the claims as representative of current, unoptimized performance. Actual performance may vary based on system configuration, workload, and other factors.
From raw data to consumable data products
Dell Data Engines unify data preparation and access on Dell AI storage - ingesting data, transforming it and delivering consumable, AI-ready data products.
Purpose-built engines for processing, analytics and search
Unlike proprietary engines that limit flexibility. Dell Data Engines are built with open, cutting-edge tools to handle the complexity of modern data - integrated for data engineers and architects.
Open, federated and built on trusted storage
Three choices that separate Dell Data Engines from proprietary, lock-in analytics stacks
Resources
Questions
What are Dell Data Engines?
Dell Data Engines are the data-processing layer of the Dell AI Data Platform with NVIDIA, a set of open, modular engines for search, analytics, and large-scale data processing. The engines query, search, enrich, and transform structured, semi-structured, and unstructured enterprise data so the data is ready for analytics, business intelligence, and AI workloads like retrieval-augmented generation (RAG) and agentic retrieval. Only adopt the engines you need, run on your existing Dell storage, without moving data into a proprietary platform.
What is the Dell Data Analytics Engine, and how does Starburst fit in?
The Dell Data Analytics Engine, powered by Starburst, is a federated query and analytics engine that lets you analyse data across many sources—such as data lakes, warehouses, object stores, and databases—without first copying data into one location. The engine uses open table formats such as Apache Iceberg and runs SQL analytics directly where your data lives, so teams get faster insight with less data duplication and lower analytics cost.
Can I run analytics where my data already lives?
Yes. The Dell Data Analytics Engine federates queries across distributed sources, so you can analyse data in place instead of building fragile extract, transform, load (ETL) pipelines to move data into a central warehouse. This reduces copies, cuts storage and pipeline costs, and shortens time to insight while keeping data under existing governance and access controls.
How do Dell Data Engines support vector search and RAG?
The Dell Data Search Engine, built on Elasticsearch and accelerated by NVIDIA cuVS, generates embeddings and builds vector indexes so enterprise data can be retrieved semantically for RAG and agentic AI. GPU acceleration delivers up to 12 times faster vector indexing throughput, keeping indexes fresh as data changes so retrieval stays fast, relevant, and grounded in current enterprise context.
Can Dell Data Engines search both structured and unstructured data?
Yes. The engines are designed for structured, semi-structured, and unstructured data, including documents, images, video, logs, records, and other enterprise content. Search combines keyword, semantic, and hybrid retrieval across these data types, so users and AI applications can find the right information regardless of where data lives or what that data takes.
How do Dell Data Engines prepare and transform data at scale?
The Dell Data Processing Engine, built on Apache Spark, handles large-scale ingestion, cleansing, transformation, and enrichment. The engine prepares raw enterprise data (batch or streaming) into clean, structured, AI-ready datasets. Teams can apply GPU acceleration to speed data preparation ahead of training, indexing, and inference so pipelines keep pace with changing data.
What do I actually deploy to run Dell Data Engines?
Dell Data Engines run on a validated foundation such as the Dell Data Analytics Engine appliance (DDAE660) together with Dell storage—PowerScale for scale-out file and ObjectScale for S3 object—and NVIDIA acceleration, including the first validated NVIDIA Blackwell-powered infrastructure for agentic data processing. Intelligent routing lets AI agents place and process each data task in real time across the whole system—CPU, GPU, memory and storage—not just the GPU, so agentic pipelines stay fast as they scale. Start with a single engine for a specific problem and expand across search, analytics, and processing as your workloads grow, without re-architecting.
How do Dell Data Engines help lower analytics and storage cost?
By querying where data lives, the engines reduce redundant copies and ETL overhead, and open table formats avoid proprietary lock-in. Combined with cost-efficient Dell storage, customers can significantly lower analytics and object-storage costs while improving query performance, getting more insight per dollar instead of scaling cost linearly with data.
How do Dell Data Engines fit with the rest of the Dell AI Data Platform?
Dell Data Engines are the search, analytics, and processing layer of the Dell AI Data Platform with NVIDIA. They work alongside Dell Data Orchestration (which automates the end-to-end pipeline) and Dell Storage for AI (the AI-ready storage foundation). Together they take enterprise data from ingest to AI outcome. Because the platform is modular, you can start with Dell Data Engines alone and add the other layers when needed.
Do Dell Data Engines support knowledge graphs?
Yes. Dell infrastructure powers the semantic layer behind NVIDIA AI-Q, so enterprises can generate organisation-specific knowledge graphs at scale with production-grade governance. Instead of scattered records across file, object and database sources, teams get unified meaning across every data surface - giving agents and RAG applications relationships and context, not just matching passages.