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Big Data Realtime Analytics

CeleraOne's Analytics Engine is a highly scalable statistical tool for Big Data that has been engineered using CeleraOne's Auto-Parallelization technology and cutting-edge In-Memory technology

High Performance Computing

CeleraOne's High Performance Computing architecture features efficient parallel processing for a reduction in infrastructure energy needs and data analyses in real-time

Auto-Parallelisation Technology

CeleraOne's proprietary Auto-Parallelization technology analyzes computational steps, detects automatically parallelization opportunities and distributes data and computations to a cluster of high performance servers

State-of-the-art Streaming

CeleraOne's streaming architecture processes massive amounts of data within fractions of a second and with the lowest possible latencies

Cloud Solutions

Analyze your big data in seconds without additional hardware or added IT workload. Outsource computationally intensive calculations and analyses to the cloud

CeleraOne Analytics Engine

Many data analytics vendors are attempting to retrofit streaming technology into their conventional batch processing infrastructure. In contrast, the CeleraOne Analytics Engine was designed and built from the ground up for the most demanding streaming workloads. We pair horizontal scalability with great single-node performance to significantly reduce our customer's costs of operation.

In a presentation given at the Hadoop Get-Together Berlin, April 18, 2012, Dr. Falk-Florian Henrich explains how CeleraOne applies advanced compiler technology to speed up event stream processing.

1

Event Stream Processing

Event data continuously flows into the system as it is generated by users, customers, or machines. Stream processing as a mode of operation is prerequisite for obtaining results in near real-time.

2

Full Sequence Analysis

In contrast to conventional CEP systems analysis of event streams is not limited to small data windows. Process long runs of event sequences and time series. Detect long-distance features and relationships.

3

Large Scale Graph Processing

While processing events from thousands of different sources in parallel automatically build a time-dependent communications graph that reveals interactions between event sources.

4

Auto-Parallelized Operations

Operations on data streams are specified in a simple SQL-like dialect. They are auto-parallelized and compiled to native code before execution. Queries execute as fast as hand-written C code.

5

In-memory Technology

CeleraOne's unique in-memory technology provides for compression of event data and speed. Avoiding disk I/O altogether yields significant performance gains and ensures very low latencies.

6

Intuitive Web Frontend

A simple-to-use web frontend enables data engineers to experiment with live data streams without having to code in complex programming languages or wasting time with unproductive batch processing procedures.