End to end solutions

In today’s world, the data would be streamed from the local network or edge devices to a cloud provider which is rented by a customer to perform the data execution. The Big Data software stack, in an application and hardware agnostic manner, will split the execution stream into multiple tasks and send them for processing on the nodes the customer has paid for. If the outcome does not match the strict three second business requirement, then the customer has three options:
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Scale-up (by upgrading processors at node level),
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Scale-up (by upgrading processors at node level),
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Manually implement code optimisations specific to the underlying hardware.

E2Data proposes an end-to-end solution for Big Data deployments that will fully exploit and advance the state-of-the- art in infrastructure services by delivering a performance increase while utilizing less cloud resources.

E2Data will provide a new Big Data paradigm, by combining state-of-the-art software components, in order to achieve maximum resource utilization for heterogeneous cloud deployments without affecting current programming norms (i.e. no code changes in the original source).

The evaluation will be conducted on both high-performing x86 and low-power ARM cluster architectures representing realistic execution scenarios of real-world deployments in four resource-demanding applications from the finance, health, green buildings, and security domains.

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About Us

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We are asked many questions in regards to our capabilities, we dont just talk the good stuff, let us explain how we resolve those critical problems that every data practitioner has to deal with.

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E2Data plenary meeting

E2Data plenary meeting

The 1st E2Data plenary meeting was successfully held in Athens, Greece, on Ma

E2Data Kickoff Meeting

E2Data Kickoff Meeting

The E2Data project meeting was hosted by the University of Manchester and hel

Big Data Value Meet-up

Big Data Value Meet-up

Juan Fumero (UNIMAN) and Ioannis Komnios (EXUS) represented E2Data at the Big

This project has received funding from the European Union's Horizon H2020 research and innovation programme under grant agreement No 780245.