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HPE Ezmeral Software and Bosch Digital Twin Industries empower industry at the edge

Discover how HPE Ezmeral Unified Analytics Software enabled Bosch Digital Twin Industries to bring their Integrated Asset Performance Management solution directly at the point where the data is created โ€” at the edge.

HPE-Software-Bosch-Digital-Twin.pngBosch Digital Twin Industries' Integreted Asset Performance Management (IAPM) solution provides predictive and prescriptive insights informed by physics and enhanced by AI for a range of industrial companies from manufacturing to mining.

For these industries, operating in often disconnected and/or rough environements raises challenges. But using Bosch's IAPM directly at the edge reduces latency and enables real-time analytics and simulations. Additionally, the IAPM solution reduces costs such as those incurred by data transfer while offering robust business continuity. 

Integration of Bosch Digital Twin Industries' Integrated Asset Performance Management with HPE Ezmeral Unified Analytics Software  enables operations at the edge and in the cloud while reducing latency, optimizing costs, and giving customers the freedom to choose their cloud platform or none at all.

Integrated solution for optimizing edge environments 

Powered by an HPE hardware platform, HPE enables Bosch to offer their solution in scenarios with bad connectivity yet huge amounts data and demand for low latency. In some cases, the time the data needs to transfer from the production machine into the cloud and transfer the insight back to the edge is too much to act quickly in scenarios and avoid a sudden failure. In addition, data privacy concerns of customers who prefer on-premise over cloud can be addressed.

HPE Ezmeral Unified Analytics delivers an integrated and supported set of open-source tools to make it easier to develop and deploy applications faster. It provides a fully managed data science and analytics sandbox that addresses the full lifecycle of data engineering, exploratory data science, and classical business intelligence tasks using popular tools like Apache Spark, Apache Superset, Presto SQL, Apache Airflow, Kubeflow, MLflow and Feast. Bosch uses a subset of these tools, for example Apache Airflow,  in addition to their self-developed applications to realize the Digital Twin at the Edge. HPE Ezmeral Unified Analytics provides the platform to host these self-developed applications in addition of the integrated toolset and with these it enables the data cleaning, the AI model training as well as deployment and re-training of the IAPM solution.

HPE and Bosch Digital Twin Industries offer the new solution in different standard packages in "t-shirt sizes," allowing customers to easily scale up to a larger option. The new edge-to-cloud solution is available now and can be purchased from HPE and Bosch Digital Twin Industries. 

How does your data science environment look today? Do your data scientists have access to the tools they require? Do they have on platform to access all these tools? Can they easily work together with data engineers? Do you have a standardized architecture or a zoo of different tools in different versions? Does your choice limit you or do you have the flexibility to integrate your own tools?

Simplify your data engineering and data science environment today with the toolbox HPE Ezmeral Unified Analytics provides. 

Ready to get started? 

Visit our webpage to learn more about HPE Ezmeral Unified Analytics and how to simplify your own data science environment. 

Watch the following session to explore the Twin in a Box solution.

Interested in seeing a live demo? Contact us


Steinhauser_Isabelle-cropped.jpgMeet Isabelle Steinhauser, Solution Engineer, HPE Ezmeral Unified Analytics

Isabelle is a customer-centric solution engineer, solving customer problems everyday. She brings a wealth of technical expertise in fields such as AI, IoT, Data Platform and Kubernetes. Her experience spans various industries, with a particular focus on automotive and manufacturing. Connect with her on LinkedIn.

 

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