Andrea Condorelli holds a Master of Science degree in Biomedical Engineering at Politecnico di Milano. From 2014 to 2016 he has been working as researcher in CONTENTWISE, a leading firm in Video On-Demand recommendation engine. After a year as principle researcher in a fintech startup, at the beginning of 2017 he joined the ICT Innovation team of Magneti Marelli, where he is in charge of all the activities related to Big Data and Advanced Analytics subject worldwide.
September 25, 2021 08:29 AM PT
In the Manufacturing industry, reliability and time to market are key factors to accomplish business goals. Nowadays, Analytics are more and more deployed to get insights’ from data and foster a data driven culture to achieve a greater effectiveness and efficiency within business operations.
In the Analytics domain, real challenges are often represented by data collection, such as the existence of heterogeneous and widespread data sources and the choice of ingestion technologies and strategies, the need to ensure a continuous data inflow and to release production-ready Analytics services to be integrated into in daily operations
In order to address those challenges, Magneti Marelli ICT Innovation team has adopted a structured approach starting from foundations, that is by building a distinctive Big Data Architecture, known as the Magneti Marelli Architecture (MARC). Differently from common Big Data architectures, which are developed on batch or streaming paradigms, MARC is an event and service oriented architecture with the flexibility to manage complex tasks running both in the DMZ plant, in the plant network and in Cloud. It combines traditional patterns for handling data, such as “Service Broker”, “Forwarder”, “Singleton”, “Wrapping”, “Store and Forward”, with best of breed technologies such as Databricks, Microsoft Azure Data Lake Store, Azure SQL, PowerBI and Azure Functions.
In this presentation MARC key components will be introduced, together with main integrated services. Additionally, it will be shown how mentioned routine issues in data management will be addressed and solved with the aid of MARC unique structure and related services: practical examples will be provided for incremental data ingestion, incremental data processing, hybrid Spark deployments and the usage of heterogeneous Application Servers. Finally, it will be clear how the adoption of a structured approach to the development of Big Data Architecture for data management has dramatically fostered the demand for Analytics services and their effective use by the business to accomplish manufacturing cost reduction.
Session hashtag: #SAISEnt4