Elastic Cloud Reservoirs in Industrial Analytics
DOI:
https://doi.org/10.5281/zenodo.20579630Keywords:
Cloud-Integrated Data Lake Architectures, Smart Factory Data Platforms, Exploratory Production Data Analytics, Industrial Data Lake Systems, Manufacturing Data Ingestion Pipelines, Predictive Maintenance Analytics, AI-Driven Manufacturing Intelligence, Production Anomaly Detection Systems, Industrial Machine Learning Platforms, Real-Time Manufacturing Analytics, Data Governance in Industrial Data Lakes, Manufacturing Data Access Control Systems, Cloud-Native Industrial Data Platforms, End-to-End Manufacturing Data Pipelines, Industrial Data Integration Frameworks.Abstract
Cloud-integrated data lake architectures that enable exploratory production data analytics offer an efficient and flexible approach to mining data for new patterns and insights that can drive improvement. Such discoveries can enhance production quality and performance, facilitate predictive maintenance, or even support decision-making automation through AI and machine learning. Concrete ideas for supporting production analytics in a smart factory environment are provided, along with a cloud-enabled data lake reference architecture for manufacturing environments. The core components, data ingestion and integration pipelines, storage strategies, and modeling considerations are considered in the context of exploratory production analytics use cases focused the mining of production data for anomalies and failure patterns. Together, these considerations can help practitioners realize new value from cloud-integrated data lake architectures and services. However, moving production data to the cloud introduces new challenges that must be faced to make the analytics-ready data lake a successful reality. Enabling real-time analytics requires consideration of end-to-end latency, data freshness, and supported analytics features. From a data governance perspective, novel approaches to policy definition, access control, and proof-of-compliance must be developed. Even with the right design, the sensitivity and business-critical nature of production data mean that its movement must be carefully monitored, including the prevention of AET loss and potential data inconsistency during sync processes. For mission-critical decision-making, the automated orchestration of the entire pipeline chain is also essential.
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