Storage Solutions for Sensor Data

SUMMARY

In manufacturing, managing vast amounts of data effectively is crucial for ensuring smooth operations and maintaining high-quality standards. But how can you optimize storage to balance cost, speed, and scalability?

This lesson explores various data storage strategies tailored to the unique needs of manufacturing environments. We’ll discuss how to choose between on-premises, cloud, or hybrid storage options to create the best solution based on your specific needs for cost, performance, and growth potential.

You’ll also learn best practices for sensor data management, such as selecting efficient storage formats, implementing data lifecycle management, and minimizing redundancy to ensure that sensor data is accessible and useful for quality control and anomaly detection.

Finally, we’ll dive into optimization techniques for data storage costs, including storage tiering, compression, and data partitioning, which can help manage high-volume sensor data more efficiently and cost-effectively. By the end of this lesson, you’ll be equipped with strategies to handle data storage challenges and make the most of your data while supporting robust quality control in manufacturing.

About The Author

Dilek Dustegor is a Professor of Computing Science at the University of Groningen in the Netherlands. She is interested in bridging the gaps between research, development and implementation using AI and automation. She is pursuing research about modeling, design and analysis of large scale / networked systems using Internet of Things (IoT) and Machine Learning (ML) techniques, with a special interest in smart city applications. She is a seasoned educator, and loves using the newest educational technologies for an enhanced learning experience.