Systems | Development | Analytics | API | Testing

French multinational automotive manufacturer Renault Group has been investing in Industry 4.0 since the early days. A primary objective of this transformation has been to leverage manufacturing and industrial equipment data through a robust and scalable platform. Renault designed an industrial data acquisition layer and connected it to Google Cloud, using optimized big data products and services that together form Renault's Industrial Data Platform.

Last week, we shared information on BigQuery APIs and how to use them, along with another blog on workload management best practices. This blog focuses on effectively monitoring BigQuery usage and related metrics to operationalize workload management we discussed so far.

In our modern world, accelerating the process of extracting insights from data is a complex challenge. Exacerbating this task are colossal data volumes, the expansion and use of multiple cloud platforms, and the increasing demands for self-service in a way that maintains compliance. Enterprises attempting to tackle the problem encounter various forms of friction everywhere they turn.

During the process of turning data into insights, the most compelling data often comes with an added responsibility—the need to protect the people whose lives are caught up in that data. Plenty of data sets include sensitive information, and it’s the duty of every organization, down to each individual, to ensure that sensitive information is handled appropriately.