The public cloud has emerged as an ideal platform for big data, providing businesses with the resources and services they need on demand. Cloud services like Apache Spark or Hadoop integrate seamlessly with big data processing frameworks such as Apache Spark, allowing organizations to perform complex data analytics, machine learning, and data transformations. Cloud resources are elastic, enabling analytics to scale up and down according to workload, offering a flexible solution for data-driven insight.

Big data analytics has continued to be the change that many businesses need to revolutionize various aspects of their operations, including maintenance management. Thanks to the evolution of use of data, predictive maintenance strategies have become increasingly popular in recent years, with the main aim being to reduce and eliminate failures during production. With the help of the power of big data and related techniques, companies can now improve the transparency of system health conditions, boost the speed and accuracy of maintenance decision-making, and minimize costly downtime.

Data warehousing has become an important component in the era of big data technology. As data coming from different sources continues increasing, organizations require a centralized and consolidated repository to store and manage vast amounts of data from multiple sources. This is where data warehousing comes into play. By bringing together diverse data sets into a single, unified location, data warehousing enables organizations to access and analyze large volumes of data efficiently.