To protect the security market from threats, a financial monitoring organization needed an efficient system. They employed a machine learning process with an end-to-end process, cutting down the overall time to the market-required patterns, and increasing the usability of the feature library created.

A professional service provider wanted to better understand customer behavior, so in order to more effectively interact with data collected from customer experience, they integrated a big data tool into their system. After loading their data into the tool, they were able to analyze and understand customer patterns and were able to apply that knowledge to provide unique solutions.

They telecommunication company applied big data technology, comprising of a cloud service, an open source distributing streaming platform for data streaming and a storage system which was used as a data landing zone in order to gain insight into customer behavior. With this technology, they measured engagement and identified preferences of a particular user, making predictions of how users would behave.

Accommodating for projected growth in user engagement, a social network service provider wanted to deploy a more flexible and scalable system. They build a new query service to fetch aggregated values from Bigtable and processed end-user queries and deployed this query service in an engine cluster in the same region as the Bigtable instance to optimize for data access latency. This technology allowed them to easily configure existing data pipelines and build new features faster. The real-time data improved its reliability and accuracy as well.

An automotive manufacturer steered the didgital transformation of its factory and kept its machines running smooth and effectively. The company used BigData technology to achieve this by setting up sensors in the factory's machines that collected data about the machine's condition and cycle time. This data had been analyzed using analytic tools that process the data in real-time and notified workers if there was an issue. This helped reduce machine downtime, support rapid decision-making and improve production process.