At ExxonMobil, we’ve had hundreds of thousands of sensors collecting data at our refineries and chemical plants all over the world for decades. This data has always been critical to the operations and monitoring of our equipment and processes, but it is more valuable now than ever as we bring new life to our archived and current data at a global scale by leveraging a combination of tools in today’s big data ecosystem.
Regardless of the industry you are in, whether it be energy, manufacturing, transportation, telecom, healthcare, or financial services, only a fraction of the time-series data collected and stored in legacy systems is being utilized to its full potential to improve business performance.
In this session you’ll learn how to navigate some of the challenges associated with time-series data at a global scale, as well as pitfalls to avoid with real-world use cases with an emphasis on the following:
• Nifi: collection and centralization of data from legacy systems scattered all over the globe
• Spark: validation, aggregations, interpolations and more using in memory processing
• Hbase/Hive: partitioning, file formats, and storage strategies relevant to time-series data
• Consumption APIs: empower users to leverage/utilize the analytical tool of their choice through rich APIs.