As their mastery of big data evolves, enterprises are getting better at using the Hadoop platform for batch processing in analytics applications. This capability is helping business leaders gain rear-view insights into business trends, which is good, however, not good enough. For customers to know and understand what happened one week ago is a must, however, the business now needs to know what happened 1 hour ago, one minute ago, and often times within a split second in order to gain the necessary insights to ensure their competitive advantage.\nTo compete effectively in a world that is increasingly driven by big data, enterprises now need to get better at using data in real-time analytics applications to understand what is happening now and what is likely to happen in the days and weeks ahead. Customers need to utilize a core set of big data tools to be able to stream, ingest, process, transform, and analyze data at real-time speed. This evolutionary shift requires advanced tools and technologies that enable organizations to process and analyze data as it flows into the enterprise.\nFor enterprises that are on this path to real-time data analytics, there is good news on the technology front in the form of various news technology stacks. My colleague Kris Applegate covered some of these stacks in a recent blog post. In this conversation, I will zero in on one of these new stacks: SMACK, an acronym based on its five components.\nThis big data and analytics toolchain brings together key open source technologies that work together to accelerate the data pipeline\u2014from processing to analysis:\n\nSpark for large-scale data processing\nMesos for orchestrating cluster resource management\nAkka for a toolkit for data-heavy applications\nCassandra for a storage engine\nKafka for event processing\n\nCollectively, these technologies in the SMACK stack work together to leverage distributed low-latency tools to process data at high speeds.\nThe use cases for high-speed processing via the SMACK stack are all over the map\u2014from fraud detection and recommendation engines to predictive analytics and supply chain optimization. For illustrative purposes, let\u2019s look at a straightforward use case from the world of manufacturing and the Internet of Things (IoT).\nIn this use case, a manufacturer captures enormous amounts of data from devices on the manufacturing floor using an edge device. Light weight analytics can be run at the edge in order to filter and aggregate the data to the core data center for in-depth analysis. The manufacturer can put the SMACK stack to work to analyze this IoT data in real time to identify performance trends that indicate when devices are heading toward failure. With these immediate insights, the manufacturer can work proactively to address the equipment issues before they bring down the manufacturing line\u2014at a potentially huge cost to the business.\n\nThe key takeaway here is the importance of fast processing of big data. In many cases, we lose business value if we tuck data away for analysis at a later point in time. To gain the full value of data, we increasingly need to analyze it in real time. And when it comes to this need, we require combinations of technologies like those in the SMACK stack.\nAt Dell, we are working actively to help organizations put these new technologies to work in open source Hadoop deployments. You can learn more at Dell.com\/Hadoop.\nArmando Acosta is the Hadoop planning and product manager at Dell.\n\u00a92016 Dell Inc. All rights reserved. Dell, the DELL logo, the DELL badge and PowerEdge are trademarks of Dell Inc. Other trademarks and trade names may be used in this document to refer to either the entities claiming the marks and names or their products. Dell disclaims proprietary interest in the marks and names of others.\nIntel and the Intel logo are trademarks of Intel Corporation in the U.S. and\/or other countries.