Although typically thought of as an artifact of legacy computing, batch processes remain vital to today’s real-time enterprises. Behind the real time systems that power the real time enterprise, such as customer order fulfillment, account management, supply chain scheduling and optimization, or financial trading systems, are regularly-updated back office business systems. Over the years, batch technology has evolved from script-based automation to rules or policy-driven workload automation.
All things that can be automated will be automated. Beyond the careful choreography of sophisticated workflow, managing myriad simultaneous requests puts unprecedented demand on the software that must schedule and perpetually deliver against well-defined service levels, in real time. Gone are the days of simple batch processing. Welcome to the oh-so-very-flat world that is embracing service-oriented architectures, grid architectures, expansive ecosystems, and end-to-end processing. Workload scheduling has got to grow up.
Managing Hadoop batch processing may consume a significant portion of application developersí time and effort, which drives up application development times and costs. This paper from BMC discusses the obstacles IT organizations face in developing and managing Hadoop jobs and workflows and how a workload automation solution can remove these barriers.
IT groups must support many applications and servers across multiple platforms that frequently operate independently of each other. However, coordinating job scheduling across all these applications and networks is often required to optimize resource utilization. The traditional approach of applying more staff, toolkits, and rudimentary scheduling software to cobble together automated batch processing solutions becomes cost-prohibitive, inefficient, and error-prone, as the number of moving parts increases and the environment becomes more heterogeneous.