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It is more difficult to obtain timely analyses of their enormous and fast-growing data volumes. Organizations struggle to obtain maximum performance and scalability from Splunk without understanding the workload, and then configuring Splunk infrastructure for that workload.
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Still other workloads have a more balanced amount of data indexing and search queries. Some workloads require more indexing of data, while other workloads focus on running searches.
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Efficiently deploying Splunk Enterprise requires understanding the characteristics of a Splunk workload, and how the components of Splunk work together. Monitor it as it streams through the IT, security, and IoT systems.īut as with any technology, efficiency is paramount to maximizing total cost of ownership and return on investment.Investigate machine and event data in its raw form.Splunk Enterprise can help solve these challenges by enabling enterprises to: But machine data is complex, and enterprises face significant challenges in converting it into timely insights. Monitoring and analyzing machine data can help organizations solve many problems, like analyzing Web traffic, understanding customer behavior, streamlining financial analysis, improving customer experience, and more. Splunk data ingestion, processing, and results Efficiently exploit the power of machine dataĪpplications, operating systems, networks, security software, client devices, the Internet of Things (IoT), and other technologies generate a significant amount of event and machine data.
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These configurations are designed for various Splunk workloads, using Dell EMC servers and storage with the latest Intel technology.įigure 1 shows that Splunk Enterprise ingests and processes data from many different sources to produce actionable insights, maximizing the value of event and machine data.įigure 1. To simplify this often-complex process, Dell EMC, Intel, and Splunk worked together to design a reference architecture with several different configurations. This knowledge equates to low search runtimes, high data ingestion rates, and high numbers of concurrent searches.