Splunk’s annual gathering, this year called .conf 2015, in late September hosted almost 4,000 Splunk customers, partners and employees. It is one of the fastest-growing user conferences in the technology industry. The area dedicated to Splunk partners has grown from a handful of booths a few years ago to a vast showroom floor many times larger. While the conference’s main announcement was the release of Splunk Enterprise 6.3, its flagship platform, the progress the company is making in the related areas of machine learning and the Internet of Things (IoT) most caught my attention.
Topics: Big Data, Predictive Analytics, Machine Learning, IT Analytics & Performance, Operational Performance, Plunk, Analytics, Business Analytics, Business Intelligence, Business Performance, Cloud, Information Management, Internet of Things, Operational Intelligence, Data, Information Optimization, industrial internet, private cloud
The concept and implementation of what is called big data are no longer new, and many organizations, especially larger ones, view it as a way to manage and understand the flood of data they receive. Our benchmark research on big data analytics shows that business intelligence (BI) is the most common type of system to which organizations deliver big data. However, BI systems aren’t a good fit for analyzing big data. They were built to provide interactive analysis of structured data sources using Structured Query Language (SQL). Big data includes large volumes of data that does not fit into rows and columns, such as sensor data, text data and Web log data. Such data must be transformed and modeled before it can fit into paradigms such as SQL.
Topics: Big Data, Predictive Analytics, Software as a Service, IT Analytics & Performance, Operational Performance, Analytics, Business Analytics, Business Intelligence, Business Performance, Cloud, Information Management, Operational Intelligence, Data, Information Optimization, hybrid cloud, infrastructure as a service, private cloud, public cloud