New Versions of Splunk Enterprise, Splunk Cloud, Splunk ITSI, Splunk UBA and Splunk Machine Learning Toolkit Now Available
Splunk Inc., first in delivering “aha” moments from machine data, announced new and expanded artificial intelligence (AI) capabilities across its product portfolio. With the power of AI, Splunk customers can use Splunk solutions to help boost their profitability, performance and security. Splunk also expanded integration capabilities with open source software and cloud-native technologies as part of its ongoing commitment to provide a true, open machine data platform for customers. To learn more and experience Splunk® Enterprise and Splunk Cloud™, visit the Splunk website.
“Organizations frequently consume high amounts of staff time and resources to monitor, analyze and respond to IT operational alerts. Splunk’s new AI enhancements, including the ability to correlate metrics and activity data, enable customers to get answers from their machine data more efficiently,” said Tim Tully, chief technology officer, Splunk. “Our latest wave of innovation is intended to arm customers with the tools needed to translate AI into actionable intelligence. While AI and machine learning often seem like unattainable and expensive pipe dreams, Splunk Cloud and Splunk Enterprise now make it easier and more affordable to monitor, analyze and visualize machine data in real time.”
Splunk Platform Helps Accelerate Customers’ Digital Transformation Through AI and an Expansive and Open Ecosystem
Splunk Cloud and Splunk Enterprise 7.1 deliver AI through machine learning to help customers monitor, search and alert on the critical information organizations need to accelerate their business. These latest releases include an updated metrics engine to power customers’ ability to monitor and alert on numeric data points - from CPU speeds and available hard disk space in a complex IT environment, to temperature readings in Internet of Things (IoT) devices and sensors. The latest versions are also the only enterprise-class data analytics solutions that can ingest petabytes of data per day, as well as search, monitor and alert on that data in real time. With these enhancements, users are better positioned to make sense of their machine data to predict future IT, security and business outcomes.
Splunk Cloud offers customers maximum control and real-time access to their data. This Splunk Cloud release features Dynamic Data: Self-Storage, arming customers with the flexibility to move data from Splunk to their own Amazon S3 storage environment.
“At T-Mobile we’re all about putting customers first, and with Splunk Enterprise we can ensure our customers get the best experience possible when they’re activating a phone, making a call or paying a bill,” said Jonathan Silberlicht, senior director, network service management, T-Mobile. “For example, with the new Splunk Connect for Kafka, we are expanding our real-time analytics capabilities, in turn empowering our front line to make better informed decisions when serving customers. We plan to continue to rely on Splunk Enterprise, Splunk IT Service Intelligence and Splunk Machine Learning to help us scale at Un-carrier speed.”
To expand its open technology ecosystem, Splunk introduced new data integrations with open source software projects and cloud-native technologies including:
Splunk Connect for Kafka integrates the Splunk platform with Apache Kafka, a highly scalable and reliable method for handling real-time streaming data.
Splunk Connect for Kubernetes and Splunk Connect for Docker unify the Splunk platform with the leading solutions for automating deployment, scaling and management of containerized applications.
"Splunk Connect for Kafka will improve our investigation of web activity, performance and security use cases," said John Swanson, security incident response manager, GitHub. "We're now able to ingest large, near-real-time data streams and are consuming terabytes of logs from our Kafka cluster into Splunk Enterprise every day."
Splunk Machine Learning Toolkit Adds Experiment Management Interface and New Algorithms for Pattern Prediction
Splunk is also announcing a new Experiment Management Interface for its Machine Learning Toolkit (MLTK). This interface makes it easier to view, control, evaluate and monitor the status of machine learning experiments. The latest Splunk MLTK also includes new algorithms for identifying patterns and determining the best predictors for training machine learning models.
“Hyatt uses machine learning in Splunk Enterprise to predict when and where we should act fast or plan differently to best serve our customers,” said Cesar Mendoza, application development manager, strategic systems and innovation, Hyatt. “We used the free Splunk Machine Learning Toolkit to benchmark typical Wi-Fi usage from customers across hotel sites, and used that baseline to spot low traffic. We immediately contacted our wireless service provider to correct connection issues before our customers had to call us. We’re using artificial intelligence through Splunk to more proactively serve our customers in this way every day.”
Splunk IT Service Intelligence (ITSI) Predicts Outages and Service Health and Splunk User Behavior Analytics (UBA) Accelerates Threat Identification via Machine Learning
In the latest release of monitoring and analytics solution Splunk ITSI, customers can leverage AI to help predict imminent outages and how their service health could be impacted by these outages before they occur, reducing the risk of negative impact to end-customer experience and revenue. Splunk ITSI also applies machine learning to help reduce event noise and automatically identify the events that are most business critical, so that customers can resolve those first. Similarly, the Splunk UBA updates include new machine learning models and enhancements to existing models to help customers identify and address time-sensitive security problems and insider threats more quickly