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Splunk Enterprise 7.2 Released

Splunk Enterprise 7.2 is now generally available.

Through a series of innovations in machine learning, performance and scale, new versions of Splunk Enterprise and Splunk Cloud make it easier to ask questions, take precise actions and drive meaningful business outcomes with access to data no matter where it lives.

“We are in the midst of the data revolution, and these product updates ensure the Splunk platform evolves as our world does to deliver business outcomes no matter the organization, team or dataset,” said Doug Merritt, President and CEO, Splunk. “There are two kinds of companies today: those who only record events with data, and those who make things happen with data. These advancements in machine learning, performance and investigation to the Splunk Platform help our customers take fast action, and our product roadmap will shift mindsets about what is possible to achieve.”

Splunk Cloud and Splunk Enterprise can scale to manage trillions of events in near real-time while maintaining performance and controlling costs. New capabilities include:

- Splunk SmartStore: Helps maximize data management flexibility while maintaining search performance and allowing compute and storage tiers to be independently scaled based on business demands. SmartStore automatically evaluates users’ data access patterns to determine which data needs to be accessible for real-time analytics and which data should reside in lower cost, long-term storage.

- Workload Management: Enables users to prioritize the allocation of compute and memory resources used by the Splunk Platform on searches and alerts to ensure users’ most critical analytics are completed first.

- Splunk on Docker: Splunk Support now covers Splunk Enterprise 7.2 deployments in Docker containers, enabling customers to quickly deploy and scale Splunk based on their organizations’ demands.

- Dynamic Data: Active Archive, the latest release in the Dynamic Data service series, helps Splunk Cloud customers to meet regulatory and compliance requirements by retaining less frequently accessed data and searching on this data with Splunk Cloud.

Given Splunk’s expansive ecosystem of technology partnerships and integrations, Splunk Cloud and Splunk Enterprise amplify the various investments customers make across technologies and data sources. Now, customers can move any data to and from the Splunk Platform regardless of its format, state or location. New capabilities include:

- Guided Data Onboarding, a new graphical user interface helping customers move data into Splunk Cloud or Splunk Enterprise and guiding them through the best onboarding methodology based on their specific architecture.

- Logs to Metrics helps configure and convert log events to metrics, enabling users to take advantage of breakthrough performance when monitoring and alerting on metrics with the Splunk Platform.

- Splunk Community for Machine Learning Toolkit (MLTK) Algorithms on GitHub amplifies MLTK customers’ creations and algorithms by allowing them to share, shape and build on GitHub community contributions.

- Splunk MLTK Container for TensorFlow extends the value of Splunk MLTK with additional contributions and functionality provided by TensorFlow, the popular open source library for high-performance numerical computation.

- Splunk MLTK Connector for Apache Spark taps into a vast and scalable machine learning library, MLib.

Splunk Cloud and Splunk Enterprise empower a broader set of roles within an organization to investigate their data, making actionable insights and impactful outcomes more widely available to all areas of the business. New capabilities include:

- Metrics Workspace enables users to monitor and analyze metrics data in an efficient, intuitive user interface.

- Health Report allows Splunk administrators to quickly understand the overall health status of their Splunk environments.

- Dark Mode heightens visual contrast within Splunk dashboards; perfect for SOC and NOC environments.

Splunk Next was also introduced, a continually evolving series of visionary technologies that brings the power of Splunk to more data sources and more people no matter where, when or how they access that data to deliver limitless insights. These innovations, which are made available through ongoing Beta programs, will enable customers to:

- Evaluate, transform and perform analytics on data in motion through Splunk Data Stream Processor.

- Achieve search at massive-scale, analyzing trillions of events at millisecond speeds with federated search across multiple Splunk deployments through Splunk Data Fabric Search.

- Interact with Splunk products from a mobile experience via Splunk Mobile and Splunk Cloud Gateway.

- Use augmented reality (AR) to interact with and take action from data through features such as QR codes, scanning for dashboards, UPC scanning and near-field communications New Data sources.

- See all business process flows, including the entire customer journey, to enable business users to easily see trends and make smarter decisions with Splunk Business Flow.

- Ask questions of Splunk using voice and text and receive immediate responses with natural language.

- Build next-generation data-rich apps and access a full suite of sample code, cloud-native services and more with Splunk Developer Cloud.

“Splunk is building on our strong heritage to evolve the platform for the future,” said Tully. “Our product vision is aimed at opening the aperture to bring Splunk everywhere, for everyone, and to help our customers pattern match everything with artificial intelligence and machine learning infused across the entire product portfolio. And we’re doing this with streaming data, data at rest, data from any source and on whatever kind of device you want to use to take action.”

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For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

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Splunk Enterprise 7.2 Released

Splunk Enterprise 7.2 is now generally available.

Through a series of innovations in machine learning, performance and scale, new versions of Splunk Enterprise and Splunk Cloud make it easier to ask questions, take precise actions and drive meaningful business outcomes with access to data no matter where it lives.

“We are in the midst of the data revolution, and these product updates ensure the Splunk platform evolves as our world does to deliver business outcomes no matter the organization, team or dataset,” said Doug Merritt, President and CEO, Splunk. “There are two kinds of companies today: those who only record events with data, and those who make things happen with data. These advancements in machine learning, performance and investigation to the Splunk Platform help our customers take fast action, and our product roadmap will shift mindsets about what is possible to achieve.”

Splunk Cloud and Splunk Enterprise can scale to manage trillions of events in near real-time while maintaining performance and controlling costs. New capabilities include:

- Splunk SmartStore: Helps maximize data management flexibility while maintaining search performance and allowing compute and storage tiers to be independently scaled based on business demands. SmartStore automatically evaluates users’ data access patterns to determine which data needs to be accessible for real-time analytics and which data should reside in lower cost, long-term storage.

- Workload Management: Enables users to prioritize the allocation of compute and memory resources used by the Splunk Platform on searches and alerts to ensure users’ most critical analytics are completed first.

- Splunk on Docker: Splunk Support now covers Splunk Enterprise 7.2 deployments in Docker containers, enabling customers to quickly deploy and scale Splunk based on their organizations’ demands.

- Dynamic Data: Active Archive, the latest release in the Dynamic Data service series, helps Splunk Cloud customers to meet regulatory and compliance requirements by retaining less frequently accessed data and searching on this data with Splunk Cloud.

Given Splunk’s expansive ecosystem of technology partnerships and integrations, Splunk Cloud and Splunk Enterprise amplify the various investments customers make across technologies and data sources. Now, customers can move any data to and from the Splunk Platform regardless of its format, state or location. New capabilities include:

- Guided Data Onboarding, a new graphical user interface helping customers move data into Splunk Cloud or Splunk Enterprise and guiding them through the best onboarding methodology based on their specific architecture.

- Logs to Metrics helps configure and convert log events to metrics, enabling users to take advantage of breakthrough performance when monitoring and alerting on metrics with the Splunk Platform.

- Splunk Community for Machine Learning Toolkit (MLTK) Algorithms on GitHub amplifies MLTK customers’ creations and algorithms by allowing them to share, shape and build on GitHub community contributions.

- Splunk MLTK Container for TensorFlow extends the value of Splunk MLTK with additional contributions and functionality provided by TensorFlow, the popular open source library for high-performance numerical computation.

- Splunk MLTK Connector for Apache Spark taps into a vast and scalable machine learning library, MLib.

Splunk Cloud and Splunk Enterprise empower a broader set of roles within an organization to investigate their data, making actionable insights and impactful outcomes more widely available to all areas of the business. New capabilities include:

- Metrics Workspace enables users to monitor and analyze metrics data in an efficient, intuitive user interface.

- Health Report allows Splunk administrators to quickly understand the overall health status of their Splunk environments.

- Dark Mode heightens visual contrast within Splunk dashboards; perfect for SOC and NOC environments.

Splunk Next was also introduced, a continually evolving series of visionary technologies that brings the power of Splunk to more data sources and more people no matter where, when or how they access that data to deliver limitless insights. These innovations, which are made available through ongoing Beta programs, will enable customers to:

- Evaluate, transform and perform analytics on data in motion through Splunk Data Stream Processor.

- Achieve search at massive-scale, analyzing trillions of events at millisecond speeds with federated search across multiple Splunk deployments through Splunk Data Fabric Search.

- Interact with Splunk products from a mobile experience via Splunk Mobile and Splunk Cloud Gateway.

- Use augmented reality (AR) to interact with and take action from data through features such as QR codes, scanning for dashboards, UPC scanning and near-field communications New Data sources.

- See all business process flows, including the entire customer journey, to enable business users to easily see trends and make smarter decisions with Splunk Business Flow.

- Ask questions of Splunk using voice and text and receive immediate responses with natural language.

- Build next-generation data-rich apps and access a full suite of sample code, cloud-native services and more with Splunk Developer Cloud.

“Splunk is building on our strong heritage to evolve the platform for the future,” said Tully. “Our product vision is aimed at opening the aperture to bring Splunk everywhere, for everyone, and to help our customers pattern match everything with artificial intelligence and machine learning infused across the entire product portfolio. And we’re doing this with streaming data, data at rest, data from any source and on whatever kind of device you want to use to take action.”

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...