In today's market, companies undergoing digital transformation require secure and reliable systems to meet customer demands, handle macroeconomic uncertainty and navigate new disruptions. Digital resilience is key to providing an uninterrupted customer experience and adapting to new operating models. Companies that prioritize digital resilience can proactively prevent major issues, absorb shocks to digital systems and accelerate transformations.

Splunk is happy to announce improvements to Ingest Actions in Splunk Enterprise 9.1 and the most recent Splunk Cloud Platform releases which enhance its performance and usability. We’ve seen amazing growth in the usage of Ingest Actions over the last 12 months and remain committed to prioritizing customer requests to better serve cost-saving, auditing, compliance, security and role-based access control (RBAC) use cases.

The dynamic digital landscape brings risk and uncertainty for businesses in all industries. Cyber criminals use the advantages of time, money, and significant advances in technology to develop new tactics and techniques that help them evade overlooked vulnerabilities. Critical signals — like failures, errors, or outages — go unnoticed, leading to downtime and costing organizations hundreds of thousands of dollars.

Moore’s law states that the transistor count on a microchip doubles every two years. This observation was first made in 1965 by Intel co-founder Gordon Moore. While it may sound hyperbolic at first, his estimation has held true for over 50 years. Let’s talk about how, and why, it matters.

Adaptive thresholding in Splunk IT Service Intelligence (ITSI) is a useful capability for key performance indicator (KPI) monitoring. It allows thresholds to be updated at a regular interval depending on how the values of KPIs change over time. Adaptive thresholding has many parameters through which users can customize its behavior, including time policies, algorithms and thresholds.

IT event correlation is the process of analyzing IT infrastructure events and identifying relationships between them to detect problems and uncover their root cause. Using an event correlation tool can help organizations monitor their systems and applications more effectively while improving their uptime and performance.

“How can we turn our DevOps data into useful DevSecOps data? There is so much of it! It can come from anywhere! It’s in all sorts of different formats!” While these statements are all true, there are some similarities in different parts of the DevOps lifecycle that can be used to make sense of and unify all of that data. How can we bring order to this data chaos? The same way scientists study complex phenomena — by making a conceptual model of the data.

Dark data is all of the unused, unknown and untapped data across an organization. This data is generated as a result of users’ daily interactions online with countless devices and systems — everything from machine data to server log files to unstructured data derived from social media. Organizations may consider this data too old to provide value, incomplete or redundant, or limited by a format that can’t be accessed with available tools.

A data lake is a data repository for terabytes or petabytes of raw data stored in its original format. The data can originate from a variety of data sources: IoT and sensor data, a simple file, or a binary large object (BLOB) such as a video, audio, image or multimedia file. Any manipulation of the data — to put it into a data pipeline and make it usable — is done when the data is extracted from the data lake.

Data pipelines are the foundational support to any business analytics project. They are growing more critical than ever as companies are leaning on their insights to drive their business: 54% of enterprises said it was vital to their future business strategies. Data pipelines play a crucial role as they perform calculations and transformations used by analysts, data scientists, and business intelligence teams.