Splunk's Data-to-Everything Platform is an all-encompassing suite of analytics tools that help enterprises to search, correlate, analyze, monitor and report on data in real time, available through its Splunk Cloud and Splunk Enterprise products. Today's slew of updates at the virtual event are all about expanding customer's multicloud capabilities, giving them new ways to set the right data strategy and improve access to the information their businesses generate, Splunk said. For example, the Splunk Data Stream Processor, an event streaming platform, is being updated with new capabilities that enable it to access, process and route real-time data from multiple cloud services, including Google LLC's Cloud Platform and Microsoft Corp.'s Azure Event Hub. In addition, event data now gets enriched with lookups and machine learning functionality that helps to minimize compute loads and provide more accuracy when searching through this data. Moreover, the Data-to-Everything Platform is getting a new Splunk Machine Learning Environment that will make it easy for companies to build and operationalize machine learning models by bringing data from multiple sources into a single platform.

AIops (artificial intelligence for IT operations) is one of those cool buzzwords that is actually part of another buzzword: cloudops (cloud operations), which is a part of the mother of all buzzwords: cloud computing. The concept of AIops and the tool category of AIops are really the maturation of operational tools in general. Most of those in the traditional ops tools space, at least in the past few years, bolted an AI engine onto a tool and called it AIops. Some purpose-built AIops tool startups out there are leveraging AI from the jump. All are worth a look as you select AIops tools; however, there are no mainstream brands.

AIops (artificial intelligence for IT operations) is one of those cool buzzwords that is actually part of another buzzword: cloudops (cloud operations), which is a part of the mother of all buzzwords: cloud computing. The concept of AIops and the tool category of AIops are really the maturation of operational tools in general. Most of those in the traditional ops tools space, at least in the past few years, bolted an AI engine onto a tool and called it AIops. Some purpose-built AIops tool startups out there are leveraging AI from the jump. All are worth a look as you select AIops tools; however, there are no mainstream brands.

These six enterprise tech trends defined 2019As we kick off another year, ZDNet's global team of editors has put together a list of five tech trends that will have a significant impact on the enterprise in 2020. The effects could be negative or positive, but will undoubtedly be substantial. Our panel of editors includes -- TechRepublic's Bill Detwiler, Larry Dignan, Chris Duckett, and Steve Ranger. Take a look back at best of the decade: ZDNet's top enterprise CEOs of the 2010s The PC was supposed to die a decade ago. Instead, this happened A decade of malware: Top botnets of the 2010s A decade of hacking: The most notable cyber-security events of the 2010s Device of the decade: Why did it take nine years for the iPad to get its own operating system?

Big Data and Analytics Hub spoke with IBM Distinguished Engineer John Thomas (@johnjaithomas) about some of the importance of tuning information architecture to make algorithms meet enterprise needs, as well as how machine learning can most effectively be applied in hybrid scenarios in 2018. The following is part two of a two part interview (read part one). BDAH: As we've discussed, the multicloud is a reality for many of our clients. What would be some of the challenge of doing data science in the multicloud and how do you overcome the challenges? JT: Whatever is the driving force, the multicloud is happening.

More and more cloud-computing experts are talking about "multicloud". The term refers to an architecture that spans multiple cloud environments in order to take advantage of different services, different levels of performance, security, or redundancy, or even different cloud vendors. But what sometimes gets lost in these discussions is that multicloud is not always public cloud. As machine learning (ML) continues to pervade enterprise environments, we need to understand how to make ML practical on multicloud -- including those architectures that span the firewall. Let's look at three possible scenarios.