At its .conf22 event, Splunk today announced it is making it easier to both onboard data and then manage it across hybrid IT environments via the Splunk Cloud Platform.
In addition, Splunk Enterprise is being extended to add support for Microsoft Azure with SmartStore for Azure to store cold data alongside existing support for Amazon Web Services (AWS) and Google Cloud Platform (GCP). There is also a developer edition of Splunk Cloud that enables developers to create and test their own applications, which is currently in preview.
Garth Fort, senior vice president and chief product officer for Splunk, said the overall goal is to make it easier to automatically tune the parameters used to store data in the Splunk observability platform. As more organizations embrace observability as a DevOps best practice, many of them are encountering challenges managing the large volumes of data they are now collecting in the form of logs, metrics and traces.
The Data Manager for Splunk Cloud Platform will initially be made available on AWS and Azure, with support for GCP to come later this summer. In effect, it provides IT teams with a control plane for data management that spans multiple clouds.
Ingest Actions, meanwhile, makes it possible to apply granular controls that enable IT teams to filter, mask and route data in motion within the Splunk Platform or to external AWS S3 cloud storage services.
Other capabilities Splunk unveiled at the conference include an expansion of Federated Search tools to enable a single pane of view across their entire Splunk ecosystem and a fully managed Splunk Assist service for Splunk Enterprise 9.0 that provides customers with deep insights into their security environment.
Finally, the company has enhanced Splunkbase, a framework for accessing curated collections of more than 2,500 applications and integrations provided by Splunk and its third-party partners.
As the pioneer of IT operations analytics, Splunk has a large footprint within enterprise IT environments. It is now looking to expand that base to become an observability platform provider. It’s still early days as far as the transition to observability is concerned, but the goal is to make it simpler for IT teams to query platforms and surface issues that could lead to an IT disruption before it happens.
It’s not clear how many organizations have the skills and expertise required to formulate those queries, but over time it should become more feasible to augment an IT staff with machine learning algorithms capable of discovering those issues.
In the meantime, IT organizations are collecting more data than ever about their IT environments. In theory, that data will provide the context needed to optimize application environments that get more complex with each passing day. The challenge, of course, then becomes finding a way to efficiently store and manage all that data both before and after it has been analyzed.
Regardless of the approach to observability, the need to go beyond simple monitoring of IT environments has never been more apparent.
