Kloudfuse today updated its namesake observability platform to add support for continuous profiling and real user monitoring (RUM) along with additional analytics and artificial intelligence (AI) capabilities.
Company CEO Krishna Yadappanavar said version 3.0 of the Kloudfuse platform extends an ongoing effort to unify observability in a way that reduces the total cost of IT.
For example, the continuous profiling capability enables DevOps teams to identify how hidden performance bottlenecks impact specific lines of code, said Yadappanavar.
Additionally, a Prophet tool for anomaly detection and forecasting provides more accurate results by accounting for missing data, and seasonality to improve confidence bounds across both small and large datasets. A K-Lens visualization tool makes use of outlier detection techniques to analyze thousands of attributes in a way that accelerates debugging and incident resolution.
Kloudfuse has also added a log query language dubbed FuseQL, that enables multi-dimensional aggregations and filters, while a Facet Analytics tool leverages LogFingerprinting technology developed by Kloudfuse to enable advanced search, filtering, bookmarking and grouping options.
That log fingerprinting also enables Kloudfuse to ingest, process and analyze vast amounts of real-time observability data using its own observability data lake to normalize that data, said Yadappanavar. That capability is crucial because it enables Kloudfuse to reduce storage costs while providing the tools DevOps teams need to shape data as needed, he added.
A log archival and hydration capability, for example, enables DevOps teams to store logs in a compressed format within storage systems managed by IT teams. During both archival and hydration, however, those logs can be enriched with additional labels to enhance searchability and analysis.
At the same time, cardinality analysis offers real-time insights into incoming metrics, logs and traces, which helps reduce storage and processing costs. Metrics can also be aggregated to improve query performance and reduce mean time to resolution.
A central hub for tracking microservice ownership and who on an IT team is on call is also now being provided via a Service Catalog that also discovers active and inactive services, their dependencies and version changes.
Finally, Kloudfuse is now also adding support for Arm processors found in cloud services from Amazon Web Services (AWS) and Google Cloud Platform (GCP), along with support for virtual private clouds (VPCs) and support for role-based access controls (RBAC), single sign-on (SSO) and multi-key authentication for enhanced security.
Collectively, these capabilities unify observability across both front and backends of applications, said Yadappanavar.
It’s not clear to what degree IT organizations are embracing observability, however, a Techstrong Research survey finds 63% working for organizations that will be making additional investments in observability over the next two years, with 21% describing those investments as significant. Nealy half (48%) said they already practice observability regularly.
As application environments continue to become more complex, it’s becoming all but impossible to manage IT without relying on some type of observability capability that goes beyond simply monitoring a set of pre-defined metrics. The challenge, as always, is funding the initial acquisition of a platform that requires time to show a return on that investment.
{Categories} _Category: Platforms{/Categories}
{URL}https://devops.com/?p=174053{/URL}
{Author}Mike Vizard{/Author}
{Image}https://devops.com/wp-content/uploads/2020/08/observability.jpg{/Image}
{Keywords}Blogs,Business of DevOps,Features,News,Social – Facebook,Social – LinkedIn,Social – X,Spotlight,devops,IT costs,observability{/Keywords}
{Source}Platforms{/Source}
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