StarTree broadly enhances its real-time database platform by adding query support for metrics, logs and traces.
StarTree today made a case for becoming the foundation upon which observability is gained in IT environments by adding support for metrics, logs and traces to its real-time database platform delivered as a cloud service.
In addition, the company is also making generally available a StarTree ThirdEye service for detecting anomalies and identifying the root cause of application issues. It has also made a free serverless computing tier available on its cloud service.
Viable Alternative to the Legacy Database
Announced at the Real-Time Analytics Summit conference, StarTree Cloud is based on Apache Pinot, an open source columnar database optimized for real-time analytics, originally developed by LinkedIn. StarTree, via a private preview, is signaling that it views Apace Pinot as a viable alternative to the legacy database, upon which observability platforms DevOps teams use to troubleshoot IT environments.
StarTree, to further advance Apache Pinot, is also now adding a vector indexing capability to Apache Pinot along with support for a write application programming interface (API) to facilitate real-time synchronization with extract, transform and load (ELT) pipelines and additional integrations to Grafana and Tableau to improve visualizations.
Chinmay Soman, head of product for StarTree, said the issue with existing databases is they are not designed to scale to the level of performance that modern application environments will require.
Early Days for Achieving Observability
It’s still early days for achieving observability in application environments but many DevOps teams are now moving well past simply monitoring a set of pre-determined metrics. Observability platforms enable DevOps teams to launch queries that make it easier to troubleshoot applications. Not every DevOps team knows what queries to investigate. With the rise of artificial intelligence (AI), it’s becoming more feasible to rely on machine learning algorithms to analyze application environments using metrics, logs and traces stored in an observability platform.
Few DevOps teams will build an observability platform themselves but the capabilities of the underlying databases on they are based are critical, said Soman. DevOps teams should make certain the databases used in these platforms today will be able to scale to meet real-time processing requirements that will only steadily increase as more complex applications are deployed across distributed computing environments, he added.
In the meantime, StarTree is moving toward providing more observability capabilities via the managed database service it provides via the cloud.
It’s only a matter of time before DevOps teams need deeper levels of visibility to manage modern application environments in real time. As organizations become more dependent on software than ever, even the most minor glitch can now have a major financial impact. The challenge DevOps teams face, of course, is finding the funding needed to acquire these capabilities.
In the meantime, DevOps teams should at the very least start assessing which of the many monitoring tools being employed today might one day be rationalized by an observability platform, that in time might lower total costs by streamlining DevOps workflows that today are often more disjointed than anyone cares to admit.
