IBM Buys Observability

The broader concept of 'observability' was new to me, but as described very useful.    Performance and beyond.  Clearly applicable to AI applications.  Now thinking it further.

IBM acquires application observability startup Instana  BY  Maria Deutscher  in SiliconAngle

IBM Corp. today announced plans to acquire Instana Inc., a Chicago-based application monitoring startup that has raised more than $50 million from investors including Accel and Meritech Capital Partners.

The terms of the deal were not disclosed.

Instana’s namesake platform helps organizations track the health of their workloads to identify technical issues that may interfere with the user experience. The platform can spot issues such as latency spikes that are causing an application to respond slowly and processing errors caused by software bugs.

Instana can track a wide range of workloads. The startup’s platform lends itself to monitoring applications running both in the cloud and on-premises, as well as the automated continuous integration and continuous delivery toolchains that development teams use to release new code. Notably, the platform also provides visibility into workloads running on IBM mainframes, a feature that no doubt factored into the company’s decision to buy Instana.

The deal buys IBM new artificial intelligence capabilities. In addition to displaying data about application performance, Instana uses machine learning to surface patterns of interest for information technology teams. The startup’s platform can create a map of all the different components that make up an application and automatically update the visualization when one of the components changes.  ... "

Need for Data Observability

Never called it that in particular, but yes it is important, and is worth the included tag.  And I add, the changes and non-changes in data should also be 'observable'.

What is Data Observability?
Hint: it’s not just data for DevOps.  By Barr Moses in Medium

Observability is no longer just for software engineering. With the rise of data downtime and the increasing complexity of the data stack, observability has emerged as a critical concern for data teams, too.

Developer Operations (lovingly referred to as DevOps) teams have become an integral component of most engineering organizations. DevOps teams remove silos between software developers and IT, facilitating the seamless and reliable release of software to production.

As organizations grow and the underlying tech stacks powering them become more complicated (think: moving from a monolith to a microservice architecture), it’s important for DevOps teams to maintain a constant pulse on the health of their systems. Observability, a more recent addition to the engineering lexicon, speaks to this need, and refers to the monitoring, tracking, and triaging of incidents to prevent downtime.

As a result of this industry-wide shift to distributed systems, observability engineering has emerged as a fast-growing engineering discipline. At its core, observability engineering is broken into three major pillars:

Metrics refer to a numeric representation of data measured over time.
Logs, a record of an event that took place at a given timestamp, also provide valuable context regarding when a specific event occurred.

Traces represent causally related events in a distributed environment.
(For a more detailed description of these, I highly recommend reading Cindy Sridharan’s landmark post: Monitoring and Observability.   ... .'

Observability

A new term to me, but apparently in use to understand how a system operates to work with it or debug systems.   The description makes logical sense, the details are more complex.

Software Engineering: Observability

Observability — A 3-Year Retrospective    by Charity Majors in Thenewstack

Observability, the development approach or moreover the “movement’” is about three years old and Charity Majors, one of the early pioneers in the field, has decided to take a step back, pan out and “observe” how far it has come. In this article, she takes a closer look at why it formed and why other approaches and methods fall short, citing important contributors along the way. She explains the criticality of adopting observability for any engineering team building and maintaining complex, distributed systems. .....

Like so many other terms in software engineering, “observability” is a term borrowed from an older physical discipline: in this case, control systems engineering. Observability is the mathematical dual of controllability.

“Less formally, this means that one can determine the behavior of the entire system from the system’s outputs. If a system is not observable, this means that the current values of some of its state variables cannot be determined through output sensors.”  .... "