Operations | Monitoring | ITSM | DevOps | Cloud

Observability tools have traditionally focused on capturing and analyzing log data to improve application performance monitoring and security. Data observability turns the focus back on the data to improve data quality, tune data infrastructure and identify problems in data engineering pipelines and processes. “Data analysts and business users are the primary consumers of this data,” said Steven Zhang, director of engineering at Hippo Insurance.

As an observability provider, we are always confronted with our clients’ goal for faster resolution of problems and better overall performance of their systems. By working on large-scale projects at Logz.io, I see the same main challenge coming up for all: extracting valuable insights from huge volumes of data generated by modern systems and applications.

We here at Honeycomb really like OpenTelemetry and goodie bags, so we have a nice little OpenTelemetry-flavored holiday goodie bag to share with you before you’re off for the holidays!

Is all observability data worth the same cost? If you guessed no, then you’d obviously be correct. Anyone familiar with the very nature of gaining targeted observability knows that some data points hold more value than others. Yet, many observability platforms still treat all types of log data the same, and as a result, related costs remain uniform. One of the most persistent observability challenges today is the cost of indexing log data.

In the software industry, monitoring and observability come hand in hand and are referenced simultaneously, but both are not the same thing. Having the right tools for these capabilities is vital to keep your systems, data, and security perimeter safe. With cloud solutions topping up in the integration space, great tools have been available in the market, and choosing the right one would be challenging.

In data management, numerous roles rely on and regularly use observability data. The Site Reliability Engineer is one of these roles. Site Reliability Engineers (SREs) work on the digital frontlines, ensuring performant experiences by using observability data to maintain stability and awareness of software running in various environments across organizations.

A hybrid infrastructure brings business benefits but it also brings new challenges. Migrating workloads to the cloud is a complex operation that generates more data than engineering teams can adequately manage. Traditional monitoring tools are limited in helping teams find and fix problems during and after a cloud migration. This can throw business strategies off course, limit customer value and hurt the bottom line.