Operations | Monitoring | ITSM | DevOps | Cloud

The complexity of applications is continually increasing the need for good logs. This need is not just for debugging purposes but also for gathering insight about the performance and possible issues with an application. The Python standard library is an extensive range of facilities and modules that provide most of the basic logging features. Python programmers are given access to system functionalities they would not otherwise be able to employ.

An application running in production is a difficult beast to tame. Most experienced developers–ones who spent enough late nights or Saturday mornings trying to break apart a nasty production bug–will try and create the clearest possible picture for their later selves while writing their code, so that they could understand what’s actually going on in the system during an incident.

Have you ever wanted to check the status of your Splunk Cloud Platform deployment but can't easily access your laptop? We've got you covered— the Cloud Monitoring Console is now available on Spunk Mobile.

The Splunk Dataflow template is an indispensable tool that allows Google Cloud customers to easily engineer a horizontally scalable and fault-tolerant logging export pipeline into Splunk® Enterprise and Splunk Cloud Platform™.

The past year has been significant for continued development of both DevOps practices and new developments across the open source community. To that end, Logz.io is moving forward with renewed support for the Fluentd log shipper. This new proxy will serve as an alternative to Filebeat and Logstash, which recently moved away from open source licensing. Additionally, this integration utilizes an HTTP proxy instead of the SOCKS5 proxy necessary for Filebeat.

“Observability,” is the process in DataOps of recording data generated by digital systems as they go about their processes. There are some great companies in the observability space, generating a whopping $17 billion annually, and contributing a significant portion to the modest 2.5 quintillion bytes of data created every year.

Troubleshooting production issues with virtual machines (VMs) can be complex and often requires correlating multiple data points and signals across infrastructure and application metrics, as well as raw logs. When your end users are experiencing latency, downtime, or errors, switching between different tools and UIs to perform a root cause analysis can slow your developers down.

If you are a candidate looking for your next role that involves an in-depth knowledge of Elasticsearch and the wider Elastic Stack then you will want to revise beforehand. In this resource guide on the top ELK interview questions, we've listed all of the leading questions that candidates are commonly asked about Elasticsearch, Logstash & Kibana (and their contemporary tools and plugins) alongside the answers. Want to improve your knowledge further?