The latest News and Information on Log Management, Log Analytics and related technologies.
As your infrastructure and applications scale, so does the volume of your observability data. Managing a growing suite of tooling while balancing the need to mitigate costs, avoid vendor lock-in, and maintain data quality across an organization is becoming increasingly complex. With a variety of installed agents, log forwarders, and storage tools, the mechanisms you use to collect, transform, and route data should be able to evolve and adjust to your growth and meet the unique needs of your team.
The volume of logs that organizations collect from all over their systems is growing exponentially. Sources range from distributed infrastructure to data pipelines and APIs, and different types of logs demand different treatment. As a result, logs have become increasingly difficult to manage. Organizations must reconcile conflicting needs for long-term retention, rapid access, and cost-effective storage.
Having your Cribl Stream instance connected to a remote git repo is a great way to have a backup of the cribl config. It also allows for easy tracking and viewing of all Cribl Stream config changes for improved accountability and auditing. Our Goal: Get Cribl configured with a remote Git repo and also configured with git signed commits. Git signed commits are a way of using cryptography to digitally add a signature to git commits.
As today’s businesses increasingly rely on their digital services to drive revenue, the tolerance for software bugs, slow web experiences, crashed apps, and other digital service interruptions is next to zero. Developers and engineers bear the immense burden of quickly resolving production issues before they impact customer experience.
Large Language Models (LLMs) can give notoriously inconsistent responses when asked the same question multiple times. For example, if you ask for help writing an Elasticsearch query, sometimes the generated query may be wrapped by an API call, even though we didn’t ask for it. This sometimes subtle, other times dramatic variability adds complexity when integrating generative AI into analyst workflows that expect specifically-formatted responses, like queries.