The latest News and Information on Log Management, Log Analytics and related technologies.
Log data is the most fundamental information unit in our XOps world. It provides a record of every important event. Modern log analysis tools help centralize these logs across all our systems. Log analytics helps engineers understand system behavior, enabling them to search for and pinpoint problems. These tools offer dashboarding capabilities and high-level metrics for system health. Additionally, they can alert us when problems arise.
While log parsing isn’t very sexy and never gets much credit, it is fundamental to productive and centralized log analysis. Log parsing extracts information in your logs and organizes them into fields. Without well-structured fields in your logs, searching and visualizing your log data is near impossible.
In today’s digital-first world, most security problems are actually data problems, and data volumes are outpacing organizations’ abilities to handle, process, and get value from it. You’ll have 250% more data in five years than you have today, but the chances of your budget increasing to match that are slim. The challenges that come with managing the rise in enterprise data volume directly affect your ability to adequately address cybersecurity risks.
Are you prepared for the unexpected? In today's rapidly evolving world, operational resilience has never been more critical for businesses to survive and thrive. Resiliency is the ability of a system to maintain its operations under adverse conditions, including system failures, unexpected surges in user demand, or even security breaches. The heart of many applications, particularly in this era of data-driven decision-making, is the data store or database.
Technology juggernauts–despite their larger staffs and budgets–still face the “cognitive load” for DevOps that many organizations deal with day-to-day. That’s what led Spotify to build Backstage, which supports DevOps and platform engineering practices for the creation of developer portals.
In a previous blog post, we built a small Python application that queries Elasticsearch using a mix of vector search and BM25 to help find the most relevant results in a proprietary data set. The top hit is then passed to OpenAI, which answers the question for us. In this blog, we will instrument a Python application that uses OpenAI and analyze its performance, as well as the cost to run the application.
When our CEO and co-founder Tomer Levy delivered his “Observability is Broken” presentation at last year’s AWS re:Invent, he highlighted numerous challenges faced by today’s organizations as they seek to advance their observability practices. Of the six individual points that he noted, two specifically dealt with the current shortage of available engineering expertise, with another two focused on data overload.
