Fact: The Security Operations team at Grafana Labs loves logs. They are a key pillar of observability for many reasons, such as how they are stuffed full of details to help us diagnose the “why?” when things go wrong. This is especially true when the information pertains not to a series of unfortunate events, but instead to an adversary trying to cause us harm.

We are excited to announce that Elastic has been recognized as a Strong Performer in The Forrester Wave™: Artificial Intelligence for IT Operations (AIOps), Q4 2022 in our first year participating! As organizations modernize their infrastructure and applications, operations and development teams are faced with an exponential growth in data.

We’re excited to announce significant improvements to our Archive+Restore capabilities – which enables low-cost long term log storage in AWS S3 or Azure Blob, while providing access to ingest those logs into Logz.io at any time. The first enhancement is Power Search, which will make it faster to restore logs from archived log data in AWS S3 (and soon for Azure Blob) in our Open 360™ platform.

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.

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.

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.

Resilience has become the new strategic imperative for manufacturers during these testing times. As the world’s challenges make headlines, so do the innovative responses of manufacturing leaders. Savvy manufacturers automate, overhaul fundamental processes, modernize their security posture and reduce their CO2 footprint. Forward-focused organizations double down on their cloud investment to become more agile and resilient. And none of it is possible without data.

FluentD is a free and open-source data collector. With its decentralized ecosystem, it’s known for its built-in reliability and cross-platform compatibility. One of the biggest challenges in big data collection is the lack of standardization between collection sources. They just aren’t able to talk to each other. With FluentD, you can address one of the biggest challenges to big data log collection.

In the past decade, we've seen explosive growth in the adoption of the cloud-based infrastructure model. IT organizations are increasingly choosing to reduce their up-front investments in IT infrastructure by deploying their applications into cloud environments. These environments offer on-demand availability of data storage and computing power that organizations need to handle high volumes of data and growing demand for application access and services.

You’ve made the decision to implement a centralized log management solution because you know that it’s going to save you time and money in the long term. However, to get the most bang for your log management buck, you need to understand how the different parts of your log management deployment work. Once you understand each resource, you can implement a more efficient log management architecture.