Operations | Monitoring | ITSM | DevOps | Cloud

Observability is undergoing a massive shift as enterprises drive adoption of modern technologies, including cloud and microservices, along with disruptive technologies, such as generative AI (GenAI). To keep pace with the complex requirements of the modern tech stack, operations teams need to consider and adopt next-generation observability. Splunk users are often challenged by using products that provide fragmented observability, hampering their ability to modernize their environments.

Once upon a time, we thought “Log everything” was the way to go to ensure we have all the data we needed to identify, troubleshoot, and debug issues. But we soon had new problems: cost, noisiness, and time spent sifting through all that log data. Enter log analysis tools to help refine volumes of log data and differentiate signal from the noise to reduce mental toil to process. Log beast tamed, for now….

Imagine living in a world without caller ID, which is easy if you grew up in the “late 1900s.” Every time someone called, you had a conversation that followed this pattern: Hi! Who’s this? It’s Jeff! Hi Jeff! How’s it going? Today, most people already know who’s calling when they answer the phone because caller ID is built into smartphones and communications apps. As a developer, your Python logging is your application’s caller ID.

As applications grow in complexity, the ability to gather, process, and analyze logs becomes crucial for maintaining system health and troubleshooting issues. Two popular open-source log collectors have emerged as frontrunners in this space: FluentD and FluentBit. But how do you choose between them? This article dives deep into the FluentD vs FluentBit debate, exploring their features, performance, and use cases to help you make an informed decision.

We are excited to announce the launch of Elastic's Support Assistant. This blog takes you through a tour of our latest generative AI tool and some common scenarios where it can help with your own use of Elastic technology.

Docker is a popular platform that enables developers to package, distribute, and run applications within isolated environments called containers. Logs play an important role in the use of Docker for numerous reasons. For example, The running of Docker containers in isolation makes it difficult to handle troubleshooting issues and monitor application behavior effectively in the absence of detailed logging.

Designed in the early 1990s Python was built to emphasize code readability and on the principle that there should be one way to do things. Python is one of the most popular programming languages and is utilized for a range of purposes including machine learning, web applications, and data analysis.

AWS MediaTailor provides a wealth of information via metrics, but one key feature that is very difficult to track is the Transcoding performance. What is transcoding and why is performance important?

Modern enterprises are more dependent than ever on data. That's why it's more important than ever for organizations to ensure that their data is accurate, reliable, and easily accessible. Data observability is a modern method that helps achieve this. It involves real-time monitoring of data to detect unusual patterns. By doing so, it ensures data quality and reliability, which boosts operational efficiency and governance.