Operations | Monitoring | ITSM | DevOps | Cloud

Redundancy and resiliency are both important factors for keeping things running smoothly in many industries. For example: Even small businesses, like home-based operations or mom-and-pop shops, should think about redundancy and resiliency to avoid disruptions in their day-to-day work. While researching for this article in my home office, my internet service went out and stayed out for a couple of hours.

With more organizations becoming tech-enabled to tackle the AI boom, a new term has emerged: the fusion team. At least 84% of companies and 59% of government entities have set up “fusion teams," according to Gartner data. A new concept coined by Gartner, the fusion team aims to encourage collaborative development among technology and business teams. But what exactly is a fusion team, and why is it becoming increasingly important in today's business landscape?

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.

Centralizing logs (arranging all records in one place) is often challenging as we need to decide whether to use a log shipper or directly log from the application. If you are not familiar with a log shipper, logging directly from the library might be a suitable option for development (it is easy to configure). However, in production, you'll likely want to use one of the available log shippers, mainly due to buffers, since blocking the application or dropping data (immediately) may not be an option.