During the early periods of cloud computing, most organizations used single-location data centers. These single-location data centers often faced higher risks of downtime and service disruption due to localized disasters or hardware failures. As a solution to these problems, cloud services like AWS introduced the concept of availability zones. This introduction was an important milestone in the evolution of cloud computing, as it facilitated high availability through geographic distribution.

To buy or to build... that is the question businesses must ask when deciding to buy off-the-shelf software or create custom software to satisfy their business needs. Deciding whether to buy off-the-shelf software or create custom software is a lot like choosing between a ready-made meal and cooking a meal from scratch. It's a big decision for any business (or hungry person). Let's imagine we're planning dinner...

Splunk gives you amazing tools to analyze system data and make business-critical decisions, react to issues, and even proactively address future problems. And since it is so good at those things, it’s not surprising that there are myriad tools to help you reflexively analyze your own Splunk deployment.

Data analysis is crucial in today's businesses and organizations. With the increasing amount of data being created at 328.77 million terabytes of data per day, and them being readily available to most businesses, having efficient tools that can help analyze and interpret this data effectively is essential. In this article, we will discuss the top 9 best data analysis tools currently used in the market today.

Time series data is becoming more prevalent across many industries. Indeed, it is no longer limited to financial data. As the need to handle time-stamped data increases, the demand for specialized databases to handle this type of data has also grown. The solution: Time series databases. In this introduction guide, we'll explain all the basics you need to know about time series databases, including what they are, how they work and are applied, and some of their benefits.

Splunk’s extensible and scalable data platform has been instrumental in helping ITOps teams fully understand their tech environments and tackle any IT use case with data streaming, dashboarding, federated search, AI/ML, and more. But, with the explosion of telemetry and the growing complexity of digital systems, ITOps practitioners who rely solely on a logging solution are missing out on critical insights from their digital systems.

In software development, two primary models of software exist: open source and closed source. Both types have their benefits and drawbacks, and understanding the differences between them can help you make informed decisions when choosing software for your projects. To simplify the concepts of open source and closed source software, let’s use the analogy of community cookbooks — open source — and a secret family recipe: the closed source.

Your log records may be missing a key ingredient that unlocks the world of observability for your applications, infrastructure and services. If you're building a new application or enhancing an existing one, consider adopting the OpenTelemetry Logs Data Model's Log and Event Record Definition. Adopting this definition enriches your logs by adding additional data, making it easier to use them to correlate them with metrics and traces, in addition to XYZ.

Amazon CloudWatch Logs enables you to centralize the logs from different AWS services, logs from your applications running in AWS and on-prem servers, using a single highly scalable service. You can then easily view these logs data, search them for specific error codes or patterns, filter them based on specific fields, or archive them securely for future analysis.

Like humans, machines need to continually learn from non-stationary information streams. While this is a natural skill for humans, it’s challenging for neural networks-based AI machines. One inherent problem in artificial neural networks is the phenomenon of catastrophic forgetting. Deep learning researchers are working extensively to solve this problem in their pursuit of AI agents that can continually learn like humans.