The majority of users continually depend on a variety of web applications to meet their everyday needs, so a business’s success is now often proportionate to the success of its application performance. As a result, the importance of using an appropriate APM solution has become even greater to businesses globally. Application Performance Monitoring (APM) still continues to grow in popularity and is now considered a must for observing the health and performance of your organization's applications.

In today's data-driven world, ensuring the stability and efficiency of software applications is not just a need but a requirement. Enter observability. But as with any evolving technology, there's always room for growth. That growth, as it stands today, is the convergence of artificial intelligence (AI) with observability pipelines. In this blog, we'll explore the idea behind this merge and its potential.

The availability and reliability of any IT service ultimately govern end-user experience and service performance, both of which have significant business impact. These two concepts — availability and reliability — are particularly relevant in the era of cloud computing, where software drives business operations, but that software is often managed and delivered as a service by third-party vendors.

A data lake is a centralized data repository where structured, semi-structured, and unstructured data from a variety of sources can be stored in their raw format. Data lakes help eliminate data silos by acting as a single landing zone for data from multiple sources. But what’s the difference between a traditional data lake and a real-time data lake?

When it comes to observability, your organization will have no shortage of options for tools and platforms. Between open source software and proprietary vendors, you should be able to find the right tools to fit your use case, budget and IT infrastructure. Observability should be cost-efficient, easy to implement and customers should be provided with the best support possible.

Splunk has become one of several players in the observability industry, offering a set of features and a specific focus on legacy and security use cases. That being said, how does Splunk compare to Coralogix as a complete full-stack observability solution? Let’s dive into the key differences between Coralogix vs Splunk, including customer support, pricing, cost optimization, and more.

Whatever the size of your network, as an engineer you will often notice a significant amount of log data being generated. This data will require centralizing for further analysis and management, which can be particularly challenging if you have varying log formats, such as plain text or HTML.

Imagine yourself with a folder containing millions of gigabytes of data. If you were asked to process it with an Excel spreadsheet, you wouldn’t need to be a data expert to know that’s impossible. We refer to that amount of data as “big data”. Big data requires advanced techniques, tools, and methods beyond what regular data analytics entails, which is where big data analytics comes in.

When Dolly Parton sang about “Workin’ 9-5”, she almost seemed to understand the very specific pain of sitting at an IT help desk and getting one last call at 4:59 in the afternoon. When you pick up that last call of the day, the last thing you want is to begin a long, arduous investigation.

Grafana Loki is an open source logs database built on object storage services in the cloud. These services are an essential component in enabling Loki to scale to tremendous levels. However, like all SaaS products, object storage services have their limits — and we started to crash into those limits in Grafana Cloud Logs, our SaaS offering of Grafana Loki.