Operations | Monitoring | ITSM | DevOps | Cloud

Apache Kafka® is one of the most popular streaming and messaging platforms, commonly used in a pub-sub (publish-subscribe) model, where consumer software applications send data via messages that producer software applications can consume. Teams use Kafka for a variety of use cases, including monitoring user activity, sending notifications, and concurrently processing streams of incoming data such as financial transactions.

Last year we published a popular paper on how to calculate the true cost of an Elasticsearch, or ELK (for Elasticsearch, Logstash, Kibana) stack environment. The paper helps readers calculate their overall annual cost of ownership for their ELK environment, and reveals how the cost burden of ELK is much higher than anticipated for most customers. That paper clearly hit a nerve — it’s been, by far, our most downloaded piece of content.

AIOps is a DevOps strategy that brings the power of machine learning to bear on observability and system management. It’s not surprising that an increasing number of companies are now adopting this approach. AIOps first came onto the scene in 2015 (coincidentally the same year as Coralogix) and has been gaining momentum for the past half-decade. In this post, we’ll talk about what AIOps is, and why a business might want to use it for their log analytics.

We’re thrilled to announce the release of the Splunk App for Content Packs, an app that acts as a one-stop shop for prepackaged content and out-of-the-box searches and dashboards for common IT infrastructure monitoring sources, making it easy to get up and running with Splunk for IT use cases. In the past, you may have had to install and manage individual apps like Splunk App for VMWare and Splunk App for Windows Infrastructure.

The pursuit of Digital Transformation and DevOps practices has led to several benefits such as increased deployment rates and better collaboration across teams. However, it has also led to endless abstraction, an increase in responsibilities, and many new tools (Kubernetes, hybrid-clouds and all their services, etc.). This increase in complexity has turned observability into an essential component of all ecosystems.

PostgreSQL is a popular open-source, object-relational database. As with any other data storage solution, capturing metrics is crucial for making sure your database is reliable, available, and performing optimally. This will help you dig deeper into database performance problems, do performance tuning, optimize queries and indexes, and make partitioning decisions. But that’s not all. You’ll also be able to set up alerts and plan for failures or upgrades.

Sometimes, the data you want to analyze lives in AWS S3 buckets by default. If that’s the case for the data you need to work with, good on you: You can easily ingest it into an analytics tool that integrates with S3. But what if you have a data source — such as logs generated by applications running in a Kubernetes cluster — that isn’t stored natively in S3? Can you manage and analyze that data in a cost-efficient, scalable way? The answer is yes, you can.