In part 1 of this series, we talked about zombie data and what it means for your observability architecture. In this post, we’ll talk more about how to handle all of it. How well can your organization handle the firehose of data it’s collecting? Yes, you have the ability to collect it, but chances are you don’t have the financial or human resources available to analyze all of it effectively.

The ongoing partnership between Papertrail and DigitalOcean led to the development of the Papertrail software as a service (SaaS) add-on in the DigitalOcean Marketplace. With the add-on, developers can add powerful, simple, and scalable Papertrail log management to their DigitalOcean infrastructure in seconds. In two earlier posts, we reviewed how the add-on helps teams simplify and centralize log management.

Ask any cloud software team using Kubernetes (and most do); this powerful container orchestration technology is transformative, yet often truly challenging. There’s no question that Kubernetes has become the de-facto infrastructure for nearly any organization these days seeking to achieve business agility, developer autonomy and an internal structure that supports both the scale and simplicity required to maintain a full CI/CD and DevOps approach.

We’ve reached the point where our ability to collect data has actually exceeded our ability to process it. Nowadays, it’s commonplace for organizations to have terabytes or even petabytes worth of data sitting in storage, waiting patiently for well-intentioned systems admins to eventually analyze it.

DevOps is an IT delivery concept that combines people, practices and tools with the shared goal of accelerating the development of applications and services. Adopting DevOps at enterprise level typically requires: The continuous development of DevOps practices, as well as other factors like the rapid pace of modern code changes, facilitates a need for DevOps monitoring: a set of tools and processes to support the entire software development lifecycle.

Redis is an open-sourced, BSD 3 licensed, highly efficient in-memory data store. It is used widely in the industry because of its incredible performance and ease of use. It can easily be used as a distributed, in-memory key-value store, cache, or message broker. It can hold virtually any data structure, making it highly versatile. Redis was architectured and developed with speed in mind and designed to keep all the data in memory.

Grafana Loki is Grafana Labs’ open source log aggregation system inspired by Prometheus. Loki is horizontally scalable, highly available, and multi-tenant. In addition, Grafana Cloud Logs is our fully managed, lightweight, and cost-effective log aggregation system based on Grafana Loki, with free and paid options for individuals, teams, and large enterprises.

The real beauty of this modern, cloud-fueled, DevOps-driven world that we are living in is that it’s so highly composable. In so many ways, we’ve been freed from the limitations and structures of the previous annals of software and technology history to build things the way that we want to, and however we choose to do so.

For any organization that stores, processes, or transmits cardholder data, monitoring can pose a particular set of challenges. The Payment Card Industry (PCI) Data Security Standard (DSS) dictates rigorous monitoring and data security requirements for the cardholder data environments (CDEs) of all merchants, service providers, and financial institutions.

In the recent past, container-based deployment architectures have played a significant role in improving applications on multiple fronts, including: Containers are all-inclusive packages containing lightweight services which are easy to spawn and terminate. However, container-based deployments can comprise hundreds of individual services and their replicas spinning up and down at any moment.