Operations | Monitoring | ITSM | DevOps | Cloud

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.

Open source monitoring and observability tools can be found in production all over the world – whether they’re being used by startups or entire enterprise development teams. DevOps, ITOps, and other technical teams rely on tools like Prometheus, Grafana, OpenSearch, OpenTelemetry, Jaeger, Nagios, Zabbix, Graphite, InfluxDB, and others to monitor and troubleshoot their cloud environment.

Software monitoring, how does it work? “We paid for a bunch of tools but we don’t know what we should be looking at. There are tons of charts that don’t seem to mean anything!” If you talk to people about software monitoring you’ve inevitably heard something similar to this. With so many possible metrics it can feel like searching for a needle in a haystack. Even with curated dashboards there is inherent confusion about what is important.

Flexera’s State of the Cloud Report 2022 pointed out that significant cloud spending is wasted, a major issue that is getting more critical as cloud costs continue to rise. In the current macroeconomic conditions, companies focus on identifying ways to reduce spending. To effectively do that, we need to understand the pricing model. We can then work towards the challenges of cost monitoring, optimization, and forecasting.

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.

In my work as a technical evangelist at Cribl, I regularly talk to companies seeing annual data growth of 45%, which is unsustainable given current data practices. How do you cost effectively manage this flood of data while generating business value from critical data assets?

Logz.io is one of Logz.io’s biggest customers. To handle the scale our customers demand, we must operate a high scale 24-7 environment with attention to performance and security. To accomplish this, we ingest large volumes of data into our service. As we continue to add new features and build out our new machine learning capabilities, we’ve incorporated new services and capabilities.

Development teams build modern applications using microservice architectures. Individual services are built and maintained by separate teams, and then these services are combined using container-based orchestrators to comprise a complete product offering. Microservices are a standard development method because they allow teams to iterate releases, providing ongoing new customer-facing features and bug fixes without needing to redeploy an entire platform or app.