This blog post explores how DORA metrics can be used to improve DevOps practices, specifically focusing on incident management. DORA metrics are a set of four key metrics that measure the performance of a DevOps team: deployment frequency, lead time for changes, change failure rate, and mean time to restore (MTTR). By implementing DORA metrics, teams can identify bottlenecks in their workflow and make data-driven decisions to improve efficiency and agility. The blog post also discusses different tools that can be used to track DORA metrics and manage incidents. Finally, it highlights the benefits of using DORA metrics, such as improved communication with stakeholders, faster incident resolution, and increased business agility.

This blog post offers a comprehensive comparison of two critical AWS services for monitoring and logging: CloudWatch and CloudTrail. It clarifies their distinct functionalities and use cases to empower users to make informed decisions for their AWS environment.

CloudWatch is a monitoring service designed for AWS resources and applications. It collects metrics, monitors performance, offers alarms for anomalies, and provides log data analysis.

CloudTrail acts as a watchdog, meticulously recording AWS resource activity through API call history. This log data is invaluable for security analysis, compliance, and troubleshooting.

The blog highlights key features of each service, including:

CloudWatch: Metrics, alarms, logs, events, anomaly detection, custom dashboards.

CloudTrail: Activity logging, event history, multi-region support, data event logging, integration with other AWS services, log file encryption, and validation.

Use cases explored for each service include:

CloudWatch: System-wide monitoring, event detection and response, application performance monitoring, custom metrics, and disaster recovery.

CloudTrail: Change management, security and compliance monitoring, governance and auditing, and risk management.

Using Rego for cloud configuration is awful. Use Really: policy-as-code built for humans.

When we started building Resourcely, the global configuration engine for cloud infrastructure, we slowly realized that the status quo of policy-as-code was broken. Writing our first Resourcely guardrails in Rego took hours to create and even more time to maintain. Writing new policies was extremely tedious and time-intensive, especially given the fact that we wanted to make them flexible. To help achieve our mission of making infrastructure more secure, it became evident that a new policy language would be needed that allowed policy to be written and maintained without headaches.

Do you rely on your monitoring solutions to let you know when things are wrong? That is so brave of you! On a more serious note, please think twice. Monitoring is not enough! It can’t explain why things happen the way they do (because it doesn’t see the past beyond metrics) and it doesn’t tell you what is going to happen (so it can’t predict the future). This is a serious problem and we need a solution (spoiler alert: we need database guardrails). Let’s read on to understand why.

This blog post is a guide for engineers on how to create and manage on-call rotations and schedules. It highlights the benefits of having an on-call rotation system, including faster incident response times, reduced stress for engineers, and improved knowledge sharing. The blog post also details factors to consider when creating a rotation schedule, such as team size, system complexity, incident frequency, and customer needs. It offers tips for building an effective system, including exploring different rotation options, defining clear responsibilities, investing in training, and leveraging on-call scheduling software. Finally, the blog post introduces Squadcast as a unified incident response platform that can help organizations streamline their on-call operations.

The blog post discusses the importance of monitoring for DevOps and SRE teams, emphasizing choosing the right tool based on specific needs. It categorizes monitoring into network, server, and application monitoring and highlights factors to consider when selecting a tool. It then dives into popular incident monitoring tools like Prometheus, Zabbix, and Datadog, along with their key features. Finally, it offers a conclusion recommending further exploration of each tool's website for a deeper understanding