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This blog post explains observability, a method to understand how a system works by examining its outputs. Observability is different from monitoring, which just collects data. The three pillars of observability are metrics (numerical indicators), logs (event records), and traces (request flow tracking). Popular observability tools include Prometheus, Grafana, Jaeger, ELK Stack, Honeycomb, Datadog, New Relic, Sysdig, and Zipkin. By understanding these pillars and using the right tools, you can gain valuable insights into your system's health and troubleshoot problems before they impact users.

This blog post compares two leading incident response tools: PagerDuty and Splunk On-Call (formerly VictorOps).

Choosing a VictorOps Alternative: PagerDuty is a robust alternative to Splunk On-Call, excelling in alerting, incident management, and automation.

Choosing a Splunk Alternative: If real-time alerting, collaboration, and swift response are your priorities, PagerDuty might be ideal. Splunk On-Call excels in data analysis and proactive problem identification.

Feature Breakdown:

Alerting & Escalation: PagerDuty offers real-time, multi-channel notifications with escalation policies, while Splunk On-Call focuses on data correlation and customization.

Incident Response: PagerDuty provides collaboration tools and centralized consoles, whereas Splunk On-Call centers on log analysis and root cause investigation.

Automation & AI: Both leverage automation and AI, with PagerDuty emphasizing alert grouping and workflows, and Splunk On-Call focusing on anomaly detection and predictive analytics.

Integrations: PagerDuty boasts seamless integrations with various tools, while Splunk On-Call prioritizes data source connections and custom app building.

Pricing: PagerDuty has tiered pricing starting at $25 per user per month, while Splunk On-Call's pricing is complex, ranging from a free tier to expensive enterprise plans.

Beyond the Giants:

The blog also introduces Squadcastas a contender, offering a blend of features from both PagerDuty and Splunk On-Call at an affordable price.

This blog post compares two incident alerting and response platforms: Opsgenie and Pagerduty. It helps readers choose between the two based on their needs and budget.

Here's a quick breakdown:

On-Call Scheduling: Opsgenie is easier to use, Pagerduty is more powerful but complex.

Alerting: Pagerduty offers more sophisticated alerting with AI-powered noise reduction. Opsgenie provides the basics but lacks advanced features without extra cost.

Incident Response: Pagerduty excels with features like automated actions and deep ITSM integrations. Opsgenie offers basic functionalities.

Integrations: Pagerduty offers more integrations (including Atlassian ecosystem) while Opsgenie has a respectable library of essential connections.

Pricing: Opsgenie starts at $11/month/user, Pagerduty starts at $25/month/user (with additional costs for advanced features).

Overall, Opsgenie is ideal for those who prioritize user-friendliness and affordability. Pagerduty is better suited for those who need advanced features, strong integrations, and robust incident response capabilities, but are willing to pay a premium.

This blog post discusses how Squadcast's Microsoft Teams application can improveon-call incident response workflows. It highlights the key features of the integration, including real-time incident notifications, actionable messaging, and clear on-call visibility. The post also details the benefits of using Squadcast, such as improved collaboration, reduced downtime, and enhanced situational awareness. It concludes by explaining the simple three-step integration process and mentions additional features of Squadcast.

Tim Paul watched a talk about sustainable AI at Services Week 2024 by software developer Ishmael Burdeau, who mentioned the Jevons paradox. The paradox explains how energy efficiency gains can result in more energy consumption rather than less, seen in various scenarios such as improving road networ..

"Canonical’s 10th Long Term Supported release sets a new standard in performance engineering, enterprise security and developer experience."..

High throughput shared file systems are crucial for HPC and AI environments, providing the storage needed for training large models or conducting research. With Amazon FSx for Lustre, organizations can leverage AWS compute, network, and storage resources on-demand, reducing time and cost. By utilizi..

Cloudflare now offers software development kits (SDKs) for Typescript, Go, and Python. To get started, install the packages using the following commands: npm install cloudflare for Typescript, go get -u github.com/cloudflare/cloudflare-go/v2 for Go, and pip install --pre cloudflare for Python. Autom..

AWS provides horizontal scaling, but vertical scaling is not available. By using Amazon CloudWatch Alarms, Amazon RDS Events, Simple Notification Service, and Lambda Functions, the goal of vertical autoscaling was achieved...

There are several key issues that can affect ML models' functional performance in production, including training-serving skew, data and concept drift, and data processing pipeline issues. Monitoring model performance in production requires setting up a system that can ingest data and prediction logs..