klotz: observability*

Observability refers to the ability to understand the internal state of a system by observing its output. It involves monitoring, logging, and tracing various other forms of data collection to gain insights into the system's behavior, performance, and health. In the context of cloud engineering, observability is crucial for maintaining the efficiency and reliability of distributed systems, as it helps identify and diagnose issues, optimize performance, and ensure security. Observability tools, such as Splunk, Honeycomb, and OpenTelemetry, are used to collect and analyze metrics, logs, and traces, enabling capacity planning, root cause analysis and incident response.

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  1. The Cloud Native Computing Foundation (CNCF) announces the expansion of OpenTelemetry into CI/CD observability, enabling standardized and vendor-agnostic monitoring of CI/CD pipelines.
  2. Use Callbacks to send Output Data to Posthog, Sentry, etc. LiteLLM provides input_callbacks, success_callbacks, and failure_callbacks to easily send data based on response status.
  3. The article explores the evolution of observability from traditional APM tools (Observability 1.0) to a more developer-focused approach (Observability 2.0). Observability 2.0 aims to provide real-time, actionable insights and empower developers throughout the software development lifecycle, addressing technical debt and enhancing the developer experience.
  4. Using Digital Twins to optimize data center operations and eliminate wasted IT infrastructure can save significant costs and improve sustainability.
  5. Performance Observability for Apache Spark. DataFlint is an open-source D-APM (Data-Application Performance Monitoring) tool for Apache Spark, built for big data engineers.
  6. This Splunk Lantern blog post highlights new articles on instrumenting LLMs with Splunk, leveraging Kubernetes for Splunk, and using Splunk Asset and Risk Intelligence.
  7. This article discusses the benefits of a disaggregated observability (o11y) stack for modern distributed architectures, addressing issues of flexibility, high cost, and lack of autonomy in traditional solutions. It highlights key layers of a disaggregated stack โ€” agents, collection, storage, and visualization โ€” and suggests the use of systems like Apache Pinot and Grafana.
  8. How to ensure data quality and integrity using open-source tools for observability in data pipelines.
  9. Data pipelines are essential for connecting data across systems and platforms. This article provides a deep dive into how data pipelines are implemented, their use cases, and how they're evolving with generative AI.
  10. OpenTelemetry is not just an observability platform, it's a set of best practices and standards that can be integrated into platform engineering or DevOps.

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