The hottest Observability Substack posts right now

And their main takeaways
Category
Top Technology Topics

All you need is Wide Events, not “Metrics, Logs and Traces”

A Song Of Bugs And Patches • 224 HN points • 15 Feb 24
🕹 Technology Observability Systems Events Metrics
  1. The concept of 'Wide Events' is proposed as a simpler and more effective approach to observability than the traditional 'Metrics, Logs, and Traces'.
  2. Older systems like Open Telemetry may contribute to confusion by categorizing data into distinct pillars, making observability seem complex.
  3. A system like Scuba, based on 'Wide Events', enables streamlined investigation and data exploration, emphasizing the importance of simplicity in observability tools.

The 3 Pillars of Observability in Distributed Software Systems[System Design Sundays]

Technology Made Simple • 99 implied HN points • 19 Jun 23
🕹 Technology Observability Metrics
  1. Observability in distributed software systems is crucial as they grow in complexity and scale.
  2. The 3 pillars of observability are logs, metrics, and traces, each offering unique insights into the system's operations.
  3. Combining logs, metrics, and traces is essential for building tools that enhance observability and improve system performance.

The Future of Network Observability and Network Automation

Internet Dynamics • 58 implied HN points • 06 Sep 23
🕹 Technology Networking Observability Automation Data Analysis APIs
  1. Network observability is crucial for network automation to handle real-time mitigation and remediation.
  2. Observability solutions need to consider topology, alerts, correlation, suppression, policy, and meta-data for effective network monitoring.
  3. Future approaches to observability and automation should recognize and manifest common components like Topology, CMDBs and Meta-data.
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How to make history with LLMs & other generative models

Leigh Marie’s Newsletter • 74 HN points • 21 Sep 23
🕹 Technology Machine Learning Infrastructure Applications Observability Privacy
  1. LLMs like Github Copilot can augment developer productivity and provide new opportunities for AI-enabled developer tools startups
  2. Generative models can significantly enhance efficiency for knowledge workers in fields like consulting, legal, medical, and finance, offering potential for startups in these areas
  3. New infrastructure opportunities exist around running large models locally, providing compute resources for model training, and challenging incumbents in ML frameworks and chips

OpenTelemetry in 2023

Bit by Bit • 21 implied HN points • 28 Aug 23
🕹 Technology Observability Protocol
  1. OpenTelemetry (OTEL) has evolved to cover all of observability, providing a stable standard and SDKs for metrics, logs, and traces.
  2. OTEL is now the second most active project in the CNCF, showing widespread adoption among observability providers.
  3. Key sub-projects of OTEL include specifications, implementations, the OpenTelemetry Protocol, the OpenTelemetry Collector, and the Open Agent Management Protocol.

The Architecture of Modern Observability Platforms

Bit by Bit • 11 implied HN points • 26 Jul 23
🕹 Technology Observability Architecture Platforms Data Storage
  1. Observability platforms help organizations understand the health of their applications using metrics, logs, and traces.
  2. Modern observability platforms tackle the challenge of handling large volumes of data and offer different types of architectures.
  3. Unifying the storage, ingestion, and querying layers can significantly scale and reduce costs in observability platforms.

First Mile Observability and the Rise of Observability Pipelines

Bit by Bit • 8 implied HN points • 14 Aug 23
🕹 Technology Observability Data Collection Open Source
  1. Observability extends beyond just backend systems to include the 'first mile' of data collection and processing.
  2. First-mile observability involves components like receivers, processors, and exporters to create observability pipelines.
  3. Various open-source and commercial solutions exist for implementing first-mile observability pipelines, with options like Vector, Fluent Bit, OTEL Collector, Cribl, Calyptia, Datadog, and Mezmo.

Middleware - Bringing observability up to speed

Termsheet by Attack Capital • 4 HN points • 04 Apr 23
🕹 Technology Observability AI Cloud Microservices Startup
  1. Founder Laduram Vishnoi's frustration with high costs of cloud observability tools led to the creation of Middleware.
  2. Middleware addresses challenges with traditional observability tools by offering a comprehensive and unified solution for cloud-native and microservices.
  3. Middleware uses AI-powered algorithms, is vendor agnostic, and correlates data from various sources to provide real-time observability and streamline issue debugging.

How we think about Data Pipelines is changing

The Orchestra Data Leadership Newsletter • 0 implied HN points • 08 Nov 23
🕹 Technology Data Pipelines Continuous Integration Infrastructure Observability
  1. Data pipelines are transitioning towards a focus on reliability and efficiency, similar to software engineering practices.
  2. Continuous Data Integration and Delivery in data engineering involves releasing data into production in response to code changes in a simple manner.
  3. Observability and metadata gathering play a crucial role in ensuring data quality and preventing issues before they occur in data pipelines.

Guidelines for Chaos Engineering, Part 1

realkinetic • 0 implied HN points • 06 Jul 20
🕹 Technology Chaos Engineering Monitoring Testing Resilience Observability
  1. Chaos testing helps understand how systems react to failure and ensures adequate monitoring for resilience.
  2. The goals of chaos testing include aligning system behavior with expectations and identifying gaps in monitoring and response capabilities.
  3. Performing chaos engineering involves defining steady-state metrics, forming hypotheses, running experiments, and adapting based on findings.

Microservice Observability, Part 2: Evolutionary Patterns for Solving Observability Problems

realkinetic • 0 implied HN points • 03 Jan 20
🕹 Technology Observability Microservices Data Collection Data processing Infrastructure
  1. Observability involves capturing various signals like logs, metrics, and traces to ask questions of systems without knowing those questions in advance.
  2. Challenges in observability can include agent fatigue due to multiple operational tools requiring unique agents, capacity anxiety with elastic microservice architectures, and the need for foresight in collecting necessary data.
  3. Implementing an observability pipeline can help in capturing wide events, consolidating data collection, decoupling sources and sinks, normalizing data schemas, and routing data to various tools for better observability in systems.

Microservice Observability, Part 1: Disambiguating Observability and Monitoring

realkinetic • 0 implied HN points • 03 Oct 19
🕹 Technology Microservices Observability Monitoring Architecture
  1. In microservice architectures, the conversation shifts from traditional monitoring to observability due to the complexity of multiple services interacting dynamically.
  2. In static monolithic architectures, monitoring is more straightforward with a single runtime and centralized telemetry.
  3. Observability offers deeper insights into system behavior by exploring new discoveries after the fact, providing more context and a higher level of granularity compared to traditional monitoring.