The hottest Observability Substack posts right now

And their main takeaways
Category
Top Technology Topics

What It Actually Takes to Build a Data Pipeline System

SeattleDataGuy’s Newsletter • 718 implied HN points • 14 Jan 26
đź•ą Technology Observability
  1. A reliable pipeline system needs many core components—secure secrets and connection management, rich logging and monitoring, dependency tracking, execution routing, scheduling, data quality checks, pipeline definitions, and a usable UI—because missing any of these creates ongoing operational headaches.
  2. Operational practices like idempotency and easy backfilling, clear ownership, alerting/on-call routing, and environment isolation are critical so reruns don’t create duplicates and failures get handled quickly.
  3. Most teams should prefer existing tools unless they have a clear reason to build. If you do build, explicitly scope features—like compute routing or AI integrations—and plan for long‑term maintenance.

Preparing for a world where humans don’t write code

Brick by Brick • 72 implied HN points • 09 Feb 26
đź•ą Technology Observability
  1. AI agents will increasingly write production software autonomously, making human code writing and review a bottleneck and causing many current development practices to stop scaling.
  2. Trust should come from continuous validation, observability, scenarios, and invariants rather than relying on humans to read code, and code should be treated as disposable when generation is cheap and continuous.
  3. Organizations should create small AI-first teams that build real production systems under strict constraints (no human-written or human-reviewed code) to learn what breaks, then let successful practices spread while humans focus on intent, constraints, and outcomes.

AI Agents are here. Security isn’t ready.

Brick by Brick • 18 implied HN points • 20 Jan 26
đź•ą Technology Observability
  1. AI agents are becoming autonomous actors that plan, execute, and adapt across systems. Adoption is accelerating even though security practices are not yet ready.
  2. You can’t secure what you can’t find, so teams need new discovery and observability that capture reasoning traces, tool calls, and decision paths—not just inputs and outputs.
  3. Control depends on giving agents first-class identities and enforcing continuous, context-aware authorization so actions can be audited, constrained, and revoked without killing their autonomy.

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

A Song Of Bugs And Patches • 224 HN points • 15 Feb 24
đź•ą Technology Observability
  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.
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The 3 Pillars of Observability in Distributed Software Systems[System Design Sundays]

Technology Made Simple • 99 implied HN points • 19 Jun 23
đź•ą Technology Observability
  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
đź•ą Technology Observability
  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.

What is SaaS Observability?

Sarah's Newsletter • 139 implied HN points • 30 Aug 22
đź•ą Technology Observability
  1. SaaS Observability sheds light on the health of all data and automations in SaaS tools.
  2. Business teams should not need to rely on technical-heavy tools to ensure their systems are working correctly.
  3. Having bad data quality and anomalies in automations can impact business operations significantly and require constant monitoring.

How to make history with LLMs & other generative models

Leigh Marie’s Newsletter • 74 HN points • 21 Sep 23
đź•ą Technology Observability
  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
đź•ą Technology Observability
  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
đź•ą Technology Observability
  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
đź•ą Technology Observability
  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
đź•ą Technology Observability
  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.

It's not what DevOps mean!

Cloud Weekly • 2 HN points • 14 Apr 23
đź•ą Technology Observability
  1. Avoid having gatekeepers in your release cycle to reduce costs and improve organizational efficiency.
  2. Challenge bad processes and strive for daily value delivery to engineers and users.
  3. Embrace DevOps principles like automation, collaboration, and continuous testing for faster, high-quality software delivery.

Guidelines for Chaos Engineering, Part 1

realkinetic • 0 implied HN points • 06 Jul 20
đź•ą Technology 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 1: Disambiguating Observability and Monitoring

realkinetic • 0 implied HN points • 03 Oct 19
đź•ą Technology Observability
  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.

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

realkinetic • 0 implied HN points • 03 Jan 20
đź•ą Technology Observability
  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.

How we think about Data Pipelines is changing

The Orchestra Data Leadership Newsletter • 0 implied HN points • 08 Nov 23
đź•ą Technology 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.