Category: Logs Analytics
Articles tagged Logs Analytics
One edit, every dashboard updated: managing Kibana observability at scale with Terraform
Define your golden-signals panels once in a shared HCL library and use for_each to generate every team's dashboard, with drift detection and git rollback built in.
Elastic z/OS ingest: five architectures for mainframe data
This field guide walks through the ingest architectures I've seen work in production, the data quality checks that decide whether your dashboards actually work, and the ECS mapping that makes mainframe data usable to the platform.
One OTLP endpoint, three teams, zero routing rules: Elasticsearch Streams AI Partitioning
Stop writing log routing rules upfront. See how Streams AI Partitioning reads your data, proposes child streams, and lets you set per-team retention in minutes.
SNMP Topology Data in Kibana: Collection to Canvas
The Network Topology plugin for Kibana provides a ready-to-deploy Logstash pipeline, a structured schema, and a topology view that shows what's connected to what.
Configure downsampling directly in Elastic Streams, no more JSON editing needed
Configure downsampling in Elastic Streams alongside retention and tiers, with a live preview and validation. No more editing ILM or lifecycle JSON.
Piping Hot: Bringing ES|QL to Your Grafana Dashboards Using the Elasticsearch Plugin
You can now write ES|QL queries in Grafana with the Elasticsearch plugin. Learn how to enable it and write pipe-based queries directly in the Grafana UI.
Migrate Logstash Pipelines from Azure Event Hubs to OTel Collector Kafka Receiver
Step-by-step guide to migrating Logstash pipelines from the Azure Event Hubs plugin to the OpenTelemetry Collector Kafka receiver.
From raw logs to system knowledge: the AI context layer observability is missing
A self-updating knowledge base built from your logs: services, dependencies, and failure modes, so your AI agents always know what they are looking at.
Fixing Elastic Streams processing failures without dropping data
When your Streams ingest pipeline breaks, failed documents land in the failure store, not the floor. Here's how to use those exact failures to fix your pipeline without re-ingesting from the source.
Connecting Cursor to Production Logs via the Elastic MCP Server
Learn how to connect Cursor to your Elastic APM data using the Elastic Agent Builder MCP server, so you can debug production errors and make UI decisions backed by real usage data without leaving your editor.
Exploring metrics from a new time series data stream in Discover
Discover helps you see and understand the metrics in a time series stream, with no manual work required. Once you see that your metrics data is flowing, you're ready to build dashboards, alerts, SLOs, and more.
How Streams Generates a Log Pipeline in Seconds
Streams generates a complete, tested log processing pipeline from a single click. Here's the two-stage mechanism behind it: deterministic fingerprinting, a reasoning agent that iterates against real data, and hard validation thresholds that enforce quality before you see the result.