ELK Stack Consulting & Services for Enterprises

Log pipeline architecture across Beats and Elastic Agent, Logstash pipeline development, ingest pipeline design, Kibana dashboards, spaces and RBAC, alerting design, and migration or consolidation onto ELK. For Elasticsearch cluster architecture, sharding, and ILM specifically, see our Elasticsearch consulting page — this engagement covers the stack around it.

Ingest pipelines run on the cluster and are simpler to operate — no extra tier, no separate scaling. They are the right default for straightforward parsing and enrichment. Logstash earns its place when you need persistent queuing, complex conditional routing, enrichment from external sources, or outputs beyond Elasticsearch. Many estates end up with both, and the mistake is running Logstash everywhere out of habit.

The Elasticsearch page covers the search and storage engine: cluster topology, shard sizing, mappings, ILM tiering, and query performance. This page covers the stack around it — how logs and metrics get collected, parsed, enriched, and presented. Most log platform problems live in the pipeline and in Kibana, not in the cluster, which is why we treat them as separate engagements.

Measure before scaling. The usual causes are grok patterns with catastrophic backtracking on a high-volume source, batch sizes too small for the output, and JVM heap sized without regard to persistent queue usage. Converting hot-path groks to dissect and tuning worker and batch settings often recovers several times the throughput. If the bottleneck is genuinely downstream indexing, we address the cluster instead of adding Logstash nodes that will just queue.

Yes, and this is one of the most common requests we get. The realistic pattern is selective: move high-volume operational logs where Splunk's ingest licensing hurts most, keep security use cases where Splunk is genuinely strong, and reassess after. We map saved searches and alerts to equivalents, run parallel ingest with side-by-side validation, and only then decommission the Splunk feed.

Spaces for boundaries, roles for permissions, and document- or field-level security where teams share an index but not all of its contents. The failure mode we correct most often is a single default space where every team's dashboards accumulate until nobody can find anything, with an admin role handed out to make the problem go away.