Elasticsearch
11 Modules ~30 hours Beginner → Advanced
Master Elasticsearch end-to-end: from "what is an inverted index?" to running production clusters, ingesting at scale, and building dashboards in Kibana. Covers OpenSearch where it diverges.
Course roadmap
| # | Module | Status | Topics |
|---|---|---|---|
| 0 | Setup & ELK in Docker | Plan ready | docker-compose ELK stack, first index, hello-world search |
| 1 | Indexing & Mapping | Plan ready | Field types, analyzers, dynamic mapping, index templates |
| 2 | Query DSL | Plan ready | match, term, bool, range, nested, script queries |
| 3 | Search Relevance | Plan ready | TF-IDF, BM25, function_score, boosting, highlighting |
| 4 | Aggregations | Plan ready | bucket vs metric, terms, date_histogram, pipeline aggs |
| 5 | Ingest Pipelines | Plan ready | Beats, Logstash, ingest nodes, processors, enrich |
| 6 | Kibana | Plan ready | Discover, Lens, dashboards, saved searches, alerts |
| 7 | Cluster Management | Plan ready | Sharding, replicas, shard allocation, master/data/coord nodes |
| 8 | Performance Tuning | Plan ready | Heap sizing, refresh interval, force-merge, hot/warm/cold |
| 9 | Security & Monitoring | Plan ready | TLS, RBAC, Watcher, Stack Monitoring, audit logs |
| 10 | Capstone | Plan ready | Build a log analytics platform: Filebeat → ES → Kibana with alerts |
What's available now
Curriculum plan published. Content rolling out 2026 H2.
Related courses:
- PostgreSQL — relational complement to ES
- Kubernetes — operate ES on K8s
Last updated
2026-05 — Curriculum plan published.