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Observability · Courses
Elasticsearch: from discovery to expertise
Discover everything you need to know about deploying, configuring and administering an Elasticsearch cluster.
At your pace, through practice.By the end of this course you will be able to
- Install an Elasticsearch cluster and index documents
- Write efficient search and aggregation queries
- Manage index lifecycle (ILM) and backups
- Detect changes with alerts
- Design mappings, ingest pipelines, data streams and ILM policies
- Troubleshoot and size a cluster
- Implement vector and hybrid search
- Deploy and automate Elasticsearch (ECK, Terraform)
LEARNING PATH
Course curriculum
0Training overview1 lesson
1Fundamentals and architecture9 lessons
Installation, Lucene, nodes, shards, replicas and allocation.
- IntroductionLesson · ⏱ 6 min
- Elastic architecture and best practicesLesson · ⏱ 18 min
- InstallationLesson · ⏱ 4 min
- NoSQL databases, indices, shards and Apache LuceneLesson · ⏱ 11 min
- Index - best practicesLesson · ⏱ 11 min
- Kibana installationLesson · ⏱ 4 min
- Setting up the test environmentHands-on lab · ⏱ 28 min · Level 1/5
- Elasticsearch installationHands-on lab · ⏱ 15 min · Level 1/5
- Advanced distributed architectureArticle · ⏱ 4 min
2Cluster administration6 lessons
Cluster APIs, licensing, monitoring, troubleshooting and performance.
- Licensing: Elasticsearch license modelsLesson · ⏱ 6 min
- Testing queries and configurations with Kibana Dev ToolsLesson · ⏱ 9 min
- Retrieving cluster informationHands-on lab · ⏱ 56 min · Level 3/5
- License managementHands-on lab · ⏱ 28 min · Level 1/5
- Monitoring and troubleshootingArticle · ⏱ 3 min
- Performance and capacity planningArticle · ⏱ 3 min
3Data management and ingestion7 lessons
Indices, mappings, pipelines, data streams and ILM.
- IndicesHands-on lab · ⏱ 42 min · Level 3/5
- TemplatesHands-on lab · ⏱ 15 min · Level 3/5
- Mappings and text analysisArticle · ⏱ 5 min
- Ingestion and ingest pipelinesArticle · ⏱ 6 min
- Data streams and templatesArticle · ⏱ 4 min
- Index Lifecycle Management (ILM)Article · ⏱ 3 min
- Transforms and enrich: summarising and enriching dataArticle · ⏱ 6 min
4Security and access8 lessons
Users, roles, spaces, TLS, API keys and audit.
- Kibana spaces: hidden or disabled featuresLesson · ⏱ 8 min
- Security: users, roles and API keys (reserved and deprecated roles)Lesson · ⏱ 14 min
- External users and roles: Role MappingLesson · ⏱ 12 min
- Space managementHands-on lab · ⏱ 35 min · Level 2/5
- Users and rolesHands-on lab · ⏱ 42 min · Level 3/5
- API keysHands-on lab · ⏱ 15 min · Level 3/5
- Role mappingsHands-on lab · ⏱ 28 min · Level 4/5
- Advanced securityArticle · ⏱ 4 min
5Search and analytics6 lessons
Query DSL, ES|QL, aggregations, Kibana and vector search.
6Change detection1 lesson
Alerting, Watcher and anomaly detection.
7Backup and resilience3 lessons
Snapshots, SLM, disaster recovery and multi-cluster.
8Kubernetes and automation2 lessons
ECK, Terraform, Ansible and CI/CD.
10OpenTelemetry and Elasticsearch5 lessons
Compatibility, ingestion of OTel logs, traces and metrics.
- OpenTelemetry and Elasticsearch: compatibility and architectureArticle · ⏱ 6 min
- Ingesting OpenTelemetry logsArticle · ⏱ 6 min
- OpenTelemetry traces and Kibana APMArticle · ⏱ 4 min
- OpenTelemetry metricsArticle · ⏱ 4 min
- Lab: Send OpenTelemetry logs, metrics and traces to ElasticsearchHands-on lab · ⏱ 45 min · Level 3/5
9Best practices and final project2 lessons
Production operations and validation project.
90Certification preparation1 lesson
Preparation paths for the vendor's official certifications: unlimited practice exams, dashboard and readiness verdict.
LEARN BY DOING
Use cases & applications
Centralised log analysis
Ingest, index and analyse massive volumes of logs with the ELK stack.
Security monitoring (SIEM)
Use Elasticsearch for threat detection and security event analysis.
Analytical dashboards
Visualise real-time metrics with the Kibana interface.
Geospatial analysis
Store geographic coordinates and run proximity queries.
Machine Learning application
Detect anomalies in time series directly within the cluster.
What next?
Frequently asked questions
What is Elasticsearch?
A distributed search and analytics engine based on Apache Lucene.
What is an index?
A logical collection of JSON documents, split into shards (Lucene indices) spread across the cluster's nodes. It is not the equivalent of a relational database: no tables or joins, and one schema (mapping) per index.
How does a Shard work?
It is a partition of an index that lets data be distributed across several nodes.
What is Kibana used for?
It is the user interface for visualising and interacting with Elasticsearch data.
How do you secure the cluster?
By using Role-Based Access Control (RBAC) features and Kibana spaces.
What is the default query language?
You can use Query DSL through the API, or KQL (Kibana Query Language) in the interface.
Is it suitable for time series?
Yes, through Data Streams, which are optimised for time-based data.
How do you manage backups?
With Snapshots stored in a repository (NFS, S3, etc.) and managed by SLM policies.
Can an LDAP or Active Directory directory be integrated?
Yes, Elasticsearch supports Role Mapping with SAML, LDAP and other identity providers.
What is a Master node?
A node responsible for managing the cluster as a whole (index creation, node tracking).