Elasticsearch & Kibana (ELK Stack) Course in Chennai

Module 1: Introduction to Elasticsearch and the Elastic Stack

• Elasticsearch overview: distributed search and analytics engine
• Elastic Stack components: Elasticsearch, Kibana, Logstash, and Beats
• Elasticsearch architecture: nodes, clusters, shards, and replicas
• Installing and running Elasticsearch and Kibana locally and on Docker

Module 2: Elasticsearch Data Model and Indexing

• Indices, documents, and fields: Elasticsearch data model
• Dynamic vs explicit mapping: field types and mapping configuration
• Index settings: shard count, replica count, and refresh interval
• Bulk indexing API for high-throughput data ingestion

Module 3: Search and Query DSL

• Full-text search: match, multi_match, and match_phrase queries
• Term-level queries: term, terms, range, and exists
• Boolean queries: must, should, must_not, and filter clauses
• Relevance scoring: TF-IDF and BM25 scoring explained

Module 4: Aggregations and Analytics

• Bucket aggregations: terms, date_histogram, and range
• Metric aggregations: avg, sum, min, max, and cardinality
• Pipeline aggregations: moving average and derivative
• Nested and composite aggregations for complex analytics

Module 5: Kibana – Visualisation and Dashboards

• Kibana interface: Discover, Visualise, and Dashboard overview
• Data Views: connecting Kibana to Elasticsearch indices
• Visualisation types: bar charts, pie charts, maps, and data tables
• Building operational and executive dashboards in Kibana

Module 6: Logstash and Data Ingestion

• Logstash pipeline: input, filter, and output plugin architecture
• Input plugins: file, Beats, Kafka, and JDBC sources
• Filter plugins: grok, mutate, date, and GeoIP transformation
• Output plugins: Elasticsearch, file, and Kafka sinks

Module 7: Elasticsearch in Production

• Index lifecycle management (ILM): hot, warm, cold, and delete phases
• Index templates and component templates for consistent mapping
• Security: TLS encryption, authentication, and role-based access
• Snapshot and restore for index backup and disaster recovery

Module 8: Capstone Project and Assessment

• Log analytics pipeline: Logstash ingestion and Elasticsearch indexing
• Full-text search application with Query DSL and aggregations
• Kibana operational dashboard build for a log monitoring use case
• Final assessment and course certification

Experience in the Industry Gain expertise from Elastic-certified engineers who have deployed Elasticsearch and Kibana for log analytics, application search, security analytics, and observability at enterprises managing terabytes of data daily.

Backgrounds at the Top Our Elasticsearch trainers have built Elastic Stack platforms at e-commerce companies, SaaS providers, and cybersecurity teams where Elasticsearch is the backbone for real-time search and analytics.

Clear & Effective Teaching Elasticsearch data model, Query DSL, aggregations, ILM, security, Kibana dashboards, and Logstash pipeline configuration are explained with practical search and log analytics examples.

Hands-On Learning Focus Students build Elasticsearch indices, write complex queries and aggregations, create Kibana dashboards, configure Logstash pipelines, and set up ILM through structured hands-on lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest Elastic Stack releases, Elasticsearch Serverless, vector search and semantic search capabilities, and Elastic Security and Observability solutions.