All 31 chapters available

Stage 03 · NoSQL, Search & Application Data

Elasticsearch and OpenSearch

A complete search-platform course covering Elasticsearch and OpenSearch architecture, documents and mappings, text analysis, Query DSL, scoring and relevance, aggregations, ingestion, data streams, shard/segment internals, lifecycle management, snapshots, security, observability, ES|QL/PPL-style analytics, cross-cluster features, vector and hybrid search, RAG/AI search, performance engineering, migrations, and production operations.

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31chapters

155lesson paths

Intermediate → Advancedlearning level

Publishedcourse state

Coverage baselineCurrent Elastic Stack 9.x and OpenSearch 3.x concepts, including ES|QL, OpenSearch vector/neural/hybrid search, lifecycle automation, security, snapshots, and multi-cluster operations

Course brief

Build and operate search systems by understanding analysis, relevance, shards, segments, lifecycle, resilience, security, and modern vector retrieval—not by treating the engine as a JSON database.

This syllabus deliberately separates foundations, modeling, querying, internals, reliability, security, performance, operations, and production design so advanced material is not compressed into generic catch-all chapters.

By the end

You will be able to

Design mappings, analyzers, index/data-stream layouts, shard strategies, and ingestion pipelines for search and observability workloads
Write, profile, and evaluate lexical, filtered, aggregate, vector, semantic, and hybrid queries with relevance evidence
Operate Elasticsearch and OpenSearch clusters with lifecycle policies, snapshots, security, allocation, monitoring, upgrades, and disaster recovery
Understand where Elastic and OpenSearch diverge in management, security, query languages, AI/search tooling, and managed-service ecosystems
Build production search/RAG architectures with measurable quality, latency, freshness, resilience, capacity, and cost objectives

Complete syllabus

31 chapters · 155 lessons.

Every lesson path was reserved in the planned syllabus and is now linked because its lesson HTML is available. The sequence moves from foundations through advanced implementation, architecture, operations, reliability, security, tuning, and a production capstone.

Chapter 1

Search Engine Foundations, Elasticsearch vs OpenSearch, Deployment Models, and Lab Setup

5 lessons

Inverted-Index Search vs Relational/Document/Vector Databases: Workload Fit and Architectural Tradeoffs Published lesson · Chapter01/Lesson1.html

Elasticsearch and OpenSearch

Build and operate search systems by understanding analysis, relevance, shards, segments, lifecycle, resilience, security, and modern vector retrieval—not by treating the engine as a JSON database.

You will be able to

31 chapters · 155 lessons.

Search Engine Foundations, Elasticsearch vs OpenSearch, Deployment Models, and Lab Setup

Documents, Indices, Data Streams, Shards, Replicas, Nodes, and Cluster Architecture

Mappings and Field Types: keyword, text, Numeric, Date, Geo, Object, Nested, and Runtime Fields

Indexing and CRUD: Bulk APIs, Refresh, Concurrency Control, and Idempotent Ingestion

Text Analysis: Character Filters, Tokenizers, Token Filters, Analyzers, and Language Search

Query DSL Fundamentals: Term-Level, Full-Text, Boolean, Range, Exists, and Filtering

Relevance Engineering: BM25, Field Weighting, Function Score, Ranking, and Evaluation

Aggregations: Metrics, Buckets, Pipeline Aggregations, Cardinality, and Analytical Search

Dynamic Templates, Index Templates, Component Templates, and Schema Evolution

Data Streams, Time-Series Data, Logs, Metrics, and Append-Heavy Workloads

Ingest Pipelines, Processors, Enrichment, Failure Handling, and Data Quality

Aliases, Rollover, Reindex, Update/Delete by Query, and Zero-Downtime Index Changes

Shard Sizing, Routing, Allocation, Awareness, and Cluster Topology

Lucene Segments, Refresh, Merge, Translog, Flush, and Storage Internals

Heap, Caches, Circuit Breakers, Thread Pools, Backpressure, and JVM/Runtime Health

Search Execution, Profiling, Slow Logs, Async/Search-After/PIT, and Pagination

Index Lifecycle: Elastic ILM/Data Tiers and OpenSearch ISM

Snapshots, Restore, Searchable/Remote Storage Concepts, and Disaster Recovery

Elastic Security: TLS, Realms, Users/Roles, API Keys, DLS/FLS, and Audit

OpenSearch Security: TLS, Internal Users, Roles, Tenants, Backends, and Fine-Grained Access

Cluster Operations, Rolling Maintenance, Upgrades, Node Replacement, and Failure Recovery

Remote Clusters, Cross-Cluster Search, Cross-Cluster Replication, and Multi-Region Design

Operational Analytics Languages: ES|QL, OpenSearch PPL/SQL, and Query API Boundaries

Observability, Dashboards, Alerts, Tracing Search Workloads, and SLOs

Vector Search Fundamentals: Embeddings, Dense/Sparse Vectors, ANN, HNSW, Quantization, and Filters

Semantic, Neural, Sparse, and Hybrid Search in Elasticsearch and OpenSearch

RAG, AI Search, Inference Pipelines, Agentic Search, and Production Retrieval Architecture

Log Analytics, Security Analytics, Search Applications, and Workload-Specific Architecture

Migration and Interoperability: Elasticsearch ↔ OpenSearch, Reindexing, Clients, Plugins, and API Drift

Performance Engineering and Capacity Planning: Indexing, Search, Shards, Hardware, and Cost

Production Capstone: Build, Evaluate, Secure, Scale, Recover, and Operate a Search Platform