Complete Course of Elasticsearch
Elasticsearch is a powerful, distributed search and analytics engine designed for handling large volumes of structured, semi-structured, and unstructured data in near real-time.
It is widely used for full-text search, log and event data analysis, monitoring, and business intelligence. As a core component of the ELK stack (Elasticsearch, Logstash, and Kibana), it plays a critical role in building scalable and efficient data pipelines and dashboards.
Learning Elasticsearch is essential for big data professionals because it is a highly sought-after skill in industries requiring fast data retrieval and analytics, such as e-commerce, cybersecurity, and DevOps.
Chapter 1: Introduction to Big Data and Distributed Search Frameworks
Lesson 1: What Is Big Data? Concepts, Challenges, and Opportunities
Lesson 2: Overview of Distributed Systems and the Role of Search Engines
Lesson 3: Key Big Data Tools and Frameworks (Hadoop, Spark, NoSQL) vs. Elasticsearch
Lesson 4: Advantages of Distributed Search in Data-Intensive Applications
Lesson 5: Real-World Use Cases: When and Why to Choose Elasticsearch
Chapter 2: Introduction to Elasticsearch
Lesson 1: What Is Elasticsearch and Its Importance in Big Data
Lesson 2: History and Evolution of Elasticsearch
Lesson 3: Core Features: Full-Text Search, Real-Time Analytics, Scalability, and Flexibility
Lesson 4: Elasticsearch Use Cases Across Industries (e.g., e-commerce, log analytics, security)
Chapter 3: Setting Up Elasticsearch
Lesson 1: System Requirements and Prerequisites
Lesson 2: Installing Elasticsearch on Local Machines and in Cluster Environments
Lesson 3: Basic Configuration (elasticsearch.yml, jvm.options)
Lesson 4: Setting Up Elasticsearch on Cloud Platforms (AWS, Azure, GCP)
Lesson 5: IDE and Tool Integration for Development (Kibana, Sense, Elasticsearch plugins)
Chapter 4: Elasticsearch Architecture and Core Concepts
Lesson 1: Cluster, Node, and Shard Architecture Explained
Lesson 2: Understanding Indices, Documents, and Data Types
Lesson 3: Data Modeling: From Mappings to Analyzers
Lesson 4: Replication, Sharding, and Fault Tolerance Mechanisms
Lesson 5: Essential Cluster Management Commands and APIs
Chapter 5: Indexing Data and Mapping Strategies
Lesson 1: The Indexing Process: How Data Is Stored in Elasticsearch
Lesson 2: Creating and Managing Indices Using REST APIs and CLI Tools
Lesson 3: Defining Mappings: Data Types, Field Attributes, and Custom Settings
Lesson 4: Analyzers, Tokenizers, and Filters: Customizing Text Analysis
Lesson 5: Bulk Indexing Techniques and Command-Line Examples
Chapter 6: Elasticsearch Querying Basics
Lesson 1: Introduction to the Elasticsearch Query DSL
Lesson 2: Full-Text Queries: match, multi_match, query_string, etc.
Lesson 3: Term-Level Queries: term, terms, range, and wildcard queries
Lesson 4: Boolean Logic and Compound Queries
Lesson 5: Practical Command-Line Query Examples
Chapter 7: Advanced Search Techniques and Aggregations
Lesson 1: Advanced Querying: Nested Queries, Parent-Child Relationships, and More
Lesson 2: Deep Dive into the Aggregation Framework: Buckets and Metrics
Lesson 3: Pipeline Aggregations and Transformations for Complex Analytics
Lesson 4: Geo-Search, Suggestions, and Completion Suggester Features
Lesson 5: Command-Line and API Examples for Advanced Searches
Chapter 8: Elasticsearch Command-Line Tools and API Operations
Lesson 1: Overview of Elasticsearch’s RESTful API and Endpoints
Lesson 2: Using cURL for Common Elasticsearch Operations
Lesson 3: Working with Official Elasticsearch Clients (Java, Python, Node.js)
Lesson 4: Scripting and Automation: Running Commands and Jobs via API
Lesson 5: Essential Command-Line Tools for Cluster, Index, and Query Management
Chapter 9: Performance Tuning and Optimization
Lesson 1: Performance Considerations and Best Practices
Lesson 2: Optimizing Indexing: Shard Strategies, Refresh Intervals, and Mappings
Lesson 3: Query Optimization Techniques and Caching Strategies
Lesson 4: Scaling Clusters: Load Balancing, Node Sizing, and Resource Allocation
Lesson 5: Monitoring Tools and Command-Line Techniques for Performance Benchmarking
Chapter 10: Securing Your Elasticsearch Cluster
Lesson 1: Security Fundamentals: Threats and Best Practices
Lesson 2: Configuring TLS/SSL for Encrypted Communications
Lesson 3: Implementing Authentication and Role-Based Access Control (RBAC)
Lesson 4: Using X-Pack Security (or the latest security modules) for Advanced Protection
Lesson 5: Command and API Examples for Setting Up and Testing Security Features
Chapter 11: Managing Elasticsearch Clusters
Lesson 1: Monitoring Cluster Health and Node Status via API Commands
Lesson 2: Adding and Removing Nodes: Scaling Your Cluster
Lesson 3: Index Lifecycle Management (ILM): Rollovers, Shrinking, and Deletions
Lesson 4: Backups, Snapshots, and Disaster Recovery Strategies
Lesson 5: Command-Line Tools and Scripts for Cluster Maintenance and Troubleshooting
Chapter 12: Integrating Elasticsearch with the Big Data Ecosystem
Lesson 1: Data Ingestion with Logstash: Pipeline Basics and Commands
Lesson 2: Visualizing Data Using Kibana: Dashboards, Visualizations, and Reporting
Lesson 3: Integrating with Apache Spark for Advanced Analytics
Lesson 4: Using Beats and Fluentd for Real-Time Data Collection
Lesson 5: Command-Line and API Examples for Seamless Integration
Chapter 13: Machine Learning and Advanced Analytics in Elasticsearch
Lesson 1: Overview of Elasticsearch Machine Learning Capabilities
Lesson 2: Anomaly Detection and Time-Series Forecasting Techniques
Lesson 3: Setting Up and Configuring ML Jobs via the API and CLI
Lesson 4: Advanced Data Analytics: Combining Search with Machine Learning
Lesson 5: Real-World Examples and Command Implementations
Chapter 14: Exploring New Features and Releases
Lesson 1: Overview of Recent Elasticsearch Releases and Roadmap
Lesson 2: New Features in the Latest Versions (improved search, security enhancements, ML updates)
Lesson 3: Upgrading Elasticsearch: Best Practices and Command-Line Strategies
Lesson 4: Enhancements in Aggregations, Query DSL, and Cluster Management
Lesson 5: Future Trends in Elasticsearch and Their Implications for Big Data
Chapter 15: Advanced Use Cases and Case Studies
Lesson 1: Real-World Applications: Log Analytics, E-Commerce Search, and Security
Lesson 2: Case Study: Building a Distributed Search Application with Elasticsearch
Lesson 3: Integrating Elasticsearch in Complex Data Pipelines
Lesson 4: Lessons Learned from Large-Scale Deployments
Lesson 5: Command Examples and Best Practices from Industry Leaders
Chapter 16: Troubleshooting and Debugging Elasticsearch
Lesson 1: Common Issues and Error Messages in Elasticsearch
Lesson 2: Log Analysis and Debugging Techniques
Lesson 3: Using the API and Command-Line Tools to Diagnose Cluster Problems
Lesson 4: Best Practices for Cluster Health Monitoring and Issue Resolution
Lesson 5: Community Resources, Tools, and Documentation for Troubleshooting
Chapter 17: Future Directions and Emerging Trends in Elasticsearch
Lesson 1: The Evolving Role of Elasticsearch in AI and Big Data Analytics
Lesson 2: Innovations in Distributed Search and Data Processing
Lesson 3: Integration with Cloud-Native and Containerized Environments (e.g., Kubernetes)
Lesson 4: Elasticsearch’s Contribution to the Future of Real-Time Analytics
Lesson 5: Preparing for Next-Generation Features and Use Cases
