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Duration 28 hours
Course Outline
Elastic Stack
- Introduction to the Elastic Stack
- Overview of the Product Ecosystem
- Enterprise Search Capabilities
- Core Elastic Concepts
- Elastic-Specific Terminology
- Nodes and Node Roles
- Scalability and Resilience Principles
- Use Cases: When and Where to Use, Pros and Cons
- Search Engine Internals
- Elastic Installation Process
- Pre-requisites and Setup Steps
- Configuration Files and Key Settings
- Elastic Cluster Architecture
- Cluster Discovery and Formation
- Master Election, Split Brain Scenarios, and Cluster Bootstrapping
- Cluster State and Shard Allocation Mechanisms
- High Availability Strategies and Backup Procedures
- Cross-Cluster Replication
- Index Templates and Mapping
- Security and Encryption Setup
- Operational Tuning and Best Practices
- Heap Size Management
- Shard Size Optimization
- Determining Shard Counts
- Hot-Warm-Cold Data Architecture
- Linux-Specific System Settings
- Index Rollover Strategies
- Configuring Cluster-Wide Settings
- Text Analytics Techniques
- Query DSL Fundamentals
- Normalizers and Tokenizers
Kibana
- Purpose and Value Proposition
- Kibana Terminology and Core Components
- Best Practices for Usage
- Installing Kibana
- Pre-requisites and Setup
- Configuration Files and Critical Settings
Logstash
- Purpose and Value Proposition
- Logstash Queue Management
- High Availability and Failure Design
- Diagnosing Logstash Issues
- Essential Filter Plugins and Application Scenarios
- Best Practices for Operation
- Installing Logstash
- Pre-requisites and Setup
- Configuration Files and Critical Settings
- Filebeat Integration
- Core Concepts of Filebeat
- Configuration Files and Settings for Filebeat
- Filebeat Installation and Directory Structure
- Processing Logic
- Additional Resources:
Production Best Practices and Methods to Reduce MTTR through Effective Troubleshooting
Requirements
A solid grasp of Linux commands and system administration principles. Familiarity with networking fundamentals, including IP addressing and port management. Practical experience with text editors and command-line interfaces. Knowledge of common data formats such as JSON, XML, and CSV. An understanding of basic data analytics and visualization principles. Familiarity with core concepts such as clusters, nodes, and storage systems.
Testimonials (1)
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