Working with Elasticsearch and the ELK Stack

In this Working with Elasticsearch and the ELK Stack course, you'll learn:

• Overview of Elasticsearch and Kibana
  • History and evolution of Elasticsearch and its place in the ELK (Elasticsearch, Logstash, Kibana) stack
  • Key concepts: clusters, nodes, shards, and replicas
  • Recent updates: Persistent Task Management and Index Lifecycle Management (ILM)
  • Elasticsearch architecture and how it scales
    • Horizontal and vertical scaling
    • Shard allocation
  • Integration of machine learning and security features
  • Best Practices:: Cluster health monitoring and performance optimization
• Install and Run Elasticsearch and Kibana
  • Installation on different platforms (Windows, Linux)
  • Introduction to the configuration files and important parameters
  • Security setup
    • Role-based Access Control (RBAC)
    • X-Pack security enhancements
• Data Ingestion and Indexing
  • Loading and Mapping Data into Elasticsearch
    • Overview of data ingestion strategies (manual loading, bulk API, Logstash, Beats)
    • Elasticsearch Ingest Nodes and Data Streams
    • Understanding and configuring index mappings (dynamic vs static)
    • Analyzers and tokenizers: how Elasticsearch processes text
  • Querying Data with Kibana
    • Discover, visualize, and create dashboards with Kibana
    • Exploring the Kibana query language (KQL)
    • Introduction to filters, aggregations, and visualizations
    • Best Practices: Building efficient visualizations and avoiding Kibana performance bottlenecks
• Advanced Features and Performance Optimization
  • Intermediate Elasticsearch Concepts
    • Understanding the indexing lifecycle (document update, versioning, deletion)
    • Index and analysis configuration: custom analyzers, filters, and tokenizers
    • Best Practice: Shard design and management
  • How Elasticsearch Works (Deep Dive into Internals)
    • Inverted index structure, how documents are stored, and term-based searching
    • The importance of shard allocation and how Elasticsearch distributes data
    • Best Practice: Shard design: when to split, merge, or resize shards
    • Understanding search relevancy and scoring
  • Utilizing Elasticsearch APIs with Python
    • How to interact with Elasticsearch via Python using the ElasticSearch-py client
    • Write Python scripts for indexing, querying, and bulk operations
    • Pagination, scrolling, and point-in-time searches for large data sets
    • Best Practices: Using bulk API for efficient data indexing
• Performance Tuning and Best Practices
  • Elasticsearch Performance Optimization
    • Key performance factors: disk, memory, and CPU utilization
    • Use of caching and understanding how Elasticsearch manages caches
    • Monitoring performance metrics (heap usage, garbage collection, search latency) with Kibana
    • Best Practices: Optimizing queries and aggregations to minimize expensive operations
  • Scaling Elasticsearch
    • Horizontal scaling: adding new nodes, managing clusters with multiple nodes
    • Vertical scaling: optimizing for hardware (SSD vs HDD, RAM, CPU cores)
    • Best Practices: Cluster health monitoring and resolving common bottlenecks (hot shards, unbalanced clusters)
  • Data Lifecycle Management and Archiving
    • Introduction to Index Lifecycle Management (ILM) policies
    • Rollups and how to manage historical data efficiently
    • Best Practices: Managing time-series data and designing indices for long-term storage
  • Security Best Practices
    • Authentication and role-based access control (RBAC)
    • Using X-Pack security features: securing Elasticsearch and Kibana
    • Best Practices: Securing data and access
  • Monitoring and Alerting with Elasticsearch and Kibana
    • Set up Kibana monitoring tools and alerts
    • Integrating Elasticsearch with monitoring solutions (Elastic APM, Prometheus, Grafana)
    • Best Practices: Efficient alerting and avoiding alert storms