Course Outline

Basics Elasticsearch:

  • Creating indexes: The most important aspects of creating indexes.
  • Data structure: Data types and their formats.
  • Usage Practices: Tips for Effective Usage Elasticsearch.
  • Index Templates: Advantages of using templates.
  • Index Management: Using aliases and index patterns.

Data mapping:

  • Mapping Capabilities: Optimize and benefit from proper data mapping.
  • Data organization: Data storage and the impact on performance.
  • Data volume: The role of mapping in data management.

Effective queries in Elasticsearch:

  • Query Engines: Building efficient queries.
  • Indexation techniques: Cache, inverted index, scoring, and others.

Clusters in Elasticsearch:

  • Cluster configuration: Selection of nodes and parameters.
  • Index management: Life cycles, fault tolerance, optimization.
  • Practical aspects of clusters: Case studies and advanced configurations.

Application Elasticsearch in practice:

  • Systems Integration: Connecting Elasticsearch to existing systems.
  • Big data processing: Massive reverse engineering.
  • Troubleshooting: Common challenges and how to solve them.
  • API Management: Using RESTful API to manage your cluster.

Data analyzers in Elasticsearch:

  • Analyzer Setup: Build advanced analyzers and test them.
  • Built-in tools: Overview of available data analysis tools.

Machine learning in Elasticsearch:

  • Anomaly detection: Comparison of temporal and population anomaly detection.
  • Anomaly Explorer Features: Overview of available features.
  • Categorization algorithms: Principle of operation of categorization algorithms.

Data backup and archiving in Elasticsearch:

  • Backup methods: Backup and restore to Elasticsearch.
 21 Hours

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