Elastic Stack Fundamentals: Elasticsearch, Kibana, and Vector Search

In a data-driven world, the ability to find and analyze information at scale is a critical skill for developers and data engineers. Organizations need to process massive amounts of data and return relevant results instantly, whether for application search or log analysis. This course provides a comprehensive path to mastering the Elastic Stack, moving from basic indexing to implementing advanced features like semantic search and piped queries. You will learn how to architect search solutions that are both scalable and intelligent, utilizing the latest advancements in search technology. What you'll learn: - Understand the foundational architecture of Elasticsearch nodes, clusters, and indices. - Master ES|QL to perform advanced data filtering and aggregation with a piped syntax. - Configure Logstash pipelines to ingest and transform data from various sources into search-ready formats. - Implement vector search and NLP models to enable intelligent, semantic search capabilities. - Design visual monitoring solutions and data dashboards within the Kibana interface. - Practice optimizing search performance using modern Lucene features and efficient mapping strategies. The course begins with essential terminology and cluster setup before progressing into advanced data modeling, real-time analytics, and machine learning integration. You will read through detailed explanations of search logic and data ingestion workflows to gain a deep understanding of the ecosystem. This course is ideal for beginners and technical learners looking to build modern search and observability infrastructure from the ground up without any prior experience in search technologies. Start building your own high-performance search solutions today through these comprehensive written lessons.