This course provides a hands-on approach to mastering Elasticsearch 8 and the Elastic Stack, enabling professionals to manage, analyze, and visualize large datasets. It begins with foundational concepts, covering installation and key components like HTTP, RESTful APIs, and Elasticsearch’s core features. Learners will advance through data mapping and indexing techniques, using analyzers, tokenizers, and the Bulk API to efficiently handle datasets of all sizes.
The course also explores Elasticsearch’s search capabilities, from basic queries to advanced techniques like filtering, fuzzy searches, and N-Gram queries. Practical exercises reinforce these skills, ensuring real-world application. Integration with Logstash, Kafka, and Apache Spark is covered, providing learners with a well-rounded understanding of Elasticsearch’s data integration capabilities.
The course concludes with performance optimization and cloud deployment strategies, ensuring participants can manage Elasticsearch in on-premise and cloud environments. Designed for data engineers, system administrators, and IT professionals, this course is ideal for those looking to advance their Elasticsearch skills. A foundational understanding of Elasticsearch and data management is recommended.
Applied Learning Project
Learners will build projects focused on real-world data management and search challenges, using Elasticsearch and related tools like Logstash and Kibana. They will apply skills in mapping, indexing, and searching data, working with the MovieLens dataset to practice importing, updating, and managing data. In later exercises, learners will integrate Elasticsearch with external data sources such as Kafka and Apache Spark to handle large datasets. Finally, they will create data visualizations in Kibana, enhancing their ability to analyze and interpret complex datasets, and driving actionable insights. These projects help solve authentic problems in data integration and visualization.
