Updated in May 2025.
This course now features Coursera Coach!
A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.
This hands-on course dives into Elasticsearch 8 and the Elastic Stack, helping professionals manage, analyze, and visualize large datasets. It starts with essential concepts like installation, core components, and working with HTTP and RESTful APIs. You’ll then explore data mapping and indexing using analyzers, tokenizers, and the Bulk API to efficiently process data at scale.
You’ll learn to build powerful search capabilities, progressing from basic queries to advanced techniques like filtering, fuzzy searches, and N-Gram queries. Real-world exercises help solidify these skills. The course also covers integration with tools like Logstash, Kafka, and Apache Spark, offering a broader understanding of data pipelines and ingestion strategies.
The final section focuses on performance tuning and cloud deployment to ensure efficient Elasticsearch management across environments. This course is ideal for data engineers, system admins, and IT professionals aiming to deepen their Elasticsearch skills. Basic knowledge of Elasticsearch and data handling 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.
