About the course
Unstructured data is rich in insights but difficult to analyze using traditional data warehousing and analysis techniques. Document stores and document search techniques, borrowed from search engine technology and NLP fundamentals, can be used with great success to provide an analytic interface to this type of data. In this course ElasticSearch, one of the leaders in this field, will be explored in depth from installation, optimization, to its daily use. Enrollment in this course is open to all students and applies credit toward the Data Engineering track.
Upon successful completion of the course, students will be able to:
Evaluate the options for storing and processing unstructured data.
Understand the costs and risks of applying structured techniques to unstructured data.
Compare options to load unstructured data into ElasticSearch including rivers and the HTTP API.
Construct queries and aggregations against data stored in ElasticSearch.
Optimize data access ad integrity using ElasticSearch indices, analyzers, and transforms.
Understand the history of ElasticSearch and search index technology and compare it to other document stores and Lucene.
Write tests against ElasticSearch.
Utilize ElasticSearch’s proprietary machine learning odes to easily implement predictive analytics on ElasticSearch data.
Employ Kibana as a quick visualization tool set that can be built easily on top of ElasticSearch.
Read more about Business Analytics
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.