Wong Advanced Elasticsearch 7.0

A practical guide to designing, indexing, and querying advanced distributed search engines
1. Auflage 2019
ISBN: 978-1-78995-656-6
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 560 Seiten

No detailed description available for "Advanced Elasticsearch 7.0".

Autoren/Hrsg.

Wong, Wai Tak

Fachgebiete

• Mathematik | Informatik EDV | Informatik Digital Lifestyle Internet, E-Mail, Social Media
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Verteilte Systeme (Netzwerke)
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse

Inhaltsverzeichnis

Table of Contents - Overview of Elasticsearch 7
- Index APIs
- Document APIs

- Mapping APIs

- Anatomy of an Analyzer

- Search APIs
- Modeling Your Data in the Real World

- Aggregations Frameworks
- Preprocessing Documents in Ingest Pipelines
- Using ElasticSearch for Exploratory Data Analysis

- Elasticsearch from Java Programming

- Elasticsearch from Python Programming

- Using Kibana, Logstash and Beats

- Working with Elasticsearch SQL

- Working with Elasticsearch Analysis Plugins
- Machine Learning with Elasticsearch

- Spark and Elasticsearch for Real-Time Analytics
- Building Analytics RESTful Services

Über Autor(innen)

Wong Wai Tak:

Wai Tak Wong is a faculty member in the Department of Computer Science at Kean University, NJ, USA. He has more than 15 years professional experience in cloud software design and development. His PhD in computer science was obtained at NJIT, NJ, USA. Wai Tak has served as an associate professor in the Information Management Department of Chung Hua University, Taiwan. A co-founder of Shanghai Shellshellfish Information Technology, Wai Tak acted as the Chief Scientist of the R&D team, and he has published more than a dozen algorithms in prestigious journals and conferences. Wai Tak began his search and analytics technology career with Elasticsearch in the real estate market and later applied this to data management and FinTech data services.