ODBIERZ TWÓJ BONUS :: »

Learning Elastic Stack 7.0. Distributed search, analytics, and visualization using Elasticsearch, Logstash, Beats, and Kibana - Second Edition Pranav Shukla, Sharath Kumar M N

Język publikacji: 1
Learning Elastic Stack 7.0. Distributed search, analytics, and visualization using Elasticsearch, Logstash, Beats, and Kibana - Second Edition Pranav Shukla, Sharath Kumar M N - okladka książki

Learning Elastic Stack 7.0. Distributed search, analytics, and visualization using Elasticsearch, Logstash, Beats, and Kibana - Second Edition Pranav Shukla, Sharath Kumar M N - okladka książki

Autorzy:
Pranav Shukla, Sharath Kumar M N
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
474
Dostępne formaty:
     PDF
     ePub
     Mobi

Ebook 29,90 zł najniższa cena z 30 dni

109,00 zł (-10%)
98,10 zł

Dodaj do koszyka lub Kup na prezent Kup 1-kliknięciem

29,90 zł najniższa cena z 30 dni

Poleć tę książkę znajomemu Poleć tę książkę znajomemu!!

Przenieś na półkę

Do przechowalni

Prezent last minute w ebookpoint.pl
The Elastic Stack is a powerful combination of tools that help in performing distributed search, analytics, logging, and visualization of data. Elastic Stack 7.0 encompasses new features and capabilities that will enable you to find unique insights into analytics using these techniques. This book will give you a fundamental understanding of what the stack is all about, and guide you in using it efficiently to build powerful real-time data processing applications.

The first few sections of the book will help you understand how to set up the stack by installing tools and exploring their basic configurations. You’ll then get up to speed with using Elasticsearch for distributed search and analytics, Logstash for logging, and Kibana for data visualization. As you work through the book, you will discover the technique of creating custom plugins using Kibana and Beats. This is followed by coverage of the Elastic X-Pack, a useful extension for effective security and monitoring. You’ll also find helpful tips on how to use Elastic Cloud and deploy Elastic Stack in production environments.

By the end of this book, you’ll be well-versed with fundamental Elastic Stack functionalities and the role of each component in the stack to solve different data processing problems.

Wybrane bestsellery

O autorach książki

Pranav Shukla is the founder and CEO of Valens DataLabs, a technologist, husband, and father of two. He is a big data architect and software craftsman who uses JVM-based languages. Pranav has diverse experience of over 14 years in architecting enterprise applications for Fortune 500 companies and start-ups. His core expertise lies in building JVM-based, scalable, reactive, and data-driven applications using Java/Scala, the Hadoop ecosystem, Apache Spark, and NoSQL databases. He is a big data engineering, analytics, and machine learning enthusiast.
Sharath Kumar M N did his master's in computer science at the University of Texas, Dallas, USA. He is currently working as a senior principal architect at Broadcom. Prior to this, he was working as an Elasticsearch solutions architect at Oracle. He has given several tech talks at conferences such as Oracle Code events. Sharath is a certified trainer Elastic Certified Instructor one of the few technology experts in the world who has been certified by Elastic Inc. to deliver their official from the creators of Elastic training. He is also a data science and machine learning enthusiast. In his free time, he likes playing with his lovely niece, Monisha; nephew, Chirayu; and his pet, Milo.

Packt Publishing - inne książki

Zamknij

Przenieś na półkę
Dodano produkt na półkę
Usunięto produkt z półki
Przeniesiono produkt do archiwum
Przeniesiono produkt do biblioteki

Zamknij

Wybierz metodę płatności

Ebook
98,10 zł
Dodaj do koszyka
Sposób płatności
Zabrania się wykorzystania treści strony do celów eksploracji tekstu i danych (TDM), w tym eksploracji w celu szkolenia technologii AI i innych systemów uczenia maszynowego. It is forbidden to use the content of the site for text and data mining (TDM), including mining for training AI technologies and other machine learning systems.