Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.43 GB | Duration: 86 lectures • 8h 14m
ELK 8.x | Learning ELK Stack (Elasticsearch, Kibana, Logstash and Beats) by project examples
What you'll learn
Learning how to deploy Elasticsearch and Kibana in various environment platforms
Administering and managing Elasticsearch and Kibana
Developing programs for Elasticsearch and Kibana
Building data visualization with Kibana
Collecting data using Logstash and Beat
Implementing high availability for Elasticsearch and Kibana
Having basic operating systems such as Windows, Linux and macOS
Having basic any programming language
Welcome to Full ELK Stack Bootcamp!
This bootcamp is designed for any developer and IT admin who want to deploy Elasticsearch, Kibana and Logstash, and develop application based Elasticsearch.
This bootcamp focuses deploying and developing for ELK stack. The bootcamp consists of the following topics
Installing Elasticsearch and Kibana on Windows, Linux and macOS
Accessing Elasticsearch REST API
Elasticsearch Document REST API Development
Collecting Data with Logstash
Data Visualization with Kibana
Collecting Data with Beats
High Availability (HA) for Elasticsearch and Kibana
Firstly, we learn how to install Elasticsearch and Kibana on Windows, Linux and macOS so you will have experiences on various platform for installation process.
Next, we learn a basic Elasticsearch REST API. This is an important thing to understand how to access Elasticsearch server from REST API requests.
We also learn how to collect data from file and database using Logstash. Another method to collect data is using Beats. We use Beat services such as Filebeat, Winlogbeat, Metricbeat, Packetbeat, Heartbeat and Auditbeat on Windows Server and Ubuntu Server.
Elasticsearch provides API SDK in order to build applications with Elasticsearch as database. Elasticsearch could be NoSQL database. In this bootcamp, we build application using PHP, ASP.NET Core, Node.js and Python.
After collected data, we can visualize the data using Kibana. We explore some charts and create dashboard on Kibana.
Last, we deploy Elasticsearch and Kibana for high availability scenario. For demo, we use three Elasticsearch servers and two Kibana servers. We also implement a load balancer using Nginx.
Who this course is for
Anyone who wants to learn Elasticsearch and Kibana
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