ELK Stack: Elasticsearch, Logstash & Kibana Bootcamp

Learn to implement distributed tracing with the elk stack across microservices, using Sleuth for trace IDs, Zipkin for timing, and Logstash to feed Elasticsearch and Kibana.

Configure logstash with elasticsearch and kibana, compare Windows and Docker setups, and learn to run the elk stack in Docker for memory efficiency.

Enable distributed system logging by deploying a Zipkin server with Docker, integrating Logstash and Logback, and configuring Spring Cloud Sleuth for services like customer, order, and orchestrator.

Configure and run elk stack components, track requests with Zipkin, and visualize logs in Kibana while standardizing application names. Explore migrating MySQL data to Elasticsearch via Logstash pipelines.

Migrate data from MySQL to Elasticsearch by configuring Logstash with a JDBC input, defining a sample database and select query, and verifying via stdout JSON.

Explore how Docker Compose orchestrates a three-container scrum master application, linking Postgres, Java backend, and Angular UI with defined dependencies and a YAML file.

Deploy a postgres database using docker-compose yaml, defining an image, container name, ports, and environment variables, with a focus on volumes for persistent data.

Configure the angular front-end container in docker-compose by defining the scrum_ui service, its container name, port, and dependencies, and bind the docker volume to the postgres db.

Triggered docker build demonstrates testing a docker-compose deployment by launching containers, resolving dependencies, and validating backend and frontend deployment with integration tests, while highlighting pruning and container names.

Deploy a WordPress site locally using Docker Compose to run WordPress and MySQL containers with a persistent volume and exposed ports; compare with basic Docker commands.

Declare entrypoints and environment variables to initialize a MySQL container in Docker, including root password, user, and database, with proper volume and port mappings.

Create and attach a docker volume, run a mysql container with the volume, set environment variables, map ports, and name the container for persistent data and easy management.

Deploy a WordPress container using docker run with mysql as the database, configuring environment variables, volumes, and ports, then access the WordPress site in the browser.

Learn to deploy a WordPress site using Docker Compose by configuring images, versions, ports, and dependencies, then run and access the site via the compose up workflow.

Explore distributed tracing for microservice architectures by collecting and aggregating logs from multiple services with the ELK stack, including Logstash, Elasticsearch, and Kibana, plus Zipkin and Sloop.

Explore distributed tracing across microservices using Sleuth and Zipkin, with Logstash and a TCP socket flow to propagate trace IDs, span IDs, headers, and timestamps through a synchronous end-to-end process.

Install and run the Elk stack with docker, download Elasticsearch, Logstash, and Kibana, configure Logstash input on port 5044 with SSL false, and route outputs to Elasticsearch at 9200.

Deploy a zipkin server and implement distributed tracing with Spring Cloud Sleuth, configure logback and logstash, and wire a gateway to trace customer and order services.

Demonstrate logstash in action within a full ELK stack workflow using docker-compose, from configuring services and gateway requests to viewing logs and Zipkin traces in Elasticsearch and Kibana.

Explore how service logs in Kibana reveal distributed logging across the system, showing order service app name and threadpool task details as logs are recorded.

capture the case study flow of transferring data from MySQL to the elk stack using logstash and a jdbc plugin, triggering queries and migrating legacy data.

Learn to migrate data from MySQL to Elasticsearch using Logstash, including setting up a sample database, configuring JDBC, and validating data flow with JSON lines in stdout.