Practical Full ELK Stack: Elasticsearch, Kibana and Logstash

Install and configure Elasticsearch, Kibana, and Logstash; start on Windows, Ubuntu server, and Mac OS; explore deployment on Docker.

Install Elasticsearch on Windows by downloading the zip, extracting to a folder, and running from the terminal with the Java SDK and environment configuration; note the initial password and token.

Install Kibana on Windows by downloading the Elastic zip, extracting it, and starting Kibana. Generate and use enrollment token to connect to Elasticsearch and log in.

Install Logstash on Windows by downloading the integration package, extracting it, configuring the environment, and running input-output tasks via the terminal to start your aggregation.

Install Elasticsearch on Ubuntu Server by updating repositories, installing from the Elasticsearch website, enabling the service, and verifying access via a test search and port configuration.

Install Kibana on Ubuntu server, install Elasticsearch prerequisites, update repositories, and complete the Kibana installation. Enable and start the service, verify status, and set up tunneling for remote access.

Install logstash on Ubuntu server using sudo apt-get install logstash, then verify the service with systemctl status logstash and test its operation.

Install Elasticsearch on macOS using the pro package manager, update the local repository, run the Elasticsearch runtime, and test it on localhost.

Install kibana on macos locally and configure it with your elasticsearch setup. Start the service and test the local connection from your editor.

Install elasticsearch on docker by creating a virtual network and pulling the 6.12 image; run a container with port mapping and increase docker memory to access localhost with the password.

Install Kibana on Docker by pulling the Kibana image, running a container with a named host, configuring networks and ports, and managing credentials in the environment.

Configure Elasticsearch and Kibana for remote access by setting Elasticsearch to 0.0.0.0 and Kibana's server host, then open the firewall ports.

Learn to access and manage Elasticsearch indices and documents, including create, update, delete operations, and mappings. Upload datasets to Elasticsearch and explore sample queries, aggregations, and scroll queries on Kubernetes.

Learn to set up the Elasticsearch and Kibana stack across platforms, then use Kibana dev tools to run REST API queries against Elasticsearch.

Learn to perform CRUD operations on an Elasticsearch index via Kibana, including creating an index with shard and replica settings, querying, updating, cloning, and deleting indices.

Learn how to create, read, update, and delete Elasticsearch documents by indexing products, assigning IDs, inserting without IDs (auto-generated), updating fields, and deleting records, with practical examples.

Explore how Elasticsearch infers data types for index documents, define explicit mappings for fields like name, price, and quantity, and verify mappings with client-side input validation.

Insert documents in bulk into Elasticsearch using the bulk API, specifying index name and optional ids; if you omit an id, Elasticsearch generates one, and you can verify results.

Upload datasets to Elasticsearch via Kibana, choosing CSV or JSON formats, setting delimiters and mappings, importing into an index, and verifying data with get requests and counts.

Learn to perform Elasticsearch queries using range, boolean, and sort, with practical examples on indexed data, including size and from parameters, balance and gender filters, and location-based conditions.

Master aggregations in Elasticsearch by grouping data by state and gender, using terms on keyword fields to build breakdowns and analyze results without returning individual hits.

Learn to perform a scroll query in Elasticsearch, select fields like first name, last name, state, and balance, and group balance by state, testing compatibility before execution.

Define geopoint field type mappings in Elasticsearch for a cities index, input documents with longitude and latitude, and verify location data through index queries.

Build a development application using Elasticsearch as the data source, explore NoSQL capabilities, and implement a CRUD stack with Node.js and Python for users, roles, and a demo.

Create a dedicated Elasticsearch user and a custom role with monitor, read, and write privileges for selected indices via the Kibana security portal, then connect apps to Elasticsearch.

Learn to access Elasticsearch from a PHP app using Elasticsearch PHP SDK via composer. Authenticate with basic authentication and perform create, get, update, delete, and search on indices and documents.

Build a php and elasticsearch todo app by configuring a client and composer libraries, enabling create, read, update, and delete operations with pagination and status (in progress, done).

Build an ASP.NET Core api for Elasticsearch using Visual Studio Code, configure the Elasticsearch client and certificates, and implement a minimal API with CRUD on a to-do model.

Build a Node.js app with Express to access Elasticsearch using the Elasticsearch Node.js library. Implement CRUD operations, index creation, routing, and testing with Postman.

Develop a Python-based to-do app by integrating Elasticsearch four for Python and first API, performing create, read, and delete operations with swagger-driven endpoints.

Learn to collect data using lock start, set up an Elasticsearch user, and ingest data from files and databases into Elasticsearch.

Configure Elasticsearch user security by creating roles with privileges, assigning an index like Udemy, and setting up a user for Logstash access only.

Configure Logstash to ingest keyboard input, print output to the terminal, and forward data to Elasticsearch, with a demo configuration and verification in Kibana.

Ingest data from files into Elasticsearch with Logstash, define input files, skip headers, cast stock to integer and price to float, and monitor changes to send updates to Elasticsearch.

Ingest csv data from a folder into Elasticsearch via Logstash, locking and deleting processed files while mapping product, stock, and price to a document index, and verifying data.

Ingest data from a MySQL database into Elasticsearch with Logstash, using a MySQL connector, a to-dos table, and ID-based input filtering, then output to Elasticsearch and track the last ID.

Prepare data and build your first visual chart from data index, exploring bar, stacked bar, line/area, donut, table, heatmap, and region maps, then share canvases or dashboards with external systems.

Prepare and upload a dataset to Elasticsearch via Kibwana, configure delimiter and time format, map data types, and verify results to build a financial data report.

Create your first visual chart in Kibwana by selecting the finance data, applying calendar filters, and building a vertical stacked bar chart by product and country.

Convert data from the data index into a data view in Kibana and create visuals from Elasticsearch data, including bar stack with city on the horizontal axis and gender data.

Explore building a bar chart in Kibana using Lens, selecting finance data, configuring vertical and horizontal bars, segments, and axes to visualize units sold by product.

Learn to build a bar percentage chart in Kibana's visual library, selecting finance data and time range, configuring horizontal and vertical axes, and saving the chart.

Build bar stack visualizations in Lens using the visualize library, selecting finance as the data source, and configure horizontal and vertical stacks for sales and cross sales.

Learn to create a line chart in the visual library, using a finance timestamp as the horizontal axis, configure axes and units, and break down by country.

Create an area chart in Kibana Lens by selecting finance data for September 2013 to December 31, 2014, applying area options, stacking, and breaking down by country.

Learn to use Kibana Lens to build pie and donut charts from finance data. Use the visualize library to create, customize, and name charts like profit-based product visuals.

Learn to use the visual library to create a table visualization in Kibana, selecting time, country rows, product columns, and the profit metric.

Explore creating a heat map visualization in Kibana Lens by selecting finance, time, and product segmentation, configuring sums for sales, and interpreting color intensity.

Prepare population data for map charts in the elk stack by uploading a csv, validating mapping, creating a location field from latitude and longitude, and importing for visualization.

Prepare population data for region mapping, then build a lens region map by selecting ISO region keys and the population metric, and save the chart.

Develop interactive maps in the visualization library using a population dataset, add layers, customize tooltips and styling by country and population, then save the map visualization.

Learn to build metric aggregations in visualizations by selecting libraries and applications, then configure fields like total sale price and units sold, and save and update the chart.

Learn to build a cloud-based visualization with aggregation, selecting time from September one, 2013, aggregation type, and product-based metrics like unit count and units sold.

Create a canvas in Kibana to design reports by adding text, images, and visuals, include charts like heatmaps and maps, apply filters and time ranges, and export as a PDF.

Build and customize a dashboard by placing spots and visualizations using canvas, adjusting size and position, and selecting from maps, heatmaps, and other charts.

Learn how to share canvases and dashboards using a profile mechanism, download json data and zip files, run data scripts, and embed with an iframe on external websites.

Learn to collect and analyze data with the Elk stack, using beats like filebeat, metricbeat, packetbeat and auditbeat, and configure modules on Windows and Linux servers.

Explore the Filebeat overview, showing how it centralizes log data, formats it, and ships it to Elasticsearch across Windows and Ubuntu servers.

Set up a filebeat user and role in elasticsearch via kibana's stack management, granting admin privileges for filebeat access.

Install and configure filebeat on Windows Server, verify access to Elasticsearch and Kibana, download the Windows MSI or ZIP, and configure hostname, credentials, and certificates.

Learn to configure Filebeat on Windows with the IIS module, enable the module, configure log and error folders, connect to Elasticsearch and Kibana, and verify data streams and dashboard.

Configure filebeat on Windows 11 with the SQL Server module, enable the module, point to local SQL Server logs, and verify data flows into Elasticsearch and Kibana dashboards.

Set up filebeat on Windows 11 with the MySQL module, enable and configure the module, and verify data flows to Elasticsearch, with Kibana dashboards showing the school data.

Install and configure filebeat on Ubuntu server to ship data to Elasticsearch and Kibana, including repository setup, certificates, credentials, and enabling the filebeat module and service.

Configure filebeat on Ubuntu server with the nginx module, enable and start the service, then verify pipelines in Kibana and view nginx access and error dashboards.

Configure and install Winlogbeat on Windows to send Windows event logs to Elasticsearch or Logstash, using a Windows client or server in a single stack.

Install and configure Winlogbeat on Windows server to stream event logs to Elasticsearch and Kibana, verify connections, set credentials, and validate data pipelines and dashboards.

Install and configure metricbeat across Windows and Ubuntu, enabling modules to collect metrics and send data to Elasticsearch. Explore the resulting dashboards and visualizations in Kibana to monitor system metrics.

Learn to create roles and users in the Elasticsearch security section, assign monitor and admin policies, and set up metricbeat users for dashboards, pipelines, and ingest tasks.

Install and configure metricbeat on Windows Server 2022 to feed data into Elasticsearch and Kibana, enable the system module, set credentials, start the service, and verify pipelines and dashboards.

Install and configure metricbeat on Ubuntu server, verify connectivity to Elasticsearch and Kibana, enable nginx and system modules, and start the service to populate dashboards.

Learn how to install and configure packetbeat on ubuntu server to monitor and observe network traffic, configure roles, users, and dashboards in the elastic stack with elasticsearch and kibana.

Install and configure an applet on Ubuntu server to monitor surface and heartbeat, set up a role and user, and configure the stack management rules to feed data into Elasticsearch.

Deploy auditbeat on Ubuntu server and configure it to send data to Elasticsearch. Use Kibana to explore index data, data streams, and dashboards for monitoring.

Set up a three-node Elasticsearch cluster and a two-node Kibana deployment behind a load balancer, and configure Kubernetes-based routing for accessible, scalable access.

Configure a single-machine elk stack by editing the Windows hosts file, setting hostname and IPs, and defining ports for Elasticsearch, Kibana, and the load balancer; test connectivity before deployment.

Set up the first Elasticsearch cluster by editing elasticsearch.yaml, defining cluster name and ports, running the server, retrieving the password and token, and verifying cluster status.

Configure the second Elasticsearch node, generate a join token on the first node, enroll the second node with that token, and verify a two-node cluster is active.

Configure the third Elasticsearch node by setting the cluster name, generating and applying a join token, and adding the node to a three-node cluster; verify and prepare for load balancer.

Set up an nginx load balancer for a three-node Elasticsearch cluster, configure upstream servers, enable ssl with OpenSSL, and verify load balancing and cluster status.

Configure Kibana server to connect to an Elasticsearch cluster by setting hostname and port, applying an access token, and securing traffic with certificates and ssl via a load balancer.

Configure the second Kibana server to connect to the Elasticsearch cluster by setting the hostname, generating an enrollment token, and updating the load balancer and certificate settings; test the connection.

Configure an Nginx load balancer for the Kibana server cluster, with upstreams for Cubana one and Cubana two, SSL certificates, and a shared cookie; then verify cross-node access.