Mastering Kibana: ELK Stack Visualization & Analytics

Explore Logstash, a real-time data pipeline that ingests data from various sources into Elasticsearch for Kibana visualization. Learn its three-stage pipeline: input, filter, and output, with normalization, enrichment, and parsing.

Learn to install Logstash, ensure Java is accessible, and set environment paths. Create a basic Logstash pipeline with input and output, testing data transfer toward Elasticsearch.

Create Logstash configuration files to read logs from a file, use grok to extract fields, filter for 200 responses, and write results to a file for elk stack visualization.

Read structured logs from a Python app with filebeat that reads app.log, then send them to Logstash and forward to Elasticsearch for visualization in Kibana.

Learn to read structured logs from a Python application using Filebeat, forward them to Logstash, and index them in Elasticsearch for visualization in Kibana.

Learn to ingest Windows system logs into Elasticsearch via Logstash, using Winlogbeat and Winlogon beat, with configurations for beats input, Elasticsearch output, and viewing logs in Kibana.

Explore how to list and inspect Elasticsearch indices, view mappings, and analyze fields in Kibana using data views, Discover, and DevTools.

Explore Lucene and Kql in Kibana, mastering basic to advanced search techniques. Apply these queries to the Kibana eCommerce dataset to filter and analyze data with autocomplete and range filters.

Learn to save searches and perform time-based analysis in Kibana, using filter button or queries, with filters like taxable total price >= 100, and auto refresh on dashboards and visualizations.

Create scripted fields and runtime fields in Kibana to perform dynamic calculations without modifying your source data, using painless scripting for discounted and tax inclusive prices.

Explore and search data in Kibana using Discover, filters, and Kibana query language on sample flight data, save searches, and visualize trends with a date histogram.

Explore Kibana lens for visual data exploration with a drag-and-drop interface, building bar and pie charts from the e-commerce sample dataset, applying filters, and saving to dashboards.

Explore creating interactive Kibana visualizations with Vega using JSON syntax, enabling data transformations and custom charts from area to bar, with tooltips and order-date based time insights for total sales.

Learn to use timeline in Kibana for time-series analysis of the Kibana sample e-commerce data, applying a moving average and an offset to compare current with previous week sales trends.

Build a multi-visualization Kibana dashboard using lens and Vega-lite to analyze flight data, including average ticket price by airline, flight count over time, and delays over time.

Create a multi-visual Kibana dashboard with lens and Vega-lite to display average ticket price by airline, plus flight counts and delays over time.

Design interactive Kibana dashboards by integrating multiple index patterns from e-commerce and weblogs, create unified data views, and build line, bar, and pie visualizations to monitor website traffic and errors.

Explore advanced interactive dashboards in Kibana by creating drill downs to navigate between dashboards and optimize performance for large datasets, linking from Sales Insights to an ecommerce dashboard.

Learn to embed Kibana dashboards in external applications by copying embed codes and using iframe in a Streamlit Python app, with configurable items like query, time filter, and filter bar.

Learn to integrate Kibana alerts with external systems by configuring email connectors and indexing alert data into Elasticsearch using the sample eCommerce dataset.

Configure Kibana alerts and thresholds in the Elastic Stack using email and index connectors to monitor data in real time and notify when conditions are met.

Set up watcher threshold alerts in Elasticsearch to monitor high sales and trigger actions, then export Kibana dashboards to PDF for reporting.

Create monitoring alert rules in Kibana by configuring an index threshold rule to alert when average flight delay exceeds 60 minutes in the last 30 minutes, with email notifications.

Learn to secure Kibana dashboards by using spaces to create separate environments, assign permissions, and copy dashboards between spaces for tailored team views.

Explore role-based access control in Kibana with built-in security features. Create sales manager and sales analyst roles with index and Kibana privileges, assign users, and test access.

Secure Kibana with RBAC and spaces by creating a Flight Ops space, a read-only Flight Ops analyst role, and deploying the Flight Analytics dashboard.

Explore Kibana's machine learning UI to detect anomalies, perform data frame analytics for regression and classification, and manage trained models with data visualizer and AI Ops lab.

Explore anomaly detection with Elastic ML in Kibana using the Kibana Sample data eCommerce to spot unusual patterns in sales transactions, with time series analysis, forecasting, and anomaly explorer.

Configure multi-metric machine learning jobs in Kibana to detect anomalies and trends in time-series e-commerce data, leveraging gender, city, and manufacturer splits, 15-minute buckets, and forecasts.

Learn to set up multi-metric anomaly detection and forecasting in Kibana ML using sample flights data, including flight delay minutes, distance, and ticket price, split by carrier.

Explore ingesting, exploring, and visualizing Kibana data from logs using file uploads, dashboards, and lens visualizations to analyze bytes, geo, status codes, and top browsers.

Add multiple visualizations to a Kibana dashboard, including gauge, heat map, table, and vertical bar charts, using sample data logs and metrics like average memory, sum of bytes, and counts.

Create interactive Kibana dashboards with drilldowns and filters to explore status codes, traffic, and top urls. Then set up index-threshold alerts for monitoring to email when traffic exceeds thresholds.

Learn to secure Kibana with RBAC and spaces by creating a space for the web analytics dashboard, defining roles, and assigning read and admin privileges.

Set up machine learning driven anomaly detection in Kibana using sample weblog data, choose a single metric of bytes, bucket span 15 minutes, then forecast and export the report.