Logstash Fundamentals Masterclass (elasticsearch, filebeat)

Install elasticsearch, logstash, and kibana on Windows or Linux, ensure Java is installed, then start elasticsearch with the batch script and configure logstash, and adjust JVM memory if needed.

Learn how to configure a Logstash pipeline with input, filter, and output stages, and understand the skeleton and attributes of each section to manage log data flow.

Learn how Logstash uses input data sources and input plugins to ingest data, apply filter plugins to process it, and route results through the pipeline via output plugins.

Configure Logstash input with a file source by setting absolute paths, start position, and log type; learn how logs flow from file input through standard formats and port-based ingestion.

Logstash uses filters to normalize logs, including grok parsing, date and geoip for timestamps and location, mutate for field changes, with input plugins such as filebeat.

Explore Logstash output concepts by configuring Elasticsearch as an output, creating indices, and using common output plugins to route logs to disk, console, or monitoring services.

Learn the end-to-end log parsing workflow with Logstash, from file input and separator-based parsing to filter, mutate, and indexing in Elasticsearch, followed by Kibana visualization.

Complete the exercise to create a student records set with name, class, sex, and enrollment details, then insert these three guards into elastic sites and view the results.

Transform data by working with data arrays and formatting, using log stats and elasticsearch to process logs from an application server; apply configured log steps to real logs.

Define common data types like string, numeric, boolean, and objects, and learn why assigning the correct type to each field enables accurate aggregations and transformations in Logstash pipelines.

Explore how to format data and transform logs using Logstash, Grok, and regex to extract structured fields like timestamps, component, and log level from unstructured logs.

Explore how data aggregation in the Logstash fundamentals masterclass combines multiple events into a single view for analytics, using log data with start, processing, and completed states.

Process real apache logs with logstash using file input, grok patterns, and geoip to enrich data, then ingest into elasticsearch and view results in kibana.

Configure log stress to generate and ingest various logs into Elasticsearch, then view Windows event logs on Kiran to verify end-to-end log processing.

Explore the Elasticsearch beats family, including Filebeat, lightweight agents that collect data from various sources and send it to Elasticsearch with minimal resource use; contrast with Logstash processing.

Filebeat is the most commonly used log beat, available on many operating systems, that reads logs from files and uses backpressure to adapt its output to Logstash or Elasticsearch.

Metricbeat collects system level metrics such as cpu usage, memory, file system, and network for monitoring, and forwards them to the next component; internal modules gather metrics from various services.

Packetbeat captures network traffic between two nodes to analyze and monitor application performance, supporting multiple protocols, including DNS and ICMP.

Learn how heartbeat checks system uptime by collecting continuous status from running systems and publishing it. Feed this data into Elasticsearch and Logstash to monitor uptime and downtime.

Auditbeat audits user and process activity on Linux-based systems, similar to auditd, and helps monitor for security breaches by tracking file changes and other system activity.

Winlogbeat enables Windows admins to collect and forward Windows event logs for analysis, prioritizing critical and informative events within the data pipeline.

Explore functionbeat, a beat deployed as a function to collect logs from sources like aws lambda and cloudwatch, feeding them into the elasticsearch stack.

Explore community beats within the elastic stack, understanding their purpose, open-source nature, and how to exercise them to solve real use cases and select the right beat for optimal results.

Compare Beats and Logstash: Beats are lightweight, purpose-specific data shippers, while Logstash offers data processing, filtering, normalization, and plugins to transform and forward data to Elasticsearch.

Explore how to work with and manage Logstash plugins, including input, filter, and output plugins. Input ingests data, filter structures it, and output passes it to the next consumer.

Explore how Logstash input plugins serve as sources of events, with hands-on coverage of file input, Beats, DCP socket, and S3 stream integration to feed Elasticsearch.

Learn how filter plugins like grok, date, mutate, and multiline aggregation transform unstructured logs into structured data for Elasticsearch, including IP addresses, DNS logs, and geo location.

Explore Logstash output plugins, routing processed events to Elasticsearch, MongoDB, Kafka, or the console, and writing to CSV and other delimited formats.

Review the fundamentals of automated log analysis, install Elasticsearch and kirana, and configure Beats to ingest, filter, and view real-time logs using practical examples.