Introduction to ELK Tech Stack

ELK Stack, also known as the Elastic Stack is a powerful and versatile open-source toolset that has revolutionized the way businesses manage and analyze their data. ELK Stack seamlessly integrates these three robust components to offer a comprehensive solution for searching, analyzing, and visualizing large volumes of data in real-time. So, buckle up, for a comprehensive overview of the ELK stack and its components, which will be a great starting point for beginners.

What Is Log Analytics?

Log analysis refers to the process of examining and interpreting log files generated by various systems, applications, or devices. It involves analyzing these logs to gain insights, identify patterns, detect anomalies, troubleshoot issues, and make informed decisions.

Here are some common benefits of log analytics: 

• Troubleshooting and issue resolution
• Performance monitoring and optimization
• Security analysis and threat detection
• Compliance and auditing
• Business insights and decision-making

ELK stack plays an important role in achieving the above.

What is ELK Tech Stack?

The ELK stack, which is an acronym for Elasticsearch, Logstash, and Kibana, forms a powerful combination for centralized logging, log analysis, and real-time data visualization. An extended and robust elastic stack, it also incorporates Beats and Xpack, augmenting its capabilities.

Developed by Elastic, these open-source tools are widely utilized to streamline log management and gain valuable insights from real-time data visualization. 

Let’s understand the components of the ELK Stack individually.

Elasticsearch

Elasticsearch is a distributed, real-time search and analytics engine. It stores and indexes large volumes of structured or unstructured data, making it highly scalable and efficient for searching, querying, and analyzing data in near real-time.

Elasticsearch provides fast and flexible search capabilities, enabling users to perform complex searches across various fields and apply aggregations to explore and visualize data.

Logstash

Logstash is a data collection and processing tool. It’s basically a data processing pipeline that takes the data from multitude of sources and tosses it over to a visualization tool like Kibana or Elasticsearch. 

Logstash can also enrich data by applying filters, transformations, and enrichments before sending it to Elasticsearch. It allows ingestion, parsing, and transforming data from various sources and formats.

Together, Logstash and Elasticsearch serve as the foundation for data processing and storage, seamlessly feeding valuable insights into Kibana's powerful visualization and analytics capabilities. 

Kibana

Let’s now look at where Kibana fits into the ELK Stack model. To be defined in a single line, Kibana is a dashboard for analyzing and visualizing data. 

Now that you’ve started receiving data from Elasticsearch, what should be your next step? This is where a data visualization tool like Kibana jumps in. You can analyze and visualize any datalog with Kibana. It provides a user-friendly interface to interact with the data stored in Elasticsearch.

Beats

Beats serves as lightweight data shipper that send various types of data from different sources to Elasticsearch or Logstash for processing and analysis. 

They flawlessly integrate with the ELK stack, enhancing its capabilities by facilitating the collection and transmission of data from diverse sources, such as system logs, network packets, metrics, and audit logs.

It simply sends over data to Logstash or Elasticsearch that can be installed over the servers. There are multiple types of beats that have different tasks.

Other tools offer similar functionalities to beats and might be better suited for specific use cases. For example, Fluentd, RSyslog, Splunk Universal Forwarder, Logagent, NXlog, Filestash, etc.

How Does the ELK Stack Work?

The components of the Elastic Stack – Beats, Elasticsearch, Kibana, and Logstash – collaborate seamlessly to ingest, process, store, and visualize data. Here's a simplified workflow illustrating the same:

Data Collection with Beats

• Beats collects data from various sources such as logs, metrics, or network packets. 
• Sends the collected data directly to Logstash.

Data Processing with Logstash

• Logstash receives data from Beats and applies filters, transformations, and enrichments to the data, ensuring its compatibility and consistency.
• Processed data can be sent to Elasticsearch or to other systems for further processing or storage.

Data Indexing and Storage with Elasticsearch

• Elasticsearch receives data from Logstash, indexes and stores the data in a distributed manner, ensuring high availability and scalability.
• Indexed data becomes searchable, enabling fast and efficient retrieval using Elasticsearch's powerful search capabilities.

Data Visualization with Kibana

• Kibana acts as the visualization layer. It connects to Elasticsearch and provides a user-friendly interface for data exploration. Users can create interactive dashboards, visualizations, and reports using a rich set of tools and templates.
• Kibana enables real-time monitoring, data discovery, and analysis, allowing users to gain valuable insights from the indexed data.

As the complexity of your application increases, you might end up using additional components to enhance the resiliency of your application, such as Kafka, RabbitMQ, and Redis, etc.

Now that you have a clear understanding of how the ELK Stack works, the next crucial step is to install and configure the stack appropriately.

ELK Stack Plugins & Integrations

The ELK stack can hold an unlimited number of integrations, as long as you have the resources to support them. You can use Elasticsearch API to create custom ELK Stack integrations apart from the existing plugins & integrations.

Plugin/ Integration NameDescriptionEnhanced TableA Kibana plugin that provides two visualizations: Enhanced Table and Document Table. Enhanced Table has enhanced features like computed columns, filter bar, and pivot table.ElastAlertA rule-based alerting system for Elasticsearch. ElastAlert can be used to send notifications, execute scripts, or take other actions when specific conditions are met.Kibana DashboardsAllows you to create and share dashboards. Dashboards are a great way to visualize and analyze your data.LogTrailAllows you to view, analyze, search, and tail log events in realtime. LogTrail is a great way to troubleshoot problems and investigate security incidents.WazuhWazuh helps organizations identify and respond to security incidents in real-time by monitoring and analyzing logs, events, and file integrity, enhancing overall cybersecurity.

You can also integrate ELK Stack for collecting data from various data sources like Azure Monitor, Amazon Cloudwatch, Google Cloud Platform, Sumo Logic, etc. 

Check complete list of Elastic integrations & plugins.

ELK Stack v/s Grafana

Kibana and Grafana are potent data visualization tools but have different origins and purposes. Let's find out how do they stack up against each other so you can figure which one fits your needs better.

• Kibana (the ‘K’ in ELK Stack) was built on top of the Elasticsearch stack, famous for log analysis and management. Grafana was created mainly for metrics monitoring, supporting visualization for time-series databases.
• The ELK stack is designed to be scalable, storing and analyzing large amounts of data. Grafana is not as scalable as the ELK stack, so it may not be a good choice for organizations that need to store and analyze large amounts of data.
• If you’re using Elasticsearch as your primary data source, Kibana might be the right choice for advanced query & analysis capabilities.
• Elasticsearch is a powerful search engine that can be used to search and filter data quickly and efficiently. Grafana does not have a built-in search engine, so you will need to use a third-party search engine to search your data.
• If you self host Grafana, it’s easier to maintain than Elasticsearch. Maintenance is an overhead with Elasticsearch.

Pricing Comparison:

• Both Grafana & Kibana start free. Moving up, Grafana offers cloud-based service with different plans & features. The cloud service has three plans: Free, Pro, and Advanced. The Free plan has limited usage and features, such as 50 GB of logs, 10k metrics, and 500 k6 virtual user hours per month. Pro plan costs $29 per month plus usage, and includes more usage and features, such as 100 GB of logs, 20k metrics, 1000 k6 virtual user hours per month, and one enterprise plugin. 
• ELK stack also offers a cloud-based service called Elastic Cloud, which has four plans: Standard, Gold, Platinum, and Enterprise. The Standard plan costs $95 per month. It includes core Elastic Stack features, security alerting, centralized ingest and agent management, malware prevention, host data collection, case management, APM observability apps, logging, metrics, enterprise search apps for websites, mobile apps, and workplace searches.

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Conclusion

In conclusion, the ELK Stack proves to be an invaluable tool for organizations seeking powerful log analysis and real-time data visualization capabilities. So, let the ELK Stack guide your data journey, turning log chaos into valuable wisdom, because in the world of data, it's all about finding the ELK-usive truth. 🙃

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