Building a Custom Elasticsearch Dashboard with Kibana

Building a Custom Elasticsearch Dashboard with Kibana is a game-changer for anyone looking to visualize and analyze their data.

You can create a custom dashboard using Kibana's intuitive interface and a variety of visualization tools.

With Kibana, you can easily drag and drop visualizations onto your dashboard, making it simple to create a tailored view of your data.

By leveraging Kibana's features, you can create a custom dashboard that meets your specific needs and provides valuable insights into your Elasticsearch data.

Understanding Elasticsearch is essential to creating an effective dashboard. Elasticsearch is a search and analytics engine that helps you store, search, and analyze large volumes of data quickly.

It's designed to handle high-volume data with high performance. The Elasticsearch stack includes various components such as nodes, indices, shards, and cluster health that need to be monitored closely.

Monitoring these components is crucial for gaining insights into resource utilization and system performance. The out-of-the-box visualization provides monitoring metrics per node.

Elasticsearch version 8.1 has specific visualization and metric specifications that differ from other versions.

A unique perspective: Elasticsearch Performance

Kibana is a powerful tool for visualizing data from Elasticsearch. We can exclude CSS and styles to enrich our charts and use the defaults provided with Kibana.

To consume our ingested or indexed documents visually, we can use Kibana instead of Elasticsearch Query and Postman, which is great.

Kibana provides a range of features to help us get started, including a search bar and filtering control. We can also create dashboards and save visualizations for later use.

Related reading: Elasticsearch with Kibana

To build a view that matches the index pattern, select “.monitoring-*.” This will allow you to create visualizations based on the data.

The first visualization you'll create is a line graph to show CPU utilization. Choose “.monitoring-*” as the data view and select a line graph as the visualization type. Use the formula max(node_stats.process.cpu.percent, kql=’node_stats.process.cpu.percent: *’) to calculate the CPU utilization.

To break down the CPU utilization by elasticsearch.node.name, simply add the field to the visualization. This will give you a clear picture of how CPU utilization is distributed across your nodes.

You can create similar visualizations for other metrics, adjusting the formula accordingly. For example, you might create a gauge to show memory usage or a time series chart to show disk usage.

Curious to learn more? Check out: Elasticsearch Spring Data

Our data is structured and in JSON format, which means we can't use traditional relational database systems (RDBS) queries.

We use Elasticsearch, specifically its Lucene search engine, to handle aggregations and create buckets for our data. This is particularly useful for fields like colors, where we can use a Date_Histogram to organize and analyze data.

With Kibana, we can easily create a time search bar to filter data by date, making it easier to visualize and analyze our data.

We have elastic search and all our documents reside in segments and shards. Our data is structured and is in JSON format.

Lucene provides aggregations to achieve complex queries, which we can't do with relational databases. We'll use Date_Histogram on the field "colors" to create buckets and have a Kibana TimeSearchbar to provide date.

Without Kibana, we can still do this using aggregation and filtering combined. Visit my article for Elastic Search aggregation.

Expand your knowledge: Elasticsearch Composite Aggregation

We can't use RDBS queries, so we're relying on Lucene for aggregations. This is a great example of how ELK dashboards can handle complex data queries.

To view this data, we'll go to the Dashboard page from the hamburger menu. It shows a list of Dashboards and a Create dashboard button that we'll use to build a new dashboard.

For instance, we can create a dashboard called "Car Sales" using the Create dashboard button. This dashboard will allow us to view our data in a visual format.

Our data is stored in segments and shards, and we're using Elasticsearch to handle it. This is a great way to store and query large amounts of structured data.

We can view numerous metrics, including pipeline failures, processor type, and processor time, using the Ingest Pipeline Monitoring dashboard. This is a great example of how ELK dashboards can provide valuable insights into our data.

Expand your knowledge: Elasticsearch Ingest Pipeline

Log analysis is a crucial aspect of data analysis and ingestion. It involves collecting and analyzing logs from various sources to gain insights into system performance, user behavior, and potential issues.

Logs can be gathered through various means, including Filebeat, Elastic Agent, and Logstash. These tools help collect logs from different sources and feed them into Elasticsearch.

The log analysis and analytics dashboard provides a broad overview of logs, offering a consolidated perspective across all aspects. It allows you to view metrics such as log source, log stream, and log users.

Here are some key metrics you can view in the log analysis and analytics dashboard:

By analyzing logs, you can identify patterns, trends, and potential issues, enabling you to take corrective action and improve system performance.

The Elastic SIEM detection engine is a powerful tool for analyzing cybersecurity-related data. It can be used for both SIEM and Elastic Endpoint data analysis purposes.

This dashboard presents key metrics such as observed hosts, top source IPs, and new alerts, giving you a comprehensive view of your security posture.

The Elastic SIEM detection engine is a useful method for analyzing all cybersecurity-related data stored within your Elastic Security setup.

Expand your knowledge: Elastic Search by Field

Threat detection is a crucial aspect of security, and having the right tools can make all the difference. Elastic's SIEM detection engine is a powerful tool for analyzing cybersecurity-related data.

This engine can be used for both SIEM and Elastic Endpoint data analysis purposes, making it a versatile solution.

The results of the Elastic SIEM detection engine can be displayed in a threat detection dashboard, providing valuable insights into observed hosts and top source IPs.

New alerts can also be tracked, giving you real-time visibility into potential threats.

A unique perspective: Elastic Cross Cluster Search

The Watcher History Dashboard is a powerful tool for monitoring your Elasticsearch deployment's configured watcher jobs. It extracts data from the system watcher history index, which is automatically generated when utilizing watchers.

This dashboard provides a detailed view of your watch events, allowing you to track the total number of watch events over time. The Watcher History Dashboard also visualizes watch executions, giving you a clear picture of how your watchers are performing.

By analyzing top conditions, you can identify areas where your watchers may be struggling or inefficient, enabling you to make data-driven decisions to optimize their performance.

Check this out: Elasticsearch Watcher

A secure analytics platform is crucial for threat detection and security. It's essential to collect, store, search, and analyze data all on one platform to build trust and streamline operations.

You can use Kibana to manage your team's access rights, share insights, and connect with other systems. This single platform approach eliminates the need to learn and manage disparate tools for different data sets and use cases.

Organizing dashboards and visualizations using Kibana Spaces is a great way to control access and content. You can invite users into specific spaces and give them access to specific features and content.

Role-based access control is a key feature of Kibana Spaces. It allows you to give the right access to the right people, integrating with industry standard identity management systems like Active Directory and LDAP.

To share insights and visualizations, you can use the sharing option that works for you. This includes embedding dashboards, sharing links, or exporting to PDF, PNG, or CSV files and sending as an attachment.

Here are the benefits of a secure analytics platform:

By implementing a secure analytics platform like Kibana, you can improve your threat detection and security efforts, while also streamlining your operations and building trust within your organization.

Elastic Stack Monitoring is a crucial aspect of ensuring your Elasticsearch cluster runs smoothly. Leveraging the built-in monitoring application can be quite advantageous.

To gain a comprehensive understanding of your cluster's performance, you should be aware that visualization and metric specifications can differ based on the Elasticsearch version. The dashboard example in this discussion is designed for version 8.1.

Monitoring the indexing latency is a vital metric to track, as it can indicate potential bottlenecks in your cluster. This metric is also included in the Elasticsearch Monitoring dashboard.

By utilizing this dashboard, you can view various metrics such as hosts with the most queries and active shards. This information can help you identify areas of improvement in your cluster's performance.

The Elasticsearch Monitoring dashboard has been created as an extension to the usual functionality, allowing you to tap into all available capabilities for monitoring your production cluster.

Discover more: Elastic Search Cluster

You can use Kibana to create dashboards that pull together charts, maps, and filters to display the full picture of your data. Rapidly create dashboards that enable deeper analysis.

Kibana provides a range of features to help you scale your analytics, including Discover, Kibana Lens, and Elastic Maps. You can explore, visualize, and analyze a ton of data with Kibana, no matter how much, what data type, or what data source.

To build trust on a single, secure data analytics platform, use Kibana Spaces to organize your dashboards and visualizations. This allows you to use role-based access control to invite users into certain spaces, giving them access to specific content and features.

A different take: Elasticsearch Analytics

Elasticsearch for Aggregations is a powerful tool for analyzing large datasets.

We can use Elasticsearch to see aggregations visually, which is a great alternative to using ElasticSearch Query and Postman.

Our goal is to consume our ingested or indexed documents to get insights from the data.

To achieve this, we can exclude the css and styles to enrich our charts and use the defaults provided with Kibana.

This approach allows us to focus on the aggregations and visualizations, rather than getting bogged down in the details of the underlying data.

Analyzing your data with machine learning (ML) capabilities can help you automatically detect anomalies, classify data into categories, and identify trends that lead you to root causes.

You can use Elastic's machine learning to transition straight into configuring the appropriate machine learning from your Kibana Lens visualization, making it easier to get started.

Kibana offers powerful analysis on any data from any source, including threat intelligence, search analytics, logs, and application monitoring.

To analyze with the power of ML, you can use Kibana's machine learning features, such as automatic anomaly detection, classification, and trend identification.

Here are some ways you can use Kibana's machine learning capabilities:

By leveraging Kibana's ML capabilities, you can gain deeper insights into your data and make more informed decisions.

You can scale for all your data with Kibana, handling both structured and unstructured data with ease.

Kibana's advanced analytical capabilities, such as machine learning and correlations, help you find relevant results that might otherwise go unnoticed.

Automate workflows to respond faster to critical scenarios like application downtime or security threats.

Kibana's unified visual UI lets you manage security settings, monitor the stack, and ingest and roll up your data from one place.

Here are some key benefits of using Kibana for faster solutions and decision making:

Having a robust Elasticsearch dashboard is crucial for data observability, allowing you to understand and explore your data.

With Elasticsearch, you can analyze and visualize potential security breaches, giving you a clear picture of your data's integrity.

You can also use Elasticsearch to share and take action on search analytics, improving your customer's search results and overall experience.

Elasticsearch's data analytics capabilities enable you to observe, protect, and search your data with ease, making it an essential tool for any organization.