Data Analysis with Machine Learning

Machine learning has revolutionized the way we analyze and interpret data. With its advanced algorithms, it has become easier to uncover patterns and trends that were once hidden. One such tool that harnesses the power of machine learning is Kibana. Kibana provides a free Data Visualizer feature, enabling users to gain deeper insights into their data. If your data is stored in Elasticsearch and includes a time field, Data Visualizer can help you identify potential fields for anomaly detection. Anomaly detection is crucial in today’s rapidly evolving landscape as it allows us to detect unusual or suspicious activities that may indicate cyberattacks, infrastructure problems, or business issues.

By leveraging machine learning algorithms, Kibana’s anomaly detection feature can automatically spot anomalies in your data without the need for extensive human effort. This saves valuable time and resources while ensuring that critical events...

Technical requirements

As per the basic requirements, we assume that you have data ingested in the cluster and that Elasticsearch and Kibana are set up on the nodes of (any) environment (cloud or local).

Once Kibana is up and running, we can navigate to the Kibana home page and choose Try sample data | Other sample data sets and click on Add data for Kibana_sample_data_ecommerce dataset. We’ll be using this dataset to create the visualization on Lens in this chapter.

Understanding anomaly detection in time series data

Anomaly detection is the process of identifying the points in data that don’t fit the normal data behavioral patterns. To make this effective, we can automate the whole process. The important point to note here is that this process will be more efficient when the size of the data has increased. The Elastic Stack supports several data analysis use cases that use supervised and unsupervised machine learning, as follows:

• Anomaly detection
• Outlier detection
• Fraud detection
• Forecasting
• Language detection

Our main intention behind putting various techniques to use is to bring out the insights from the most normal-looking data. When we look into anomaly detection, we identify patterns and unusual behavior in the near real-time current and historical data. An unusual data point can be seen in the form of a high spike or very low data behavior, as shown here:

Figure 6.1 –...

Analyzing data with entity-centric analysis

The feature of Elastic’s machine learning entity-centric analytics allows you to analyze your data by utilizing algorithms for classification, outlier detection, and regression. It also enables you to generate new indices that include the results alongside your original data.

If you possess a license that includes machine learning features, you can create jobs for entity-centric analytics and view the outcomes on the Data Frame Analytics page in Kibana. The key features that help with this type of analysis are transforms and DataFrame analytics.

Let’s understand both.

Transforms

Transforms are specific implementations that are used to convert typical time series data into entity-centric data so that we can categorize the data into specific entities. We can do this by creating new indices with summarized data in them. Transforms work by helping us leverage their continuous mode functionality, where we can not only...

Setting up alerts

Kibana’s alerting capabilities go beyond the basics, incorporating support for machine learning rules. These rules allow you to schedule checks that can detect anomalies in one or more anomaly detection jobs.

Additionally, they can assess the health of a job based on specific conditions. When the conditions of a rule are met, Kibana creates an alert and triggers the associated action. This integration of machine learning with alerting in Kibana enhances the platform’s ability to proactively identify and respond to potential issues or abnormal patterns in data. By leveraging machine learning algorithms, Kibana empowers users to automate the monitoring process and improve overall operational efficiency.

With its advanced alerting features, Kibana provides organizations with a powerful tool for staying ahead of potential problems and optimizing their data analysis workflows.

The Kibana platform offers a range of powerful machine learning capabilities...

Summary

In this chapter, we understood the extensive features and capabilities that Kibana’s machine learning provides, thereby leveraging the big data in your systems to find some meaningful business insights to help the use case.

Then, we saw how anomalies can be deemed unusual behavior, even in the most normal-looking data. This helps us be aware of and predict potentially alarming events in the future. Moreover, we saw how the alerting mechanism in Kibana helps provide some great findings for the data analysis process.

With Kibana’s comprehensive suite of machine learning functionalities, you can streamline your data analysis workflow and stay proactive in identifying potential issues or anomalies in your datasets. Whether it’s identifying anomalies, monitoring job health, or leveraging other advanced analytics capabilities, Kibana enables users to unlock valuable insights from their data and drive meaningful business outcomes. In the next chapter, we...