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Quick Start Guide

This guide will help you get up and running with the StarTree ThirdEye Community Edition on a local Kubernetes environment in a few minutes.


For differences between StarTree ThirdEye Community Edition vs Enterprise Edition, visit: Link

Watch this video to learn how to install StarTree Community Edition


Please follow the Community Edition installation guide to install the ThirdEye cluster.

Exploring Alerts and Anomalies with StarTree ThirdEye Community Edition


Sample Dataset

This Helm chart also includes a helper job to onboard a sample dataset into Pinot and explore anomalies and alerts using ThirdEye.

The helper job simulates a retail-order tracking event dataset from a global eCommerce company. With this sample, we can explore how the eCommerce company's business operations or data analytics team could monitor and track the order events with ThirdEye.

Apart from tracking, we can also:

  • Learn about patterns in the order events dataset. For example, if there are spikes, drops, seasonality, or fraudulent activity
  • Identify root causes for the patterns by drilling down into different dimensions of the dataset
  • Take necessary actions, such as resolving the fraudulent activity to deliver the best customer experience and prevent monetary losses in the order check-out process

The Community Edition Helm chart runs a Kubernetes Job called quickstart-helper that onboards a sample dataset for you to analyze. The job first checks to see if all Pinot and ThirdEye components are up. It then creates a Pinot Table with the sample retail-orders dataset and sets up anomaly detection in ThirdEye.

Wait for the quickstart-helper-XXXXX Job to go into the Completed state before proceeding. Please note that it should be in the Completed state and NOT in the Running state. You can view the Job state by running kubectl get pods.

Dataset Exploration

We can explore the data from the ThirdEye UI. Port-forward to the UI pod to access it from the browser. You can port-forward with the following command and visit the UI on your browser at http://localhost:8081/

kubectl port-forward svc/startree-startree-thirdeye-ui 8081:8081

You should see the following page. To view the alerts setup by the quickstart-helper job, click on the Alerts box.


View the alert by clicking on the alert name, in our case: threshold-alert-orders-sample


Our order-events sample dataset was created in the year 2021. Therefore please change the date range using the data-selector on the alert detail page. In the example beloew we are viewing the date range from Jan 1st, 2021 to March 31th, 2021.


Once you hit the Apply button on the data-range selector, you should be able to view the anomalies plotted on the chart.


You can view the alert configuration on the bottom of the alert page. The sample alert is pre-configured to use the startree-threshold Alert Template which detects threshold based anomalies for event_type in the order_events dataset.

Find anomalies

View the related anomalies for the alert by clicking on the link in the Anomalies in the Time Range section. Then from the list view select a specific anomaly to analyze.


We can perform root cause analysis for the drop in order_events by event_type by clicking on the Investigate Anomaly button. This opens up a page that allows us to perform dimensional analysis.


What went wrong?

We can see that the dimensional analysis indicates a drop in the number of orders which are in the IN_PROGRESS, PLACED states and increase in DELIVERED state.

What has contributed the most to the spike? Looking at the above heatmap it clearly indicates that the order bookings has increased. In order to what has contributed to the spike the business operations team can drill down on the "orders delivered" and look at other dimensions which has caused the spike.

As a call to action

  • The business operations team can reach out to order management team to understand why there are more order bookings on that day as compared to week-over-week or week-over-two-weeks.
  • If there is a public holiday or special occassion then it is an expected spike and probably no action is needed. Otherwise, the shipment department can proactively investigate and resolve the issue with the drop-in # of shipped orders for improved customer experience.
  • Maybe there is a specific campaign running or discount given which is resulting in an increase in order volume.
  • Sometimes the reason could be a pricing glitch and maybe selling at a cheaper price resulted in more orders getting booked and delivered however not good for revenue and business growth. Then the team can go and make the pricing adjustments accordingly to prevent business loss.

Using StarTree ThirdEye the custom events can be captured and correlated with the spikes or drops. This further helps users of StarTree ThirdEye to decide which specific action can be taken to resolve the outliers.

To learn more about StarTree ThirdEye please visit the documentation link which covers the concepts and how-to guide for using StarTree ThirdEye.