The Evolution of Predictive Analytics in IT Operations and DevOps: Why Incident Prediction combined with Metric Prediction is needed to maintain a competitive edge

InsightFinder Unified Intelligence Engine (UIE) platform leverages our advanced AI to seamlessly integrate and analyze diverse data sources, delivering actionable insights for optimal decision-making.

Patents

Our portfolio of patents showcases our commitment to innovation and leadership in the field of AI for DevSecOps.

Integrations

Our platform expertly integrates with leading observability solutions and open source monitoring tools, ensuring a comprehensive and versatile approach to achieving AI-driven self-healing and self-optimizing.

Customer Success Stories

Discover how our customers have transformed their operations and achieved remarkable results.

Incident Prediction

Leverage the power of AI to foresee and preemptively address system incidents, ensuring smooth operations and zero downtime.

Incident Investigation

Empower your team with powerful root cause analysis to quickly identify, analyze, and resolve system incidents, enhancing efficiency and minimizing impact.

Unified Health View

Gain a comprehensive overview of your system’s health with real-time monitoring data fusion and insight extraction, enabling informed decision-making and proactive management.

Google Cloud Cost

Google Cloud Integration

InsightFinder can stream the cloud cost data from google cloud and generate anomalies to automatically alert the team of any unwanted or unidentified activities in the account from hackers or over utilization and help reduce your cloud bills.

Project Creation

Go to “Settings”->“System Settings”. Click on “Add New Project”.
Select “Google Cloud Cost” from the list and click on “Create Project” on the next page.
This is where you will start configuring the project.
1. Project Id, Private key Id, Private key, Client email, Client id – Use your GCP authentication key information here. Please Note – Private Key starts with 5 ‘-’ and ends with ‘\n’.
2. Another way is for users to directly upload JSON key file.
  (See Figure 1)
Click on “Verify” and you will be taken to the next page, if verification is successful.
Select the billing table from the list and specify any historical period. (Refer next section to understand the gcp billing table setup). You can also specify the sampling interval, as well as the collection frequency (how often data is pulled from BigQuery).
(See Figure 2 and 3)
Then on the next page, you can type in the “Project Name” and “System Name” and click on Register.
(See Figure 4)
Once a project is successfully created, you will see a message like below.
(See Figure 5)
Once a project is successfully created, you will see data in the metric chart like below.
(See Figure 6)

GCP Billing Table Setup

First, be sure you have either the Billing Account Costs Manager role or the Billing Account Administrator role on the billing account for which you want to fetch billing data. Ensure that the service account you will use has the BigQuery data viewer role for this table.

Use an existing project or create a new project for billing dataset at Dashboard.
Link the project to the billing account from which you want to fetch data. Documentation link.
Add the BigQuery User role for the project you are using.
Create a dataset in BigQuery under the project you plan to use by going to the console at Console and in the left menu click “Create dataset”
Give your dataset a name and fill out other fields as desired. If you are going to use Detailed usage cost data (see below), you will need to choose either the mult-region US location or the multi-region EU location. Do not enable table expiration. In the advanced settings, leave Encryption as “Google-managed encryption key”.
(See Figure 7)
Once you have created the dataset, go to the Billing export page at https://console.cloud.google.com/billing/export and select “EDIT SETTINGS” under that export. Choose the project and dataset you chose/created in the previous steps and click “SAVE.” Enable the BigQuery API if you are prompted to do so.
(See Figure 8)
You should be able to see the table in the BigQuery console under the dataset you created earlier and the table that will appear in the InsightFinder configuration list.

Warning: Use of BigQuery can result in Google Cloud costs. However, the cost is either 0 or a small amount depending on the existing usage of BigQuery.