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Precise anomaly detection with threshold-less alerts, root cause analysis, incident prediction, and automated remediation to ensure continued service delivery.

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Confluent Kafka Integration

Confluent Kafka Integration for Logs

InsightFinder can stream the logs from Confluent Kafka topics and correlate it with other data to generate anomalies, automatically identify root causes and predict incidents. Below documentation is a walk through of how to setup and configure a confluent kafka agent and project.

Project Creation
Agent Setup
Figures

Project Creation

  1. Go to “Settings”->“System Settings”. Click on “Add New Project”.
  2. Select “Custom” from the list and click on “Create Project” on the next page.
  3. This is where you will start configuring the project.
    1. Instance Type – Private Cloud
    2. Data Type – Log
    3. Agent Type – Live Streaming
      (See Figure 1)
  4. Click on “Next” and you will be taken to the next page.
  5. Then on the next page, you can type in the “Project Name” and “System Name” and click on Register. You can also define detection keywords and Incident labels.
      1. Detection keywords/regular expressions are used to detect which log entries will produce allowlist alerts.
      2. Incident labels/regex are used to identify which log entries indicate incidents.
        (See Figure 2)
    2. Once a project is successfully created, you will see a message like below.
      (See Figure 3)

    3. Once a project is successfully created, you need to set-up an agent.

Agent Setup

You would need a server where you can run this agent.

  • For container:
    1. Access the image
    2. Follow the steps from the overview section to use the agent
  • For GitHub:
    1. Download the agent from the link on the server.
    2. Follow the steps from the link to install the agent on the server.

Once you have an agent running, you will be able to see the data streaming into the InsightFinder project. To check,

  1. Go to Analysis->Log/Trace Analysis.
  2. Select your project and date. Click on the Refresh button on the right top. You will get the calendar view of log data.
    (See Figure 4)

Figures

Click on an image to expand.

Figure 1
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Figure 4
Figure 4

Confluent Kafka Integration for Metrics

InsightFinder can source the metrics from Confluent Kafka and correlate it with other data to generate anomalies, automatically identify root causes and predict incidents. Below documentation is a walk through of how to setup and configure a confluent kafka agent and project.

Project Creation
Agent Setup
Figures

Project Creation

  1. Go to “Settings”->“System Settings”. Click on “Add New Project”.
  2. Select “Custom” from the list and click on “Create Project” on the next page.
  3. This is where you will start configuring the project.
    1. Instance Type – Private Cloud
    2. Data Type – Metric
    3. Agent Type – Streaming
      (See Figure 1)
  4. Click on “Next” and you will be taken to the next page.
  5. Then on the next page, you can type in the “Project Name” and “System Name” and click on Register.
    (See Figure 2)
  6. Once a project is successfully created, you will see a message like below.
    (See Figure 3)
  7. Once a project is successfully created, you need to set-up the agent.

Agent Setup

You would need a server where you can run this agent.

  • For container:
    1. Access the image
    2. Follow the steps from the overview section to use the agent
  • For GitHub:
    1. Download the agent from the link on the server.
    2. Follow the steps from the link to install the agent on the server.

Once you have an agent running, you will be able to see the data streaming into the InsightFinder project. To check,

  1. Go to Analysis->Metric Analysis->Overall Line Chart.
  2. Select your project and date. Click on Reload button on the right top.
    (See Figure 4)

Figures

Click on an image to expand.

Figure 1
Figure 1
Figure 2
Figure 2
Figure 3
Figure 3
Figure 4
Figure 4

Learn About Monitoring

How InsightFinder Can Help Amplify Your Confluent Data
In today’s digital age, developers need solutions that can help them quickly build and deploy applications. Confluent Cloud is an innovative way to streamline the application development process. But what if you could do more? What if you could amplify the data coming through your Confluent Kafka cluster with predictive capabilities? Enter InsightFinder. InsightFinder analyzes powerful metrics and log data from any user source and uses real-time streaming to analyze and predict performance issues. Read on to learn more about how InsightFinder can help you get the most out of your Confluent Cloud experience. Real-Time Analysis & Predictions InsightFinder's real-time analysis and predictions give developers better insights into their data streams in order to make more informed decisions about their applications. This helps developers identify potential problems before they become serious issues, saving time in the long run by avoiding costly fixes down the line. Additionally, InsightFinder's machine learning algorithms can detect anomalies in data streams that may not be immediately obvious to human eyes, helping developers stay ahead of future issues before they arise. Cloud Monitoring  InsightFinder also provides cloud application monitoring capabilities that allow developers to monitor their cloud usage in real-time and identify trends as they happen. This allows developers to optimize their cloud resources for maximum efficiency while proactively preventing performance issues due to resource constraints or capacity limits. Additionally, InsightFinder can generate comprehensive reports with detailed metrics on application health and usage patterns so that teams can stay up-to-date on their cloud operations at all times.   Confluent Cloud is a powerful tool for streamlining the application development process. When combined with InsightFinder’s predictive capabilities it becomes even more powerful by amplifying their Confluent data with real time analysis and predictions. Insightfinder gives developers the ability to get deeper insights into application performance while proactively preventing potential issues before they become serious problems. This makes it an invaluable addition for any developer leveraging Confluence Cloud for streamlined application development. To consider Confluent and InsightFinder, learn more here and sign up for a free trial here.
Read More
Find and fix cloud cost anomalies in real-time
InsightFinder customers can now monitor their Amazon Web Services (AWS) and Google Cloud Platform (GCP) cloud costs within the InsightFinder platform and get notified with any abnormal cloud bills automatically. Customers are sometimes surprised by their cloud usage bill at the end of the month. How did usage get so high, and why wasn’t someone aware of this earlier? The truth is - it is time consuming to constantly monitor platform usage. In addition, it is often difficult  to immediately identify when something appears out of the ordinary. The chart will show usage level and cost over time. If there is a deviation from the norm, the project owner will receive a notification via email, slack, or teams. In contrast to existing threshold based alerting provided by cloud service providers, InsightFinder employs patented unsupervised machine learning algorithms to help customers discover any unexpected anomalies in their cost data. Once an anomaly is flagged and identified, InsightFinder’s holistic analysis can help point to potential causes, including the root cause, of the problem. For example, one InsightFinder customer was alerted to a spike in usage for their AWS EC2. Because of InsightFinder anomaly detection, the team quickly became aware of the problem. After reviewing the system activity surrounding rapid increase in usage, the team determined they had a security breach. Without InsightFinder, they most likely would have had to deal with high bills because of unknown increased usage, and their systems would have been vulnerable to attack for a longer period of time. Monitoring cloud storage costs will not only have a positive impact on managing cost effectively. Cost monitoring helps signal other, deeper issues that InsightFinder can find and fix. These tools will alert customers quickly not only to general cost activities, but also as a way to understand other unusual system behavior that could be caused by inefficient usage, security issues, and more.
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Incident response versus Incident prediction 
We have focused on responding to incidents when they happen, and that needs to change. The traditional role of IT Operations is to set up, fix, and monitor the applications and systems a particular organization needs. While the responsibilities include proactive work, it is understood that IT Operations is responsible for resolving issues that go wrong. As a result, the job has been focused on monitoring and response to problems. Incident response is the traditional action and role we think about when we define traditional ITOps roles.They are the firemen of the organization. An incident response organization places its primary importance on observability and alert tools. Which tools can detect and surface a problem the most quickly? How accurate is the alert tool? Is there a lot of noise, or false alarms, which causes incidents to be overlooked? The function of all these tools are meant to help you pay attention and respond quickly when something goes wrong. However, today’s reactionary response mentality for IT is no longer a sustainable strategy for businesses.  Why is this the case? First, the architecture of applications has become increasingly complex. Operational challenges, such as CI/CD acceleration and microservices have created tooling complexity. Today, there are more interconnected systems, making it harder to have clear visibility into what is happening across the entire environment. When they go down, it can take hours and often days to find where and when the problem occurred. Many times, reactive analysis often fails to find the true root causes and the same incident is more than likely to happen again in the future. With increasing complexity comes the need to monitor the different systems and applications that are controlling your business. Observability needs have grown as well as tools to help create visibility within these systems. While more information can be good, it comes at a cost. With new data comes data silos. Although more insights are available, they most often exist in silos. Therefore, when a problem occurs and is flagged in one system, it takes time to understand how the problem impacts all systems, how to untangle it, and where it started. Businesses need to use a tool that has overall visibility to all tools and applications, and understand how they work together. By leveraging the right AI system to oversee and analyze all the data and information, businesses can now be proactive because their data can be used to predict incidents. The new approach, or proactive ITOps, unites and analyzes all data sources, and therefore can detect root causes before they progress into incidents. Because root causes are flagged at an early stage, the incident can be predicted instead of observed when it happens. As a result, businesses can fix problems in advance of them taking a huge impact on the bottom line. Monitoring, by definition, is reactive. Contextualization, correlation, and analytics are needed in order to make use of the massive amount of data and observability information now available. Once these elements are applied to massive amounts of information, you can be proactive about your ITOps focus.
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