登录查看更多内容

How can you balance real-time monitoring with long-term data analysis in DevOps metrics?

由人工智能和领英社区提供技术支持

DevOps metrics are essential for measuring the performance, quality, and value of software delivery processes. However, not all metrics are equally important or relevant for different purposes and audiences. In this article, you will learn how to balance real-time monitoring with long-term data analysis in DevOps metrics, and why both are necessary for continuous improvement.

此文章中的业界达人

由社区从 5 条内容中精选。了解更多

1 Real-time monitoring

Real-time monitoring is the process of collecting and displaying data about the current status and performance of your software systems, applications, and infrastructure. It helps you detect and troubleshoot issues, optimize resources, and ensure reliability and availability. Real-time monitoring requires tools that can capture, aggregate, and visualize data from various sources, such as logs, metrics, traces, and events. Some examples of real-time monitoring tools are Prometheus, Grafana, Elastic Stack, and Datadog.

添加您的观点

Steven Poitras

Helping build the future at DevRev | Previously Chief Architect at Nutanix | Creator of NutanixBible.com
举报内容
When it comes to real-time monitoring, the ability to sift through the noise is crucial. To ensure this, a few tips: - use anomaly detection to account for things "out of the ordinary" - make sure you're alerting on the correct items, and reduce noise alert monitors - use a solution with tracing

已翻译

赞

2 Long-term data analysis

Long-term data analysis is the process of extracting and interpreting data over longer periods of time, such as weeks, months, or years. It helps you identify patterns, trends, and correlations, evaluate outcomes and impacts, and support decision making and planning. Long-term data analysis requires tools that can store, query, and analyze large volumes of historical data, such as databases, data warehouses, and business intelligence platforms. Some examples of long-term data analysis tools are MySQL, PostgreSQL, Amazon Redshift, and Tableau.

添加您的观点

Steven Poitras

Helping build the future at DevRev | Previously Chief Architect at Nutanix | Creator of NutanixBible.com
举报内容
This is where anomaly detection and accounting for differing periods (e.g. h/d/m/q/y) can help you identity things out of the ordinary. When it comes to algorithms for detecting expected "bands", we had used 'Generalized Extreme Studentized Deviate Test', however, I'm sure there are much better things out there today.

已翻译

赞

3 Balancing the two

Balancing real-time monitoring with long-term data analysis in DevOps metrics is not a simple task. It requires a deep understanding of your goals, stakeholders, and context. To achieve this balance, you should define your key performance indicators (KPIs) and align them with your business objectives and customer expectations. KPIs should be specific, measurable, achievable, relevant, and time-bound (SMART). Additionally, you must choose the right level of granularity and frequency for your metrics, as well as use different dashboards and reports for different audiences and purposes. Moreover, you should implement feedback loops and continuous learning to adjust and optimize your processes based on the results. This will enable you to collect and act on the data and insights from your metrics quickly, frequently, and iteratively. Finally, continuous learning is essential to foster a culture that encourages experimentation, testing, and learning from your metrics.

添加您的观点

Steven Poitras

Helping build the future at DevRev | Previously Chief Architect at Nutanix | Creator of NutanixBible.com
举报内容
Like anything in life, balance is imperative. If you focus too much on long term, you may miss critical items happening in real-time and vice-versa. I think a lot of times, people focus on the wrong metrics or KPIs, or ones that are too pointed, which leads them to wrong conclusions. The key is that the more you can blend or correlate metrics to help provide visibility, the more you can relate them to help determine causality.

已翻译

赞

4 Benefits of balancing

Balancing real-time monitoring with long-term data analysis in DevOps metrics can bring many benefits for your software delivery processes. These include improving the quality and reliability of your software products and services, increasing the efficiency and productivity of your software development and operations teams, enhancing customer satisfaction and loyalty, and driving the innovation and growth of your business. By utilizing this approach, you can identify and resolve issues quickly, prevent them from recurring, optimize the use of resources, reduce waste, automate tasks, deliver value faster, meet customer needs and expectations, provide a consistent experience, measure the value of your software solutions, demonstrate their impact, and support strategic decisions.

添加您的观点

Steven Poitras

Helping build the future at DevRev | Previously Chief Architect at Nutanix | Creator of NutanixBible.com
举报内容
If you don't balance, there will be gaps in your coverage. For example, if you only focus on real-time items, you will miss out on important items like seasonality, trends, etc. If you only focus on long-term perspectives, you will be able to see trends, seasonality, etc, however, you will potentially miss critical items that are happening in the present. Balancing isn't beneficial, it is imperative.

已翻译

赞

5 Challenges of balancing

Balancing real-time monitoring with long-term data analysis in DevOps metrics can also pose some challenges for your software delivery processes. It is important to choose the right tools and platforms for your metrics, which should be able to integrate with existing systems, scale with data volume and complexity, and provide necessary features and functions. Additionally, the cost and complexity of metrics can increase due to various data sources, storage and processing requirements, and security and compliance standards. To ensure accuracy and consistency of your metrics, you must pay attention to the quality and reliability of data collection and analysis methods, alignment and agreement of definitions and calculations, as well as validity and relevance of assumptions and interpretations. Moreover, you need to be aware of potential pitfalls and biases of your metrics that might lead to misleading or inaccurate conclusions, such as vanity metrics, correlation vs causation, survivorship bias, or confirmation bias.

添加您的观点

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Cmdr (Dr.?) Reji Kurien Thomas , FRSA, MLE?

I Empower Sectors as a Global Tech & Business Transformation Leader| Stephen Hawking Award 2024| Harvard Leader | UK House of Lord's Awardee | Fellow Royal Society | CyberSec | CCISO CISM CCNP-S CEH
举报内容
Hybrid Monitoring Framework - At a leading e-commerce company, we implemented a hybrid framework that combined real-time monitoring with long-term trend analysis. This approach allowed us to quickly detect and address immediate issues while also understanding long-term patterns for strategic planning. Automated Alert System - Integrated an automated alert system in a financial services company's DevOps pipeline. It flagged anomalies in real-time, while storing data for deeper, long-term analysis, ensuring immediate responsiveness without losing sight of broader trends

已翻译

赞

System Architecture

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

How can you balance real-time monitoring with long-term data analysis in DevOps metrics?

1

2

3

4

5

6

1 Real-time monitoring

2 Long-term data analysis

3 Balancing the two

4 Benefits of balancing

5 Challenges of balancing

6 Here’s what else to consider

System Architecture

给文章评分

感谢您的反馈

更多System Architecture相关文章

更多相关阅读内容

How can you balance real-time monitoring with long-term data analysis in DevOps metrics?

1

2

3

4

5

6

1 Real-time monitoring

2 Long-term data analysis

3 Balancing the two

4 Benefits of balancing

5 Challenges of balancing

6 Here’s what else to consider

System Architecture

给文章评分

感谢您的反馈

查看其他技能