Software Development Updates

DevOps methodology encourages you to develop better quality software and quick deployment updates through a set of tools, DevOps best practices, and a culture of collaboration. Together, the development and operations teams strive to optimize workflows and increase efficiency through automation. To achieve these goals, you must monitor the system performance, including all services, infrastructure, and applications. This monitoring requires a complete diagnosis of your system using effective DevOps monitoring tools to detect and resolve issues.

This article explores the top DevOps monitoring tools, highlighting their features, pros, and cons, allowing you to analyze and pick suitable options. We also discuss the factors to consider when choosing the right tool and the monitoring best practices to use them effectively. 

1. What is DevOps Monitoring?

DevOps monitoring is a continuous process of tracking, identifying, analyzing, and reporting on the specific components of the DevOps pipeline using predefined metrics. The project workflow consists of various stages, including planning, development, integration, testing, deployment, and operations. 

Monitoring in DevOps helps identify issues in the system or its performance before deployment of the code. It also tracks changes in the user experience and alerts the team about it. This process can reveal if new releases are not working up to expectations, allowing developers to go back to the previous version. 

2. Benefits of DevOps Monitoring Tools

There are several benefits of using DevOps monitoring tools. Here are a few important ones: 

2.1 Improved System Visibility

Monitoring tools provide comprehensive visibility into the functioning of the services, applications, networks, and infrastructure. They gather data to offer valuable insights into the system’s performance. 

2.2 Troubleshooter Access

Although DevOps can’t solve every issue within your software, its monitoring tools can certainly help you troubleshoot issues in production environments. These tools monitor app performance, detect issues, and facilitate quick fixes. You can also eliminate manual errors by automating the entire process. 

2.3 Identifying Bottlenecks

DevOps monitoring tools can help you identify performance bottlenecks in your development workflow. They get to the root causes and offer suggestions for resolving them. 

3. Types of Monitoring in DevOps

We saw the reasons why you need to implement DevOps monitoring. However, there are various types of it. Each takes care of different aspects of a DevOps project. Let’s take a brief look at them. 

3.1 Infrastructure Monitoring

Infrastructure monitoring is about assessing the performance and health of your infrastructure components, like databases, networks, and servers. Infrastructure monitoring provides insights into the server response time, disk usage, network latency, resource utilization, and more crucial metrics. 

Prometheus, Datadog, Zabbix, and Nagios are popular infrastructure monitoring tools. Mainly, there are two types of infrastructure monitoring; agent-based and agentless. The agent-based method involves installing an agent on each host, and the agentless method includes using built-in protocols like WMI, SNMP, and SSH to monitor the components of the infrastructure. 

3.2 Application Performance Monitoring (APM)

Application Performance Monitoring (APM), refers to the continuous monitoring of the performance and availability of specific apps. It monitors metrics, such as hardware utilization, API responses, transaction times, SLA status, user response times, and more. With this type of monitoring methodology, developers can find performance issues in DevOps and fix them instantly. 

3.3 Network Monitoring

Network monitoring is a part of infrastructure monitoring that focuses specifically on tracking the health and performance of network-related components. It uses metrics like hardware failure, uptime, and bandwidth to assess the condition of virtual machines, firewalls, routers, switches, and servers. 

A system administrator had to manually perform checks which are now automated using network monitoring tools. So, any network-related failures or issues can be addressed before they spoil the user experience. 

3.4 Security Monitoring

If there are any security vulnerabilities or threats to your system, then this monitoring methodology comes to your rescue. Focused on system security, it monitors user behavior, system activities, network traffic, and logs to detect security breaches, suspicious activities, or anomalies. 

You can ensure the safety of your DevOps projects by leveraging security monitoring tools such as vulnerability scanners, Intrusion Detection Systems (IDS), and Security Information and Event Management (SIEM). 

3.5 Cost Monitoring

You might face cost overruns during large and complex DevOps projects. Use cost monitoring tools to avoid that by tracking resource usage, costs per unit, and more in real-time. These tools allow you to share cost reports with the finance teams or engineers. 

With cost monitoring tools, you get a better picture of resources used at each stage of DevOps and understand your gross margins and cost of goods sold. 

3.6 Synthetic Monitoring

Synthetic monitoring is one type of software testing process. It simulates the real end-user interactions for your app or websites using behavioral scripts. The aim is to get to know their user experience. It is measured with metrics, such as the speed, performance, and functionality of the system or its components. 

With synthetic monitoring, you can find and fix issues before they can have any real impact on the user experience. Additionally, It enables you to establish performance benchmarks for future reference. 

4. Factors to Consider When Selecting DevOps Monitoring Tools

When it comes to picking the best DevOps monitoring tools, you have to keep the following things in mind. 

• All-in-One Observability: You need a tool that views all the components together. Having different views gives way to visibility gaps created by fragmented data. 
• User-friendly Interface: Pick a tool that can be customized as per your requirements and allows you to monitor metrics of your choice. 
• Automatic Discovery: Look for automated tools that identify the important components of the system and automatically find errors in them. Remember, not all tools come with this capability. 
• Real-time Metrics, Events, and Distributed Traces: Ensure the tool provides real-time data, events, and distributed traces. Real-time data and alerts allow you to address issues before they become a serious and costly problem. 
• Root Cause Analysis: Find a tool that can help you find and fix the root cause of the problem and not just cure the temporary symptoms. 
• Data Retention Periods: Every tool comes with a limited data retention period. It’s costly to have a longer data retention period, but it helps you find the patterns for the problems that arise occasionally. 
• AIOps and Machine Learning: Many monitoring tools are powered with AI and ML technologies to improve their capabilities, such as searching and answering, which leads to reduced time in resolving technical issues and improved productivity. 
• Resource Usage and Cost Monitoring: Keep track of resource usage and costs, as managing it is the most important metric to monitor in every project. 

5. Best Monitoring Tools In DevOps

Earlier, we discussed the benefits of using the monitoring tools. But how can you avail that if you don’t pick the right tool? Well, we looked at the deciding factors for that. Now, it’s time to browse through the most popular and preferred options.

5.1 Nagios

An open-source network monitoring tool, Nagios helps you track the networks and servers within your IT system. It comes with an alert system that will notify you about the errors upon their detection. You can get started with monitoring by entering the IP address of the device. 

Nagios monitors your system resources such as its CPU, load, memory, and Swap space. It can also track your server’s services. Along with 24/7 monitoring capabilities, Nagios provides an intuitive web interface to track the status of your services.

Features

• Comprehensive Monitoring: Nagios can monitor network protocols like HTTP and FTP, services like HTTP and POP3, applications, web servers, system metrics, and devices like switches, routers, and printers. Its informative and easy-to-use interface provides such as availability reports, SLA reports, and historical data analysis, to monitor efficiently monitoring.
• Scalability: Nagios is capable of scaling for large-scale deployments. It can manage thousands of hosts and network components. To handle extensive implementations, you can implement load-balancing techniques and distributed monitoring nodes to ensure efficient performance.
• Logging and Reporting: Nagios thoroughly logs all monitoring events, notifications, and actions. It also provides multiple reporting options, like availability reports, SLA reports, and historical data analysis, to help track system performance.
• Configuration: The flexible configuration files let the administrators manually configure hosts, services, notification settings, and more. 
• Easy Integration with Third-Party Tools: Nagios easily integrates with third-party graphing tools like Grafana and Cacti, tracking tools like Jira, and communication platforms like Slack.

Pros: 

• Automation: Nagios can automatically execute scripts in response to particular events, increasing the resilience of systems. 
• Flexible: Nagios is versatile and can monitor a wide array of components including servers, network devices like switches and routers, services, applications, and even business processes.
• Real-time Alerts: Notifications and instant alerts immediately update you regarding any system problem, such as maintenance and downtimeNagios’s vast built-in plugin ecosystem.
• Extensibility with Plugins: The vast built-in plugin ecosystem of Nagios extends its functionality and also supports custom plugins for different applications and services.
• Community Support: Nagios is open source and has comprehensive support from users and contributors in terms of plugins, extensions, tutorials, online resources, and support.

Cons:

• Learning Curve: You need to manually configure the text files, which is difficult, especially for beginners. The learning curve is steeper than that of other DevOps monitoring tools.
• Complex Scaling for Large Environments: Large-scale deployments can be cumbersome as they require additional configuration and also third-party tools.
• No Built-in Log Management: Nagios employs integration with outside tools like Kibana and Greylog for log monitoring.

5.2 Prometheus

Prometheus is an end-to-end monitoring system that allows you to display the data with customizable graphs. Many organizations prefer to use this tool for application and infrastructure monitoring purposes because of its open-source nature. Prometheus sends alerts to the DevOps teams about the system outages through Alertmanager. 

Prometheus consists of a vibrant ecosystem because it is open-source and, hence, community-driven. You can easily download and use it for measuring types, displaying data graphically, storing time series in local memory, alerting, and more. 

Features:

• Multi-Dimensional Data Model: Prometheus employs a multi-dimensional data model to represent time-series data identified by metric names and key-value pairs.
• PromQL (Prometheus Query Language): PromQL is a powerful and flexible query language for filtering, aggregating, and slice-and-dice time-series data. It can easily perform aggregations, helping with real-time analysis.
• Pull Model: Prometheus collects time-series data using a pull model over HTTP. It periodically scrapes metrics from the services it monitors.
• Time-Series Database (TSDB): Prometheus has a built-in time-series database (TSDB) optimized for storing and retrieving large amounts of metrics data. It also supports local as well as remote storage integrations.
• Custom Alert Rules: You have the flexibility to define your own alerting rules using PromQL according to some predefined conditions. Alerts can be routed to multiple notification channels like email, Slack, and more.
• Data Visualization Through Integrations: Prometheus integrates easily with visualization tools like Graffana to create real-time dashboards and reports using different types of graphs based on collected data.
• Microservice Monitoring: Prometheus is suitable for monitoring applications with microservice architecture as it integrates with service discovery mechanisms like Kubernetes and Consul.

Pros:

• Active Community: There is a large community of active users contributing to improvements, bug fixes, and feature enhancements.
• Dynamic Service Discovery: Prometheus, without manual configuration, is capable of finding new services and instances in dynamic environments, especially microservices and container-based systems, and monitoring them.
• Federation and Scalability: With the help of federation, you can scale monitoring across multiple Prometheus instances in a large or distributed environment.
• Real-time Analysis: PromQL generates real-time insights based on the collected data.
• No External Dependencies: Prometheus does not require external databases or complex configurations as it runs independently. 

Cons:

• No Built-In Long-Term Storage: You’ll need an external storage system to store and analyze metrics over extended periods.
• Scaling Limitations: It’s quite challenging to scale Prometheus in large environments, especially with many microservices.
• Cloud-Native Focus: You have to configure service endpoints manually for static or non-cloud environments.

5.3 Splunk

Splunk is used to monitor and analyze machine-generated data, such as logs, through an easy-to-use web interface. It’s a continuous monitoring tool that tracks data from each phase of the software lifecycle that is generated frequently but isn’t used effectively. 

After gathering the data, Splunk stores it in a central index where it is assessed thoroughly. It’s an ideal option if your team’s purpose is to find the root cause of the problems. Splunk helps you identify trends and correlate data from various sources. 

Splunk effectively gathers large and unstructured datasets from multiple sources and compiles them in a central index. The users can request the necessary information from there to fulfill their requirements. 

Features:

• Advanced Analytics: You can collect, index, search, visualize, and analyze your data in real-time. It allows you to identify patterns, trends, and potential issues in the infrastructure or applications.
• Scheduled Reporting: Splunk generates scheduled reports in PDF, CSV, or JSON format that automatically run queries and deliver the results to specific users. 
• Customizable Dashboards: You can create interactive dashboards using visualizations such as charts, graphs, and tables to monitor system performance.
• Search Processing Language (SPL): Users can perform complex searches and queries to filter, aggregate, correlate, and visualize indexed data in real-time.
• Centralized Log Collection: Splunk consolidates logs from servers, network devices, applications, etc., into a single location for easier management and analysis.
• Role-Based Access Control: Administrators can limit access to essential data and features for authorized users only using Splunk’s role-based access control.

Pros:

• Real-time Data Processing: Splunk ingests and processes data in real-time, giving insights into system performance, security, and other operational issues. 
• Scalable Architecture: Splunk can be deployed from small setups to large-scale deployments. It’s capable of processing vast volumes of data.
• Easy Integration: Splunk can easily integrate with third-party tools, services, databases, and many more with the help of apps and add-ons.
• Deployment and Migration: You can deploy Splunk in on-premises or cloud-based environments. You can even migrate from on-premises to cloud or hybrid environments.
• Extensive Documentation and Support: You have unlimited access to various online tutorials and discussion forums to improve your understanding.

Cons:

• High Cost: Splunk licensing becomes quite expensive for large-scale deployments. 
• Frequent Maintenance: After Splunk deployment, you will need to take care of its maintenance to manage the storage and scaling. 
• Complex Setup: A proper strategy and expertise are required to deploy Splunk in large and distributed environments. 

5.4 Sensu

Monitor the functionality of your applications and infrastructure utilizing Sensu. It measures and monitors your business KPIs to track the health of your system in a cloud environment. Sensu provides APIs explicitly designed to monitor dynamic and large-scale systems. 

This monitoring platform allows you to monitor your system the way you want and is capable of addressing complex problems of your modern infrastructure. Additionally, it also offers OSS observability for different types of environments, alerting, self-healing, incident management, and health checks as a part of its monitoring solutions. 

Using Sensu, sharing monitoring workflows with engineers is possible by codifying them in declarative configuration files. They can be edited, versioned, and examined to meet your requirements. 

Features: 

• Agent-Based Architecture: Sensu uses lightweight agents deployed across multiple environments to collect monitoring data from systems, services, and applications.
• Event-Driven Architecture: The monitoring tasks called checks generate events, and these events are processed and used to trigger alerting, logging, or automated remediation.
• Built-in and Custom Plugins: Sensu is bestowed with a library of plugins that extends its functionality for monitoring databases, web servers, web servers, etc. You can even write your own plugins for your application using Ruby, Python, Bash, etc.
• Check Scheduling: Users can schedule the monitoring checks by defining check intervals, exact times for execution, and even setting conditions under which checks should run.
• Integrated Alerting: Sensu can be configured to integrate with Slack, PagerDuty, Opsgenie, and email to generate and send alerts.
• Secure: Sensu consists of many built-in security features, such as role-based access control to protect your sensitive data.
• Scalability: Sensu is capable of monitoring thousands of nodes and processing more than a hundred million events from a single cluster. It can manage ten thousand devices and almost fifty thousand agents per cluster.

Pros: 

• Cross-Platform Support: Sensu agents can run on a variety of platforms like Linux, Windows, macOS, and containerized environments like Docker and Kubernetes.
• Built-in Service Discovery: Sensu finds and monitors new services automatically when deployed, keeping up-to-date monitoring.
• Custom Checks and Plugins: Users are free to create checks and plugins according to their application requirements.
• Lightweight Agents: The lightweight agents can run on multiple platforms with minimum resource overhead, resulting in increased performance.
• Centralized Interface: Sensu’s observability control plane lets users automate and repeat the workflows, saving time and effort.

Cons:

• Complex Configuration: Sensu uses JSON and YAML to configure agents, checks, and handlers, which must be done with proper planning.
• No Built-in Dashboards: Sensu is devoid of built-in visualization methods; thus, integration with third-party tools becomes necessary, which requires more effort.
• High Resource Utilization: The required resources increase with an increase in the number of monitored services and checks.

5.5 Elastic Stack

When you bring the best of Kibana, Logstash, and ElasticSearch, you get ElasticStack. Three open-source tools were combined to design this masterpiece. Hence, it is undoubtedly excellent at searching through logs. Apart from monitoring, ElasticStack also assists with troubleshooting, security, compliance, and SEO. Developers prefer this tool for searching, storing, and assessing data from various sources. 

Features:

• Centralized Data Processing: Logstash processes and transforms all the collected data from various sources in a single place before sending it to Elasticsearch thus simplifying log management.
• Data Visualization: You can create interactive dashboards using line graphs, bar charts, pie charts, heat maps, and many more to monitor and visualize Elasticsearch data in real-time.
• Cloud Integrations: You can collect and analyze cloud service logs and metrics from various cloud platforms like AWS, Azure, and Google Cloud using Elastic Stack, as it integrates well with all these cloud platforms.
• Fault Tolerance: Elasticsearch divides data into primary and replica shards to ensure data availability if any of the nodes fails, thus avoiding work obstruction.
• Machine Learning Integration: Elastic stack integrates ML features for anomaly detection in time-series data, automated analysis of data, and predictive analysis to forecast trends. 

Pros:

• Centralized Logging: The ELK stack allows data ingestion from multiple cloud environments and transmission to a single searchable repository for analysis.
• Distributed Architecture: Elastic search can scale horizontally by distributing and replicating large volumes of data across a group of nodes, ensuring fault tolerance. 
• Interactive Dashboards: Kibana allows users to create real-time data visualizations using Elasticsearch data to analyze trends, monitor performance, and generate reports. 
• Extensible: There are numerous plugins to extend the functionalities according to various use cases.
• Cost Effective: The components of Elastic stack are free and open source, hence no licensing fees like Splunk.

Cons:

• Restricted Built-in Security Features: The open version of Elastic Stack comes with essential security features, thus incurring additional costs for sensitive environments.
• Resource Intensity: Infrastructure costs may increase with an increase in data volume because of substantial memory and CPU resources.
• Scaling Challenges: Due to sharding and replicas, difficulty arises in scaling across various nodes.

5.6 New Relic

New Relic is one of the best cloud-based continuous monitoring tools. It helps developers gain insights into the user experience and the performance of your infrastructure, applications, browsers, and networks. New Relic also has full-stack observability and mobile monitoring to track traces, metrics, logs, and events. 

Features:

• Security Monitoring: Real-time detection of security threats in application code and infrastructure to continuously protect your application from exploitation of vulnerabilities.
• Real User Monitoring: It tracks user sessions and interactions in real-time, thus giving insights regarding user experience for performance optimizations.
• Data Visualization: You are free to build customized dashboards using advanced graphing and visualization options to display real-time metrics and analytics to keep track of system performance.
• Third-Party Integrations: New Relic perfectly integrates with incident-response tools like PagerDuty and Slack), logging tools like Splunk and Sumo L, DevOps tools like Jenkins and Docker, etc.
• Network Monitoring: Network Performance Monitoring monitors network traffic between services and components to keep track of latency, throughput, packet loss, and other network metrics for unbreakable data transfer between systems.

Pros: 

• Full Stack Monitoring: New Relic monitors applications, infrastructure, logs, and real users, providing complete visibility from front-end applications to back-end services, infrastructure, and databases.
• Mobile APM: Gives mobile app performance insights over different networks through crash reports, network request performance, usage patterns, etc.
• Multi-Cloud Support: Monitors cloud services, instances, and containers for cloud providers like AWS, Azur, and Google Cloud.
• Real-time Performance Monitoring: New Relic collects and processes data in real-time and analyzes their response times, error rates, and throughput.
• Supports Multi-languages: You can use your preferred programming language like Java, .NET, Python, Ruby, Node.js, etc. 

Cons:

• Complex UI: There are many such features, metrics, and settings adding to the navigation and configuration complexity, especially for beginners.
• Expensive: As the applications scale in size, the cost rapidly increases.
• Agent Compatability Issue: We know that agents are installed on hosts for data collection, which may sometimes not be compatible with specific environments, languages, and frameworks. 

5.7 Kibana

Kibana is an analytics and visualization tool designed specially to integrate with ElasticSearch. Some of its major capabilities include viewing, searching, and interacting with the internal data indexes of ElasticSearch. 

Advanced data analysis and visualization have been rendered easy with maps, tables, and charts. Kibana has a reputation for being a tool that helps developers comprehend large and complex datasets. A browser-based interface from Kibana is simple, yet it helps build or share dynamic dashboards to display the changes in the ElasticSearch queries in real-time. 

It only takes a few minutes to install the tool. You can also use it to explore the indexes of ElasticSearch without the need to write code or use any additional infrastructure. Data visualization in Kibana is aimed at helping developers find patterns that might lead to a serious problem someday.

Features:

• Machine Learning Integration: Kibana contains built-in support for machine learning features like anomaly and outlier detection to forecast trends based on historical data, which is helpful for predictive analytics.
• Geospatial Analysis: Kibana Maps lets users visualize geolocation data on interactive maps by creating region maps, choropleth maps, coordinate maps, uploading GeoJSON files, etc.
• Dev Tools: Tools like Console interacting directly with the Elasticsearch API, Search Profiler for inspecting and analyzing search queries, Grok debugger to build and debug Grok patterns, and Painless lab to test and debug scripts in real-time help in data interaction.
• Security Monitoring: Kibana has built-in SIEM (Security Information and Event Management) property to collect, detect, and respond to security threats.
• Scheduled Reports: You can easily generate reports based on your visualization or dashboard at regular intervals without any manual intervention.

Pros: 

• Scalable: Kibana is a part of the highly scalable Elastic Stack to handle a variety of data types from small environments to large-scale enterprise applications.
• User-Friendly UI: Even non-technical persons can easily create and modify dashboards, search for data, and build visualizations without any coding knowledge.
• Customizable Dashboards: Users can create interactive dashboards using bar charts, line charts, pie charts, heatmaps, etc., for specific use cases.
• Elasticsearch Integration: Elasticsearch with Kibana allows querying, visualizing, and analyzing data stored in Elasticsearch indexes.
• Open Source: Kibana comes under the open-source Elastic Stack so users can use the open-source version.

Cons:

• Limited Data Sources: Kibana can only query and visualize data stored in Elasticsearch. 
• No Real-Time Collaboration: Multiple users cannot collaborate to edit or view changes on the same dashboard.
• Maintenance Overhead: Kibana and the Elastic Stack require active management for optimal performance, particularly in large deployments.

5.8 Grafana

Make data visualization interactive with the Grafana. This DevOps monitoring tool supports several data representation techniques thanks to its panel architecture. It can quickly obtain, monitor, and analyze data from distinct sources, as well as share the results with the DevOps team. Maintaining the stack will be fine when using Grafana, as it includes cloud offerings. 

Features:

• Provisioning: Grafana allows script generation to automate the setup of multiple dashboards and get control of them.
• Dashboard Templating: You can create dynamic dashboards based on different inputs without recreating them for various tasks and collaborate with other members of the organization on them.
• Rich Visualization: Grafana offers time-series graphs, bar charts, pie charts, histograms, heatmaps, etc., to visualize the data based on its type. Users can even customize the presentation of the data.
• Alerting Rules: You can set up alerts on dashboard panels based on data from the connected data sources and manage them within Grafana’s alert manager.
• Custom Plugins: Users can create or install custom plugins such as data source plugins, visualization plugins, and app plugins to extend their functionality and integrate with other software to meet specific needs.
• Multiple Data Sources: You can combine data from sources like Prometheus, Elasticsearch, InfluxDB, Graphite, MySQL, PostgreSQL, AWS CloudWatch, Google Stackdriver, etc., in a single dashboard for analysis.

Pros:

• Scalable: Grafana can handle large data volumes and scale across multiple servers and clusters.
• Community Support: Grafana has an active community of users contributing to plugins and support for platform improvement.
• Grafana Cloud: It’s a fully managed and scalable version of Grafana that can integrate with Prometheus, Loki, and Tempo, giving a full-stack observability platform.
• Cross-Platform Support: Grafana can run on multiple platforms like Linux, Windows, MacOS, and cloud environments.
• Security: Graffana supports role-based access control and authentication methods like OAuth, LDAP, SAML, etc.

Cons:

• Limited Built-in Data Storage: Grafana relies on external data sources like Prometheus and InfluxDB to store time-series data.
• Steep Learning Curve: Configuring Grafana with external data sources and building customized dashboards requires expertise and continuous learning.
• Limited Built-in Analytics: Grafana lacks advanced built-in analytics features like anomaly detection, predictive analytics, or machine learning.

5.9 Sematext Cloud

Sematext Cloud monitors the infrastructure and application’s performance. It does excellent log management providing detailed logs and metrics to support businesses in decision-making. You can keep an eye on the networking parameters such as network bandwidth and connection mapping between servers, containers, and cloud instances. 

Features: 

• Pre-Built Dashboards: Sematext Cloud contains built-in dashboards for standard services and technologies such as Apache, Nginx, Elasticsearch, and Kubernetes.
• Integration Support: Sematext performs comprehensive monitoring by integrating with AWS, Azure, Google Cloud, Kubernetes, Docker, Elasticsearch, Apache Kafka, Redis, etc.
• Synthetic Monitoring: It continuously tests the uptime of your services and websites and tracks HTTP response times, error rates, etc, to optimize the performance of web applications. 
• Distributed Tracing: Sematext supports distributed tracing for microservices architecture to track requests across different services concerning latency and bottlenecks.
• Threshold-Based Alerts: Users can set thresholds for alerts regarding various metrics to identify unexpected behaviors to identify unexpected behaviors.

Pros:

• Unified Monitoring and Logging: There’s no need to switch between different tools for performance monitoring and log management because you can monitor both metrics and logs in one place only.
• Container and Orchestration Support: Sematext monitors container resource utilization, performance metrics, logs, and events or containerized environments.
• Cross-Platform Deployment: Depending on your infrastructure needs, you can deploy Sematext in cloud, on-premise, or hybrid environments.
• Flexible Pricing: Sematext includes pay-as-you-go options for various use cases so that you can adjust according to your budget.
• Contextual Correlation: You can correlate logs from different parts with system metrics to trace system behavior. 

Cons:

• Performance Limitations: The performance degrades, especially during peak loads when dealing with large volumes of data. 
• Cloud-Based Solution: Sematext is a cloud-based solution that requires reliable internet connectivity for optimal performance. Hence, unstable internet access may cause difficulties or downtime.
• Search Speed: Querying large datasets takes more than expected time, hampering real-time analysis.

5.10 Zabbix

As a monitoring solution for IT components, Zabbix tracks the health and performance of cloud services, virtual machines, networks, and applications. If your system is in a multi-tenant environment with different end-users, Zabbix is an ideal option. It can also determine the flexible issue threshold in the software using backend database values. 

Features: 

• Agent-Based and Agentless Monitoring: Zabbix allows the collection of performance data using agents installed on hosts and agentless via SNMP, IPMI, HTTP, etc.
• SLA Monitoring: Zabbix’s service level agreements (SLAs) monitoring helps businesses track service uptime and performance against defined targets.
• Advanced Alerting: Zabbix supports trigger-based alerting mechanisms where users can specify conditions for alerts depending on specific thresholds or event occurrences.
• Auto-Discovery: You don’t have to configure Zabbix every time to monitor devices, as it automatically discovers network devices, servers, and applications when they appear in the network.
• Integration: Zabbix includes built-in API support for integration with third-party applications, tools, and systems. It also facilitates custom integration for monitoring, alerting, and reporting.
• Distributed Monitoring: Zabbix employs proxies to collect data in remote locations and send it to the central server for centralized monitoring and reporting.

Pros:

• No Licensing Fees: It is an entirely open-source monitoring tool that can save businesses money on commercial monitoring tools like Splunk, Datadog, or New Relic.
• Extensive Monitoring: Zabbix monitors a range of components, such as servers (Linux, Windows, Unix), applications, network devices, databases, virtual machines, and cloud services.
• Flexible Data Collection: Zabbix supports data collection using SNMP (Simple Network Management Protocol), ICMP (ping), IPMI (Intelligent Platform Management Interface), JMX (Java Management Extensions), etc.
• Active Community: Zabbix has extensive community support that contributes to knowledge, plugins, templates, and best practices.
• Comprehensive Dashboards: You can use graphs, network maps, and real-time data views to create different dashboards.

Cons:

• Dependency on Agents: Zabbix is highly dependent on agent-based monitoring that requires heavy management and maintenance.
• Lack of Advanced Analytics: Zabbix does not provide built-in machine learning, anomaly detection, or predictive analytics features important in today’s monitoring solutions.
• Heavy Resource Consumption: The Zabbix server and database consume a substantial amount of CPU, memory, and disk I/O for monitoring a large number of hosts.

5.11 Influx DB

InfluxDB is one of the top DevOps monitoring tools. It tracks microservices and cloud-based applications, so it excels at working with distributed systems. Thanks to its specialized language, InfluxQL, InfluxDB has some extraordinary capabilities. 

Features: 

• Flexible Data Model: InfluxDB’s schema-less design allows it to store different types of time series data without predefining the schema.
• InfluxQL: Users can interact with time series data using InfluxQL, an SQL-like query language that performs aggregation, time-based grouping, and filtering.
• Integration: InfluxDB is integrated with a metrics collection agent called Telegraf. It also integrates with third-party integration tools like Grafana for dashboarding and visualization, Prometheus for metrics collection, and Kapacitor for alerting.
• Alerting: InfluxDB integrates with Kapacitor, a real-time streaming data processing engine, to define thresholds and alert conditions based on time series data.
• Low Resource Usage: InfluxDB uses fewer resources compared to traditional databases, especially in terms of disk I/O, due to its efficient storage format and compression techniques.
• Tagging System: InfluxDB uses tags to competently organize and index time series data allowing users to filter and group data by dimensions like location and device type.

Pros:

• Easy-to-Use Query Language: Users familiar with SQL will find InfluxQL easier to learn and implement as it’s SQL-like.
• Sharding and Replication: It’s easier to scale InfluxDB across multiple servers where high availability and fault tolerance are critical.
• Advanced Security Features: InfluxDB Enterprise provides intensified security features like fine-grained access controls, role-based access, etc.
• Open-Source: InfluxDB’s open-source version is freely available, making it accessible to small and large organizations alike.
• Real-Time Monitoring: InfluxDB is applicable for IoT data collection, system performance monitoring, and real-time analytics due to low-latency writes and queries.

Cons:

• Vertical Scaling Challenges: Increasing server resources can be expensive in dynamic environments with large datasets.
• Performance Overhead: Flux can introduce performance overhead, making query execution slower than with InfluxQL. You will need query optimization.
• Data Retention Costs: With the increase in data, storage and resource costs increase which becomes unsuitable for long-term data retention.

5.12 Datadog

Datadog allows you to monitor events and compute performance metrics for cloud services and infrastructure. Using multiple integrations, this DevOps monitoring tool also offers SaaS-based infrastructure monitoring services, allowing you to track the health of your infrastructure by monitoring the cloud environment. 

Features: 

• Centralized Logging: Datadog collects, processes, and stores logs from servers, applications, and network devices helping in real-time log analysis.
• Network Flow Analysis: Datadog NPM tracks network traffic between hosts, containers, and services through a visual map of network connections. 
• Container Autodiscovery: Datadog automatically detects and monitors new containers as they are deployed without manual intervention.
• API Tests: Users can write synthetic tests that simulate API requests to check their availability, response time, and correctness. 
• Multi-Cloud Support: Datadog provides deep integrations with major cloud providers like AWS, Azure, and Google Cloud, allowing for unified monitoring of multi-cloud environments.

Pros:

• Microservices Monitoring: Datadog’s service map gives a real-time, visual representation of all the services in the system and their dependencies.
• 24/7 Customer Support: Datadog offers excellent customer support like 24/7 live chat and email support, especially for its enterprise customers.
• Team Collaboration: Users can collaborate among team members to share dashboards, create reports, and set up shared alerts.
• Quick Setup and Deployment: The platform offers easy-to-use agents and integrations that simplify setup and deployment.
• Automated Incident Response: Datadog integrates with incident response platforms, enabling automated responses to certain issues like restarting a service or scaling resources to reduce manual intervention and downtime.

Cons:

• Limited On-Premise Support: Datadog is basically a cloud-based solution; hence it is less suitable for fully on-premise environments.
• Closed Ecosystem: Datadog is a proprietary platform, unlike some monitoring tools such as Prometheus or Grafana allowing fewer open-source contributions.
• Limited Export Options: Exporting data from Datadog for use in other systems or analytics platforms can be difficult.

6. DevOps Monitoring Best Practices

As with the DevOps processes, you also need to be strategic about using DevOps monitoring tools. Implement the best practices discussed below for the effective use of your monitoring tools. 

6.1 Define Your Goals and Know What to Monitor

How can you use monitoring tools without knowing what needs monitoring? So, first, define your goals or expected outcomes. Based on them, decide the KPIs and focus on the key DevOps metrics that need monitoring to ensure there are no issues along the way. Say you want to deliver an enhanced user experience, then you must keep an eye on the metrics like loading speed, and more. 

6.2 Choose the Right Monitoring Tools

Now that you have decided goals and the metrics to monitor, pick an appropriate monitoring tool that can help you along the way. You have to consider the features and functionalities while making the decision. We already discussed this question in one of the previous sections. 

6.3 Monitor User Activity

Monitoring user activities allows you to identify any suspicious activity, including unusual requests, logging from unknown devices, multiple login attempts, and more. This also helps you ensure that the right users with the right authorization are accessing the resources. Monitoring user activity is important to thwart potential security risks and cyberattacks.

6.4 Automate Incident Management and Response

Trying to avoid errors is not enough. You must also prepare for possible failures, such as misconfiguration, data inconsistencies, hardware issues, network failures, and more. You must automate the process of monitoring and addressing such incidents to ensure that the issues are dealt with before they affect the user experience. 

7. Conclusion

This article acts as your guide to picking the right DevOps monitoring tools and using them the right way. It defines these tools and explains their benefits and various categories. We also understand different factors that play a deciding role in picking the right tool. You have also been served with top options for monitoring tools in DevOps with their features, pros, and cons to help you make the right decision. In the end, it also provides a common understanding of the right way to use these tools.