Home DevOps - DevOps Tools
Post
Cancel

DevOps - DevOps Tools

There are a few common tools employed in the DevOps environment, and they range from continuous delivery tools to integration and deployment tools, which put more emphasis on the automation of the system. These tools encompass configuration management tests when the system is built, version control, and application deployment, which includes any monitoring tools. For that reason, there are various tools used to implement the different processes of the DevOps system such as product and quality testing, code creation, production, and others.

System Monitoring Tools

This has been a hot topic, and many tend to shy away from it due to its complexity. In particular, monitoring tools have a high degree of automation complexity necessary when establishing the infrastructure and tools used. From this perspective, an integrated set of DevOps tools has been established to monitor the system on how they function to improve the overall performance. These tools aim at improving the visibility and productivity of the entire system to establish cross-functional collaboration. Therefore, establishing the right tools is essential and necessary through developing discipline, culture, and practices that come with the entire process.

Typically, monitoring tools could be considered to be the essential information management guide to DevOps which ensures that the system performs optimally. Though there are different tools in the market that can be used to ensure that the system is up to date, it is functional, and it can also, sustain all the activities which it does.

Tools like Nagios and Zabbix are some of the traditional ones incorporated in the system. These are open-source software, and though they are not well equipped to handle the diverse nature of DevOps that changes daily depending on market needs, they are still effective. In the case that one decides to use these monitoring tools, internal capabilities on the APIs should be extended on support the automating adaptation to configuration and changes to resource capacity.

There are also modern tools like Prometheus, Sensu, New Relic Infrastructure, and SysDig, which can be incorporated into the system to make it more viable and useful. All of these features monitor various computer resources, networks, storage availabilities, and inventories in the system. Moreover, DysDig and Prometheus are adopted for containers, which is very essential in the changing world infrastructure.

Companies like Amazon and Google usually use AWS, Cloudwatch, and stock drivers to ensure that operation is up to standards. These programs monitor productivity by watching the performance information of APIs. Though there are other forms of data transfer possibly enabled through CPU utilization or configured through notification alerts whenever the threshold is exceeded by the developer, it is worth noting that AWS user tends to inject their own metrics in the system to generate a great comprehensive analysis.

There is also an APM tool used in monitoring as the bottleneck target for the application framework. It allows the system to detect any defects that may exist in the system and deal with it appropriately. It is a unique tool compared to other tools used for monitoring the system functionality, and it enables one to create a good interface of the DevOps. On the other hand, there is a modern reigning market leader known as the New Relic which is installed for monitoring following a good reason. Precisely, it pinpoints the bottleneck in the system immediately it appears. Getting these kinds of tools ensures that there is no crackdown in the system and one can easily monitor everything going on in the system thus ensuring that the whole process is secure and up to date. Additionally, the use of AppDynamics has been integrated into the system to monitor various activities in the software functionality, which later determines the outcome of the DevOps. It also offers a clear view of the user transaction data through its performance of the metrics installed in the software.

Currently, there is great development in this technological error where tools like BigPanda and PagerDuty offer out-of-the-box integration solutions to achieve data aggregation. DevOps can easily view alerts in the system from this application, making it more useful and interesting. Moreover, these modern tools currently used in system monitoring are of great impact to the way DevOps are run to bring out the correlations between events.

It is important to remember that there are also other multiple systems integrated to modify the user’s system of every organization. Most of these monitoring tools are displayed in big screens for easy analysis and verification of defaults which may occur in the system. Besides, there are ways in which alerts can view by the management efficiently with no much ado on the side of developers. The operation team may have peace of mind knowing full well that all their worries are taken care of by the monitoring tools installed in the system.

Network Tools

Since the DevOps software development is mainly concerned with communication, a collaboration between products and management, the software is essential for its operations professionals. From this perspective, there are management tools that can be used to make the whole process more successful.

Git is extensively used and modified across the software industry as a Source Code Management (SCM) tool by DevOps. It is commonly preferred by most of the developers and remote teams due to its nature as a source contributor due to the fact that it provides open-source progress tracking on work development. Moreover, many software companies use it to create separate branches whereby they come up with new features as a great tool for experimentation. GitHub and Bitbucket are regarded as the best two online Git Repos hosting services developed by DevOps.

Furthermore, Jenkins, who is an automation tool for many software development teams provides automation platform necessary at different levels in pipeline delivery. Since it provides more than 1000 plugins in the current software industry, it became a popular tool in the software ecosystem. It is simple, to begin with, Jenkins as it runs out-the-box on Windows, Mac OS, and Linux, and it can easily be installed with Docker. Its server can be configured and set up through a web interface, and firsttime users have the easy option to install it with commonly used plugins. In that case, it provides easy deployment, and the creation of new codes can be done as fast as possible when incorporated correctly. Moreover, it fosters the effective measurement of success in each step of a pipeline delivery. Even though some complain about Jenkins’s “ugly” and non-intuitive UI, a software developer can still find everything they want using this tool without a problem.

Another CI/CD solution tool with similar characteristics to Jenkins is Bamboo due to the fact that both can be used for automation of delivery pipeline, from build to deployment. The difference that exists between Bamboo and Jenkins as a software tool is that Bamboo comes with a price tag while Jenkins is open-source. So choosing a proprietary software depends entirely on the user’s budget and goals. The reason why Bamboo has fewer plugins, which number only 100 in comparison to Jenkins 1000+, is that it has many pre-built functionalities that have to be manually set up in Jenkins. Besides, Bamboo allows for easy access to built-in Git and mercurial branching workflows and software test environments because of its seamless combination with other Atlassian products like Jira and Bitbucket. So it is quite evident that Bamboo can save developers a lot of configuration time as it also brings about a more intuitive UI consisting of tips, auto-completion, and other attractive software characteristics, thus making many software developers prefer it over Jenkins

The number one container platform is Docker since its successive launch in 2013, and it is still experiencing continuous improvement as a widely-recognized tool in the software industry. Containerization has been made popular in the highly developing technology world by Docker because it allows for easy distribution of development and quick automation of deployment of software apps. Easy separation of applications into separate containers is made possible by Docker, enabling them to become more portable and more secure. Docker containers can serve as substitutes for virtual machines such as Virtual Box. Dependency management is limited when Docker is used as a software tool because dependencies can be packaged within the app’s container and ship the whole thing as an independent unit allowing developers to run the app any machine or platform without any difficulty. Docker, Jenkins, and Bamboo, if combined and used together with one of these automation servers can enable further improvement in the developer’s delivery workflow. Cloud computing is also one of the greatest characteristics of Docker as a software tool making the major reason why cloud providers such as AWS and Google Cloud added support to Docker as it greatly simplifies the task of cloud migration.

Kubernetes is a container orchestration platform that was invented by a couple of Google engineers who were more interested in finding the solution of managing containers at scale. Though it is still new in the software industry as it was launched in 2005, it works perfectly well with Docker or any of its substitutes. Easy automation of the distribution and scheduling of containers across the whole cluster is made possible by Kubernetes because it can be deployed to a cluster of computers so that users do not have to tie their containerized apps to a single machine. Its content is made up of one master and several worker nodes, and it pays attention to almost everything. Implementation of predefined rules is made by the master node and distributes the containers to the worker nodes Redeployment is made necessary by Kubernetes if it notices that a worker node is down.

Puppet Enterprise allows for convenient management of developers’ infrastructure as code because as it automates infrastructure management, developers can deliver software faster and more securely. For smaller projects, a puppet will provide developers with an open-source tool because it is a cross-platform configuration management platform that enables developers to focus more on their software management skills thereby improving quality in software deliveries. When dealing with a larger infrastructure, Puppet Enterprise will be valuable with extra characteristics such as Real-time reports, Role-based access control, and Node management. Easy management of multiple teams and thousands of resources is possible with Puppet Enterprise because it will automatically understand relationships within a given infrastructure, and will handle failures effectively because it also understands how to deal with dependencies. It can easily integrate with many popular DevOps tools because it contains more than 5000 modules that can assist in skipping failed configuration thereby making it convenient as the best DevOps tool in terms of management strategies.

Raygun as a software tool will help accurately diagnose performance issues and track them back to the exact line of code, function, or API call. It can easily detect priority issues, thereby creating effective solutions for software problems. Raygun brings Development and Operations together by providing a single source of truth for the entire team, the cause of inaccuracies, and performance problems because it can automatically link errors back to the source code.

Log Monitoring

Before choosing any log monitoring tools, there are several factors to consider, including the functionality of these tools. There has recently been greater interest and focus on creating log management tools trained with machine learning. There is a range of features that are integrated into the system requirement, which ensures that the system is stable and sustainable to end-users.

These may include the range and scalability of the tools to be incorporated in the system, which covers the product user expands in which the DevOps source of logs is monitored. One should always take note that the logging tool has to collect and manage all the logs from the system component through the server monitoring enabled logs. Since access is provided from a central location, there is a need to create speed from all the logging tools used in DevOps. Therefore, there is a need to keep an eye on the process when seeking different solutions to the system processes at hand.

On the other hand, one may look for advanced aggregation capability when selecting suitable log tools to be used in monitoring the system. In most cases, one can be overwhelmed with unnecessary data collected during logging time. When looking for a good aggregation tool, software which should be considered are those who have shared characteristics that ensure that log origin in servers, database, devices, and applications are free from error regardless of the user’s actions. Moreover, there is the need to observe the intelligent pattern recognition in log monitoring tools proposed by the developers. To establish an intelligent pattern on DevOps, machine learning on the contemporary logging tools must be observed. The organization needs to create such chances for people to have great knowledge of machine learning promotes more knowledge on what to do and how to do it where DevOps is concerned. In this case, there is a need to learn the standard log syntaxes used on various systems for a much analysis needed by the developers and the operation team. It gives a platform on how the logs look like and how they are being incorporated into the system.

In DevOps log monitoring, there have been open-source tools that have been integrated into DevOps software to deliver efficiency of the application through logging tools. When monitoring the logs of the DevOps system, some tools should be incorporated into the system to make it more efficient and up to the requirement of the users. In this case, monitoring cloud platforms by the use of application components for processing and analyzing logs are made essential to make it more stable. Moreover, the availability of the application can be backed with other forms of logs, which make it useful.

The fact that the proprietary logging and other monitoring solutions have to remain expensive in the market, much focus has been shifted to targeted tasks whereby container cluster monitoring have been integrated to make it perfect. These tools prove to be holistic alerting and monitoring toolkits, which is responsible for creating a multi-dimensional data collection and other querying amenities.

According to Linux foundation in their guide release report on open cloud trends that are used to modify the system, the guide expounded on the third party annual report with a comprehensive state of cloud computing on logs. They incorporate the tools necessary for open cloud computing, whereby the logging monitoring is comprehensively expounded. Besides, the report entails the download aggregates necessary for analyzing the whole process thus making it a global community that illustrates different containers, monitoring and sharpening cloud computing system. From the report, one can easily access links for the descriptions of the projects intended to create a conducive environment for better performance. All these are enhanced through log monitoring, which is put in place to guide the initiator of the project and the developing team from slide back in the system. No one likes to fail, and when it comes to DevOps development, creating a sustaining application is important since it enables one to have full control of the software.

There is the continued use of Fluentd as a source of data collection tool on the logs made in the system with the aid of unified logging layer. The tool is modified in such a way that it incorporates JSON facets of processing log data through buffering, filtering, and outputting logs to other multiple destinations. Besides, such achievements in the system are enhanced through fluentd on the GitHub system. Contrary to that, most of the developers have found a way of using a container cluster for monitoring the performance using analysis tool in kubernetes. The tool supports kubernetes well, and it also enables CoreOS to operate natively, whereby the adaptation is made possible through the use of OpenShift system of the DevOps.

To understand how all these things are made possible, no need to look far; just search for an expert who understands it well. Technology is complex, and I do not expect everyone to grasp everything am talking about in DevOps, but it is important in the current technological landscape to, at the minimum, understand the main concepts behind the tool. Most of the time, people will lose attention whenever DevOps practices and tools are mentioned; the concept is much more important to those who have developed an interest in technology. How else can one do without technology in this modern world where everything is modified by humans to fit the need? Personally, I spend much of my time on the internet, searching for various features that need improvement. From the research I have done, it is very obvious that most of the influx DB technology is developed through the use of Google Cloud monitoring and logging, Grafana, Riemann, Hawkular and Kafka.

Additionally, the use of Logstash, which is an open-source to data pipeline, enables one to process logs and event data very fast. It is enabled through the use of data from a variety of systems, which made it convenient and effective to process data in the system. Logstash tool is very interesting, and the use of plugins make it more convenient in connecting variety of sources and stream of data which ensures that the central analytic system is streamlined to meet the specifications and the software requirement.

There is also a Prometheus system used by most of the applications in monitoring and as an alerting toolkit in the SoundCloud. In this case, a cloud-native computing foundation has come up with different consolidated codes to make the whole system work. Recently, the software has been configured to fit the machine-centric and micro service architectures in such a way that it creates a multi-dimensional data whenever there is a need for data collection and querying.

Deployment and Configuration Tools

DevOps is indeed evolving, and each day it is gaining popularity among people in the world. Many organizations have gained the traction of this software, which enables them to produce efficient applications and increase product sales in the market. Moreover, this has been enabled through core values like automation, measurement, and sharing towards the organization’s influence. In this case, one can note that culture of the DevOps is strategically used to bring people and processes together in order to carry out certain tasks. Specifically, culture of DevOps is to develop the system by combining different factors to make the whole process work.

On the other hand, automation is used to create a fabric for the DevOps system which eases the culture in the organization while measurements aid the improvement essential where DevOps is concerned. However, the last part, sharing, close the whole deal as it enables the feedback from all other application tools. The customer’s review must be considered more so where decision making is required.

Similarly, DevOps have the greatest concept which supports the whole process where everything can be remotely managed by network, servers, log files, application configuration via code. These code control also help the developers to automate various tests in the system, create database, and deployment process through a cool running of the software.

Let us now shift our focus on deployment and configuration tools, which is the major concept of this section. Here, one must know that the configuration management tools are very important just like the deployment tools used in the DevOps system. It creates best application practices necessary for developing it into full use to the concerned parties. Through manipulating simple configuration files, most of the DevOps team can employ the use of the best development practices, which may include version control, testing, and kind of various deployments incorporated with design patterns. By the use of code, the developers can manage infrastructure, automate the system and create a viable application for users in the market.

Moreover, by the use of configuration deployment and configuration tools, the developers can easily change the deployment platform to be faster, scalable, repeatable, and predictable in order to maintain the desire for state. So the assets are set to work by the desired state that is transitioning by the other parties in the process. This kind of configuration cannot be achieved without considering some of the advantages associated with it.

For the tools to be useful and up to the task requirement, there must be adherence to coding convenience, and all other factors are catered for before configuration into the system. By doing so, the developers can easily navigate the code used and make fine adjustments whenever required or when need arise for upgrading. No system is perfect, and at one point or another, there arises the need for improvement and adjustments which must be made by the developers to fit the customers’ needs derived from the feedback. In such a case, one is required to tread softly and observe all the obstacles that may arise in the course of development. However, the idempotency of the codes must be kept clean during the adjustments. This is to means that all the code should remain ii tacked as long as it is in use. It does not matter how many times the code has been executed; it must remain the same for future development, which may mean upgrading the system. In case one interferes with the code, future development may be made difficult, and sometimes one will derive them from somewhere else thus creating a new avenue for new DevOps creation. Similarly, a distribution design should be configured in the system to enable developers and DevOps operation teams to manage remote servers.

Pull models are used by some configuration management tools in the system as agent servers in the central repository purpose. Though there are a variety of configuration tools used by the DevOps to manage the software, and some of the features truly make it a great situation for others that are involved in the making. Therefore, there is a need for identifying and analyzing these deployment and configuration tools in full. In this case, the information obtained is based on the tools software repositories and various websites that provide the required information.

I will consider Ansible to be the most preferred tool used in IT automation since it makes the application more simple and easy to deploy. It is most suitable in situations where regular writing of scripts or custom code is not necessarily needed to deploy code. It updates the system with an automated language approach, which can be easily comprehended by anyone who cares to learn about the code used in the application. By doing so, there is no agent for installing a remote system in the software, and the information is readily available in Github repository, documentation done by the developers, and the community in which the system is developed.

Ansible stands out due to its features, which makes it the favorite of many developers and users around the world. One can use this tool to execute various tasks in the application ranging from matching the command in different servers at the same time using it at one point end. The tool automates tasks by the use of “playbooks” which are written in YAML file. The playbook facilitates communication among the team members and non-technical experts in the organization. The most important aspect of this tool is that it is simple to use, easy to read, and gentle to handle by anyone in the team though there is need for ansible tool to be combined with other tools to create a central control process.

Alternatively, one can use CFEngine as a configuration and deployment tool in DevOps development and management. Its main function in the system is to maintain and create a configuration avenue necessary in large scale computer sustenance. Just a brief history and working knowledge of CFEngine can be of much importance to some people, if not all, that may care to know much about the revelation of the tool. It was discovered by Mark Burges back in 1993 in an attempt to automate configuration management of the system. The reason behind the discovery was to deal with the entropy bugs in the computer system and to ensure that the convergence is unique and up to the desired state of the configured system. From his research, he proposes a promise theory which was later reinvented in 2004 by the cooperation between agents in the business.

Currently, the use of this promise agent theory has been put in place in such a way that it enables the running servers to pull the configuration in the system, which makes everything better at the end of the day. Though it requires some expert knowledge and for it to be integrated into the system without much ado or error there are some aspects which may cause the system to fail during installation which must be avoided at all cost. Therefore, it is best suited for the experts in the IT industry or those who have used it severely and have learn the unique features to look for during installation.

Additionally, one can intend to use a system integration framework to deploy and configure different applications in the system. Also, it is suitable for creating a platform for configuration management and installation in the entire infrastructure at hand. Its code is written in Ruby in order to keep the system running and updated all the time. The recipe used primarily describes all the series of resources that should be updated in the system, and more importantly, chef can easily run the client mode through a standout configuration called chef-solo. Due to all these factors, one should not forget that it has a great integration, which is a major cloud provider which automatically configure new machines. One should remember that chef has a solid user base which provides a solid full toolset built from different technical background for proper support and understanding of the application.

This post is licensed under CC BY 4.0 by the author.