Five Important DevOps Tools

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Where the traditional software tools and infrastructure management processes failed to provide the sufficient velocity of software products development and operations, DevOps has evolved to be the set of sophisticated practices and skillful techniques that blends together both the teams of development and operations, thus resulting in the high productivity and effective marketing in a short span of time.

And the DevOps engineering would be of utmost service to an organization or an enterprise when it is powered by some of the most important key tools that would boost the entire application lifecycle.

Here are the five most popular DevOps tools  discussed.

Jenkins

In its entirety, Jenkins practices continuous integration (CI) that seeks to consolidate all the developers working on a project into a shared connectivity and integrate together all the DevOps stages. This Java open-source automation tool leads the whole software development and testing through a seamless process and supports the developers to integrate the changes and helps the users to make use of a fully-developed build.

With the efficiently built bug databases and version control systems, the Jenkins can be integrated together with them, thanks to the some of the best plugins available in the tool. These plugins can be availed for all the necessary stages of a development or testing, plugins such as Maven, Git, Selenium, Puppet, Nagios, and Amazon EC2, important development lifecycle processes of all types that include static analysis, building, packaging, continuous monitoring, version control systems, configuration management, continuous deployment, and continuous testing and virtually every stage are under the administration of Jenkins.

How Jenkins Works

The fundamental proceedings revolve around the process where the application developer works with the source code in the Git repository in order to get rid of the bugs.

Some of the prevalent and the commonplace errors such as build failures and other architectural issues can be located and the code is transmitted back and forth to the continuous integration server.

Once the source code changes are committed to the Git repository, Jenkins server checks the repository and, if cleared, dispatches the outcome to the testing stage from which, at last, the build is hurled to the production server.

 Advantages of Jenkins over other tools

  • There is a rich set of plugins to simplify the code deployment and the infrastructure provisions. And even if the desired plugin does not exist, it is possible to code it.
  • It is designed so as to be portable to all the platforms since it is built with Java.
  • Jenkins has a stronghold of community support and it is easy to install.
  • Since it has a widespread amount of plugins and adopted globally in the DevOps settings, it is a better choice among the other tools that practice continuous integration.

Ansible 

Ansible does wonders in IT orchestration, the process which allows the tasks to run sequentially and create a chain of processes that are operated across multiple devices and servers.

Some of the crucial elements of the DevOps practice like application deployment, configuration management, and task automation are the speciality of Ansible.

It does not require us to set up a separate client-server environment since it is written in Python and installed on the remote host, a facility with which we can just run it from any of the available machines.

Rather than just administering a single system at a time, Ansible is effectively designed for multi-tier deployments. It can be easily deployed due to its capability to work even without any custom security infrastructure.

Ansible’s Design

Ansible’s architecture functions effectively by its capacity to connect to the developer’s nodes. Then the resource models are designed with the Ansible modules programs with the written code and hurled to the required state of the system.

These modules are then executed by Ansible by default over SSH. It is important to note that there is no databases or servers are needed because any machine could contain the library of modules.

In case there arises a reason to change the content, a developer can work with the text editor, terminal program, or the version control system.

Why should we consider Ansible?

  • No further dependencies are imposed by the Ansible management systems on the working environment.
  • On the managed nodes, it is just enough to add the network-level utilities like OpenSSH and the Python language since no agents are deployed to the nodes, thus ensuring the best security features.
  • A well-written Ansible playbook would be helpful to avoid typically undesirable effects on the management systems.
  • The playbooks use YAML, an easily readable language, with pictorial descriptions, making it an easy-to-handle tool.

Docker 

In the complete disregard for the produced environments and the final build, we can produce, run, and deploy the applications as a virtualized single package (a process that is called “containerization”) with Docker.

There may be several fragmented components in an application such as the configuration files, libraries, and binaries that can be wrapped up easily and deployed as a single package with this container technology.

Thanks to its agility, even though the various environments and the platforms on which it is deployed are incompatible, Docker containers can perfectly be work on those platforms.

When all the necessary tiny bits of singular engines are combined together in a single domain, developing any software and testing it proficiently is not that difficult. With Docker, the developers do not have to set up a unique development and testing environment every time a product is to be made. Just by building a good quality code, all the development efforts comes to the fruition.

Any of the developers can work on the same project with the same settings since Docker allows to set up environments that are strikingly similar to the production server. IT Sysadmins and operations team can develop pure software applications on this platform regardless of the local host environment.

Where Docker fits into DevOps?

In the DevOps ecosystem, Shell scripts are not needed and the complexity of configuration management is abstracted by Docker, thus making it easy for both large and small deployments.

By allowing the DevOps applications to run on the public cloud, in-house servers, laptops, and private cloud, Docker is notable for its highly portable and flexible facilities. In order to run isolated processes, Docker provides lightweight containers, unlike the hefty virtual machines, by implementing a high-level application programming interface (API).

With the support of Jenkins, the Docker file is brought forth from Git, where it is built with the Docker image and dispatched into the Docker registry. Then the Docker images in the Docker registry are pulled in. Thus, the entire set of sporadic applications are effectively provided for the DevOps environment.

ELK

ELK, a finest open source log analysis platform, is widely celebrated for the management solutions and the log analysis it provides for the DevOps culture. There are three resembling open-source products are making up the ELK: Elasticsearch, Logstash, and Kibana.

To perform the most complex and other full-text searches, Elasticsearch serves as the search engine tool. Just before the data is sent to Elasticsearch for the purposes of storage and indexing, that particular data is processed with Logstash. And in the open-source environment, a developer can create graphs, improve the visualizations and view the log messages with the support of the Kibana visualization tool. With ELK, the value extraction can easily be done in the end.

ELK fits well into the DevOps practice because the Stack is the major support for all the IT operations by analyzing and monitoring the entire infrastructure and by providing business intelligence, security intelligence and monitoring the application in progress.

ELK’s Core Functions

The developer input that is the software in its fullest formations, stacked into a set of distinct logs, the logs that are sent to the Logstash where it is temporarily stored before getting indexed.

Here the Logstash will listen to the combination of the logs and transform them into the JSON format before sending them to the Elasticsearch. These format-converted logs can be viewed from the Kibana, which, since it is the visualization face, serves as the user interface of the elastic search cluster.

Splunk

Splunk is a software system that is with a leaning on analyzing the big data generated automatically by the computer systems. The real-time data is gathered together and stored in a storehouse-like repository and with the data fed by the repository, it creates the reports, dashboards, and the visualized materials.

In DevOps, especially, the contribution of Splunk is in the areas of data monitoring, application management, and web-based analytics.

With Splunk, for the solutions along with the areas of marketing of the product, the developers can avail some of the free add-ons and apps across the entire application delivery pipeline for ultimate visibility.

Splunk for the Real-time Visibility

The fundamental dependencies between the developers and the IT operations and vice versa are vital in order to fix all the issues during the development and the testing stages of a software platform. Splunk does it better by supporting the final release management personnel and enables the delivery of the code to the production even faster.

In DevOps, since it combines both the development, testing, and the final marketing of the product, high visibility through the entire process is a must as a single strike, undetected or failed to be removed, would cause the entire lifecycle a damage that would be irrevocable.

Now Splunk offers a real-time and the critical visibility, thus supporting the developer to systematically process and reach out to the customers with the desired product outcomes.

The key word here is the real-time insight. Splunk goes through the data that is stored on the repository, which, in turn, would be used to build the application. Along the development or the testing stage, it checks the code and if there happen to be any worrisome issues it just offers valuable and workable insights all across the delivery lifecycle.

The availability of Application Lifecycle Analytics provides the much-needed cognizance with which the monitoring, planning, developing the code, building the application, testing the product, releasing, deploying, operating, and finally, monitoring can be effectively done with the Splunk tool.


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