Definitions:

DevOps – DevOps is the change in culture and practices of the SDLC process which enable the organization to produce high-quality software and continuously deliver the software. It has been originated from two words, Dev and Ops. DevOps has some phases through which it works. DevOps is the collaboration between development and operation teams to ensure continuous integration, development, test, deployment, and monitor. After DevOps came the development and operation team is no longer working as ‘siloed’. DevOps is much better and faster than any other traditional model in the current era because it has removed the vulnerabilities between culture ad practices which was stopping other organizations to keep up with the time which was demanding a faster and reliable process.

DevSecOps – DevSecOps is for development, security, and operations. Its main motto is to make every employee working under a project consider security policies also important like other works, so the product will be saved. DevSecOps is all about security measures from the development phase to the monitor phase. Organizations can implement DevSecOps to break down silos between development, security, and operations so they can maintain the proper security over their products. DevSecOps uses some security testing tools to integrate into their CI/CD process like –
⦁ Static application security testing (SAST)
⦁ Software composition analysis (SCA)
⦁ Interactive application security testing (IAST)
⦁ Dynamic application security testing (DAST)

SRE – SRE has been originated from google company. SRE’s main aim is to solve the operational problems right from testing to monitoring software when it is deployed in the market. The main focus of creating it was to remove the vulnerabilities of operation parts to make the product effective and reliable. It removed the ‘silos’ from all departments and made the operation teams work together to produce an efficient product, e.g – Somewhere you might have experienced you have never felt any downtime (the app is not working properly) on your Google apps even when Google teams are pushing updates over that particular app. This is like the SRE team works making your apps more trustworthy without making you feel any disturbance. SRE works with the collaboration of software engineers. SRE somewhere automates Its operation tasks and accelerate software delivery with minimizing risks.

Best Resources to Learn DevOps, DevSecOps, and SRE.

There are some resources where you can learn about all three of them is Youtube, Blogs, platforms that provide certifications, Fb Group, etc. I am going to share some of the channels and place them.

Youtube – These are some Youtube channels Devopsschool, Debug.school, and artificial intelligence universe, where you can learn DevOps, DevSecOps, SRE. Here you will get videos frequently. You can expand your knowledge here because the videos which are uploaded here are directly from our classes where some students like you would be learning at that time. There are so many video’s has been uploaded till now related to this course. These channels have been made only for DevOps and their tools-related courses, So these channels are really helpful to learn DevOps, DevSecOps, and SRE-related topics and tools. We have premium membership as well that you can buy every month where you can get your questions answered on daily basis as well as you can get access to a full playlist of DevOps-related tools like Docker, Kubernetes, Python Etc, which you can get normally in youtube. This facility is only for a premium membership program.

Fb groups – This is the FB Group DevOpsSchool where you can get the details related to DevOps, DevSecOps, and SRE. Here we upload the contents related to courses and some knowledgeable things and other important information which can expand your knowledge as well as keep you aware related to DevOps, DevSecOps, and SRE. It’s a very important place which will not make you feel regret. DevOps India is having fun on this platform with more than 5000 members in very little time. This is a public group from all over the world who are interested in DevOps. It proactive and updated group which posts, share, comments daily. It has a strict rule not to promote any advertisement, it’s completely focused on updates, sharing information, and discussion about the topics. You can see almost all participants participate in comments to share their views, even though you can find a job as well where recruiters post their vacancies here.

Blogs – https://www.thedataops.org/what-is-devops-life-cycle/, https://www.scmgalaxy.com/tutorials/what-are-devops-devsecops-and-sre-and-differences-among-them/, https://www.aiuniverse.xyz/software-development-lifecycle-sdlc-beginners-guide/ these are three blogs link where you can get some DevOps, DevSecOps, and SRE knowledge as well as differences between them. So do check these blogs, it will help you more to understand all three of them. And also you can visit our BestDevops blog website where you will several blogs related to DevOps. Blogs have always been an important part of our learning that expands our knowledge in desired topics. So always be connected to these blogs if you are seeking knowledge. A blog is always written after seeing so much information in several places, so Blogs are really important to read because you might get that information that you can’t get anywhere else. It has always been seen google plays a much important role than any other platform, that’s why Google always tries to give you the actual and correct information to help you out. So do check the Blogs.

Websites – These are the websites of an institute DevOpsSchool, ScmGalaxy where you can get the details of the complete course related to DevOps, DevSecOps, and SRE. Also, you will get a tutorial tab that has been made to help participants with DevOps-related issues which can be accessed without any charges. DevOpsSchool provides training and certification for all three of them. This Platform is one of the best platforms in the world which provides training all over the world and has made some carriers as far now in DevOps. Some of the places where it provides the training are Hyderabad, Bangalore, London, Amsterdam, Mumbai, Singapore, San Francisco, Europe, Australia, etc. DevOpsSchool provides online and also offline classes to their students just to help them according to their comfortability. There are so many tabs on this website through you can do so many things like Solutions, classes, etc. You can contact us through this website as well that is of course to help you guys with your issues. Through this website you can request to become a trainer, a trainee or can get training for your employees as a company or consulting firm.

Certification – There are some certifications to enhance your skills and knowledge about DevOps, DevSecOps, and SRE. The certifications are – Master in DevOps Engineering (MDE) – Including DevSecOps and SRE , DevSecOps Certified Professional Online Training, Site Reliability Engineering (SRE) Certified Professional Training. There are several topics will be covered in these certifications as well as you will be provided real-life-based projects which will boost your carrier also it will be more helpful for your knowledge and resume. DevOpsSchool offers a more friendly and comfortable environment where you can free to ask anything related to topics and our trainers are ready to help you all the time with PDFs, video calls, and many more. For those persons who want to build their carrier in DevOps, these certifications are very important as they not only provide the knowledge they improve your skills with real-life-based projects as well. So get it if you want to become a professional on DevOps.

Best DevOps – This is a website where updates come daily related to DevOps and other technologies like what’s happening with that specific technology or DevOps right now, what updates have come, how to use that Etc. This is the place from where you can be the first to get the latest news. BestDevOps is a platform of DevOps updates and news where you can read about DevOps-related updates, events, news, tutorials, tips, and much more. Through the contents which have been uploaded here, you will be helped a lot if you are a DevOps enthusiast. Just be with this platform and you will be up to date on regular basis. We have gathered all the blogs from different companies, different country and experts of DevOps in one place just to help you guys. BestDevOps is the DevOps portal for a website that covers a broad range of areas in DevOps.

I hope this list will help you out to connect and hear from DevOps professionals who provided information about everything DevOps-related you needed to know.

Conclusion

In this blog, we discussed the Definitions of DevOps, DevSecOps, and SRE and the resources through which you can learn about all three of the courses. The above-given resources are very important to you guys because all are important channels to learn about DevOps, DevSecOps, and SRE. You can learn DevOps from all the resources free of cost but only DevOpsSchool is the channel where you can go for the certification in any specific tools or courses.

Training Place

DevOpsSchool is a very important platform to get trained as it is one of the best platforms and to get trained from the best ones is how feels only a trained person can say. So do check it once if you are looking for certification and to boost your carrier. you can learn more from the above-described details.

You know the real-time of a nowadays trends. What the innovation and enhances are occurring in the mainstream of IT infrastructure. Daily software technology complexes are engaging with so many handy tools. Every industry and business have their worth and priorities according to the needs and requirement of software delivery process. The whole process is running through the machine-generated data. Each and every hour for entities and firms has become impossible to live without any SaaS feature. A business or organization has thousands of servers. Each server generates data. The goal is to monitor large amounts of data being generated by thousands of servers. If you want to be a Splunk user specialist. So, you shall adopt the policies of some technical advances. You may be going with certain use- cases.

So, you can generate the ability to take a scale of your industry or organization. It manages the developers/testers for investigation/troubleshooting issues in the application/environment. It helps to support/operations teams for monitoring the production environment and the security team to monitor the system for possible attacks. So, Customers can capture the data related to business. Doing compliance and auditing PCI/SOX/HIPAA log review and retention requirements. If you are looking for a lead-generating IT software carrier. Suppose, you want to take dynamic support for your services. So, you can go with Splunk advanced power user. They have developed and indulged into deep dives knowledge and skills qualified in complex collecting, searching, analyzing, visualizing the machine-generated data and reporting commands, advanced use cases capabilities of real-time feedback, performance monitor, payment process, promotional support, and practices for various building dashboards.

What are the benefits of Splunk advanced power users?

If you want to be more advance and efficient in the manipulating functions of Splunk power users. If you have seen the outcomes of functionalities of the advanced power user. Let’s come to the points of software performances. What we observe is that sometimes it’s lagging and a few hours later we find it performs efficiently. After achieving work-flowing ideas, you know the behavior of software applications. Every software stuff comes with certain features and beneficial causes. So, there are some sorts of benefits such as:

• Maintain your scalability at any firm: It makes you sufficient and specific for upcoming data analysis situations. With daily practicing strength, it reaches you hike at any industry or firm.   
• Resolve data issues more efficient: It creates awareness in you for any threat attacks and efficiently, you can fix errors multiple times with deep impacts.
• Makes you extraordinary: Makes you prodigy to the other users and develops skills to solve any data issues with extraordinary mind concept.   
• Dynamic data handling: It gives you dynamic support for instantaneously multiple server-generated data with excess control and configuring.
• Multiple mind-controlling concepts: For the data-driven industry, it gives you multiple mindsets to maintain the steady flowing data and manages the data information with analyzing dashboards.  

Why we should learn Splunk advanced power users?

After getting the full ideas about Splunk advanced power users like what is it? What does it work? Benefits. Now, the real situation comes that software launches come in the IT industry or markets. You using it and test in software applications for knowing their performances. The real query comes all after is that why it is necessary or essential to learn? You know the data is everywhere and you can’t live or walk without a single inch of it. So, you can get command over the universal forwarder, heavy forwarder, indexer, cluster master, search head, and license master. One of the most valuable reasons is that some sorts of Splunk competitors are ready to adjacents such as logDNA, elastic search, sumo logic, and solar winds Loggly. You have all the purposes to choose these techniques and strategies to raise your growth. If you fulfill all responsibility with enthusiasm. The certification would make you eligible for the regular performing and managing task performances of Splunk power user behavior and it will reflect the healthy environment with the high scope of job opportunities in certain corporate industries or organizations.

How our Splunk advanced power user course/training would help?

Meeting with all learning stuff. I’m going to elaborate on the monarchy software system. These tutorials will make you intelligent and faster as a service and give you the specific mind mapping concept. The courses of this training program would empower you to move in the correct direction with an advanced learning path. These courses would help you to understand the Scenario-based examples and hands-on challenges that will enable you to create a substantial search, analytics, datasets, reports, alerts & dashboards. It will also introduce you to Splunk’s datasets features and axis interface.

After getting the learning stack. Let’s come to the point of certification. You know that every company or corporation. After the learning process makes you clarify in certain courses or degrees. So, this will makes you developer as developing dashboards based on customer requirements and experts in Splunk SPL, XML & UI configurable items with various career paths like Splunk admin, Splunk developer, combine package of Splunk admin & developer with expected average salary process and tremendous job options for future leads. This certification demonstrates the healthy performances and suitable environment of Splunk advanced power users and makes you eligible for exact performances in any IT firm or MNCs (multinational companies).

Related video:

Before defining elastic search. I give you a brief explanation that you are in the field of data science and big data and you may have heard about elastic search and you might be wondering that how does search engine technology helps you in extracting meaning from your data at scale. How do you get answers in milliseconds? Let’s talk about that it started off as a scalable lucence version.it adds a horizontally scalable search engine. There are shards and each “shard” is an inverted index of documents but not just for full-text search.it only can handle structure data, & can aggregate data quickly and often doing a faster solution than Hadoop/ spark /flink/etc.

Often, you listen to engineers. Their sorts of priorities and various forms include in it. Now, among all engineering streams. Elastic is an integral part of the elastic search engineer. Let’s talk about an elastic certified engineer. An elastic engineer possesses the ability and skills on performing a necessary task to build complete solutions of elastic search and monitoring the functions of install, configure, and manage the cluster, indexing data into the clusters, query, and analyzing the index data.

What are the benefits of elastic engineer?

 Getting ideas about the elastic engineer. Now, it’s time to take action on benefits. What’re benefits do you getting from elastic engineering and you just can’t say words for it. After the completion of the exam and certifications then you behave as you saying. There are some of the benefits according to the ideology of elastic engineer as categorized:

• Scalability
• Really fast
• Multilingual
• Document oriented
• Auto-completion & instant search
• Scheme free

Why we should learn elastic engineer?

If you are talking about the elastic engineer then first you should assemble the conceptual knowledge about why you are learning or necessity along with the cause? Through, the learning path of elastic certification. It’s proven to be fruitful in the sectors of IT entities and organizations. Due to its Performa-based courses/training along with the professionals. That will give you the using concept with skills, task building techniques on the elastic search on the basis of two courses which are packed with elastic search engineer I and elastic search engineer II. Its certification is based on the elastic search engineer exam and after completing the procedure of all. It will create awesome technological advances, feed awareness about the industry and its impacts on the environment. One of the prominent roles of elastic certification is the job opportunity, creativity, and innovation in favor of drastic enhances.

How our elastic engineer course/training would help?

After taking the course structure of elastic search engineer. It will create ideology about the elastic search and perform functionalities in the areas of sharding techniques & strategies, tuning for ingest speed, monitoring the stack, backing up your data, and query theory.

Getting command all over the ability and data experiences will make you job achiever, exam certification and build techniques to qualify in performance considerations, high availability, always use bulk, increase refresh, disable replicas, templates/mapping, monitoring, delete snapshots, general things to avoid, general things to do, and tools for console & search profiler for better technology development. These training courses will help you to understand the advances in elastic search, develop skills, better performances and produce high-paid salary job opportunities towards the IT infrastructure. In this matter, I would like to prefer this course in the industry of software development for your future goals.

Related video:

 Now, we are going to learning about Splunk. It’s a very interesting and trending topic then Splunk is a software platform or tool that is used to analyze machine data. But have you ever thought that why analyze the data? Sometimes you will be aware of exponential growth in machine data over the last decade. This was mainly because of the growth in numbers of machines and usage due to its devices.

Analyzing the machine data is essential because of the problems their solution is hidden in data. Analyzing the machine data will help us to understand the customer behavior and requirement. Which will, in turn, help us to provide better service. The machine data will help us to alert the systems admins about security issues and system failures. Configuring the data also helps us to improve the machine functionality. However, analyzing the machine data is not an easy step. So, how do we do that? Let us look, how to do that? Suppose that you are working as a system administrator in some entities. In an entity, the data can be generated from different sources, such as sensors, network applications, cloud services, internet-using, and mobile services.

What are the benefits of Splunk?

Splunk is a handy tool to bring it into a data movement. You might be wondering why organizations are opting for spunk. Well here are some of the benefits of Splunk users:

• Input data can be in any form
• Searches using SPL (search processing language)
• Directly stores the data
• Not a single failure
• Fast installation
• Easy scalability
• Retention

Why we should learn Splunk?

After getting the whole process of Splunk. Now, let’s take a focus on learning methods. Why do we need to learn about Splunk? Every single aspect of our life is used to data. We need to monitor the large amounts of data that are being generated to improve the functioning of the systems. Actually, the prominent part of this is to collect all sources of data through multiple enterprises such as Amazon, Spotify, Google, Instagram, Netflix, Microsoft, Alibaba, Myntra, Linked In, Snapchat, and Domino is examples of big companies. These enterprises have so many applications along with this collect data and analyze as copious data. After the copious data, taking processing and analyzing the data. Through it, you may increase your profits and efficiency.   

How our Splunk course/training would help?

If you want to learn spunk then you might become a software developer, Splunk architect, spunk admin, and Splunk developer then this course tutorial will help you to understand the components of Splunk, services, spunk customers, its use case, adding data in Splunk and more career options. This training would help you to understand the Splunk software platform from beginner to advance level. To achieve these skills, you may reach yourself to be the next level and apply it in real-world scenarios.

Related video:

Under this section. We are going to talk about Splunk power user in which is derived from the word spelunking and now, it works on some methods such as log monitoring, data analyzing, data visualization, machine-generated logs, website, mobile devices, sensors, security devices, and many more. Few methods are describing as log monitoring whenever you are going to work with software developers team, monitoring team, or anything then the role of Splunk will come in data analyzing, visualizing, websites, mobile applications, sensors, and security devices. When sometimes hackers make efforts to hack websites and security devices then Splunk will come to analyze and improve it for better functioning.  

What are the benefits of Splunk power users?

After understanding the formula of Splunk as a power user. As a power user, it manages and authenticates the functions of software development. Now we are going to estimate the benefits of Splunk core power users as follows:-

• Validates skills and knowledge
• Enhances Employability
• Academic Records Shine
• Higher Salaries
• Digital Badging

Why we should learn Splunk power user?

All you would hear about the process in regard to Splunk power users. Now this time, the question raises that why Splunk power users and learning for what use. Nowadays, data is one of the entities that drive the world today. Actually, the data is oil and you know the worth of oil. All you have is lots of data and how you are using it. Now the condition is that access to the data makes it usable and valuable for the users. The spunk enterprises and cloud makes it highly scalable, speed processing, and high productivity, superior security, profitability, and competitiveness. It shows the signification of the stamp of excellence, a substantial increase in pay, helps you get hired, and clear corporate requirements.

How our Splunk power user course/training would help?

  According to the faster-growing world. It is one of the fastest improving technology. Now, you can learn Splunk much faster than any other technology and this training helps you do just that. Splunk is one of the best automation advances that is finding increased applications for big data and in a lot of industrial entities. We are offering the top Splunk power user course that has been created with massive inputs from the industry specialist so that you can learn Splunk as a power user and apply it for real-world phenomena.

Related video:

What is Splunk Enterprise Architect?

Nowadays, there are huge ups and downs comes in the fields of IT infrastructure that will make you more believe by using it. Recently, I am going to illustrate the Splunk enterprise architect. Now, it starts with the definition. It is a thorough methodology process of deployment and best-practices for the forwarder, indexer, and search head clusters on some stages as accepts the text data as input, parses the data into events, stores events in indexes, & searches and reports. As an architect, certain functions performed by the indexer receive the data from a web server with forwarder instance then indexer takes data from forwarder and sends as events to Splunk index then indexers receives the results from search head and all the information receives by deployment server.     

What are the benefits of Splunk Enterprise Architect?

After mapping the whole process of Splunk certified architect. The real architect manages with Splunk engine. These are attached with the statistically working benefits along with the architect as follows:-

• Manage the inputs: One of the best part is that monitoring the files, detect file changes, listen to network ports, and run scripts.
• Indexing the data: this is one of the ideal practices performed by architects as indexing user & engine control, data routing, cloning & load balancing as well as deployment server.
• Search for the data: it takes action on the distributed search that is performed by users and collects it for continuous monitoring.
• Visualize the user’s data: after searching the data, they acquire knowledge, report it, and scheduling for data.

Why we should learn Splunk Enterprise Architect?

There is a huge ideology and infrastructure about spunk architects. I will give you schematic thoughts that will make you more advance and the best practitioner. By the planning of architecture, the spunk engine is over there in which inputs with logs and files, indexing the server and engine control, manages the distributed searches, and visualizes the positions of users. This will make it more comfortable and easy to monitor the processed data.

How our Splunk Enterprise Architect Course/training would help?

It has a while to take action on a query about the course. This training program will give you awareness of the hand-to-hand situation and malfunctioning that occurs in the spunk engine based on the user positioning. This will give you certification and makes you a Splunk architect for future growth in this tool. I hope that this course will be aware for the better drive in the environment and also cures the error in the software development process.

Related video:

What makes Node.js the best option for the backend development of your software project?

While there is no limitation as far as the availability of programming languages for backend tasks is concerned, most developers favour Node.js over other options. That is to say, the majority of them can be found on the same page as far as the deployment of Node. js is concerned.

There are various reasons for it, though. From easy accessibility to smooth operation with codes, here is a list of some such reasons.

1. Single-source solution for both frontend and backend operations

No doubt, the backend is an important aspect of a software project. By getting it right, you can relieve yourself of the need for devoting more time and effort to its management in future. But you also need to have a neat strategy regarding how you would manage both frontend and backend at once. 

This is where Node.js can make a big difference. You can treat Node.js as the one-stop solution to handle the needs of both backend and frontend. It allows you to accomplish the task in a single language. Among other things, it cuts down the excess workload on programmers and developers.

The attribute that helps fulfil this requirement is the availability of a full-stack Javascript. This attribute makes Node.js suitable for both frontend and backend operations. Based on this feature, it boosts the productivity and efficiency of the development process.

2. Ease of accessibility

Node.js is based on JavaScript which is a common programming language at the global level for it is its ease of accessibility. Due to its wider acceptance, it is also a well-understood programming language across the globe. As a result, a Nodejs development company does not need to worry about the cost factor while hiring professionals for its software development project.

Being widely accepted, it has more dedicated developers with professional expertise and experience. As a result, recruiting or employing Node.js developers is easier for it than other programming languages. What’s more, such companies can make the most of this attribute to hire developers within a given budget.

On average, the cost of deploying Node.js is much lower than the other technologies. As a result, it is suitable particularly for those companies that have a tight budget.

3. Possibility of sharing and reusing codes

Writing codes, no matter how simple it is, is a tedious task. Doing it all over again only serves to add  more to the workload. As such programmers or developers look for programming languages that favour the sharing and reusing of the codes. Node.js is one of the trending options in this regard at present.

For companies that have a separate team of developers to take care of frontend and backend operations, Node.js is nothing shorter than a blessing. With it, the members of both teams share and reuse the codes with one another.  Consequently, the management of both ends of an application becomes quick and hassle-free. 

Plus, the whole process of the management of both the front end and backend of applications becomes streamlined and efficient.

4. Better scalability

Scalability ensures the presence of four characteristic features of applications all at one stage: quality, reputation, efficiency, and competitiveness. As a programming language, Node.js comes equipped with this feature to help developers scale their applications in both vertical and horizontal dimensions.

The best part about the feature of scalability of Node.js is the ease with which it helps developers scale their tasks. By adding fresh nodes to the current system, they can expand an application in the horizontal direction. For doing the same in the vertical direction, they need to reinforce resources that are used in conjunction with single nodes.

Both these steps are simple to conclude which makes Node.js one of the best options from the standpoint of scalability.

5. Top-notch performance

If performance is the criteria for choosing a programming language, then Node.js leaves its other alternatives far behind. If a company needs to process large amounts of data within short notice, then there is hardly any better option than Node.js. Its compatibility with JavaScript code and the V8 JavaScript engine assists in performing this task with finesse.

The V8 JavaScript engine is one of the gifts from Google for the faster processing of vast amounts of data. It enables easy implementation of data by making JavaScript code compatible with machine code.

If it is not enough, there is more to the functioning aspect of Node.js to boost performance. It ensures faster execution of code and facilitates the unblocking of I/O operations.

Additional reasons for building backend with Node.js

Apart from the aforementioned top reasons, there are also some other benefits of using Node.js for app development projects. 

Despite being free of charge, it comes with a wealth of time-tested tools. If you wish to get a solution within your budget without spending a fortune on app development tasks, then Node.js is the ideal option.

Its package ecosystem, which is the largest among the existing programming languages for app development projects, brings a wide range of open-source libraries. These libraries not only maximise the options for developers but also provide the best libraries to them to execute their task with finesse.

Lastly, its active community of developers is another option that motivates app developers of different companies to consider it for backend tasks of app development projects. It is more of a knowledge base or treasure of knowledge than a community. 

Users visit it to get answers to questions featuring various aspects of Node.js. Ever since its inception, it has been gaining popularity. Given the growing demand for Node.js among developers for the execution of backend tasks related to app development projects, it is all set to gain further traction in future.

Author Bio:

James Grills is a marketing advisor – currently associated with Cumulations Technologies, an Android App Development Company in India. He is a technical writer with a passion for writing on emerging technologies in the areas of mobile application development and IoT technology.

Node.js provides a robust server environment for companies to develop web apps swiftly and securely. In a world where taking quick decisions and actions is the key to achieving success in business, Node.js proves to be an excellent platform for developing enterprise applications

Key points of Node.js as a Platform

Node.js is an open-source platform which can essentially run JavaScript codes outside a web browser. With developers writing command-line tools which then run server-side scripts, Node.js can efficiently produce dynamic web page content even before the page reaches the user’s web browser.

Using asynchronous programming and implementing push technology over web sockets in the real-time web applications, Node.js has been able to transform the process of web & request-response. Now, the server and clients can freely communicate and exchange data and enjoy an unrestrictive two-way connection. Developers can share the same piece of coding with the client and server side through Node.js’ asynchronous programming.

According to a survey held back in 2019, Node.js was deemed as the most popular platform among developers in the category of “Frameworks, Libraries, and Tools”. According to a user survey held for Node.js users, more than 40% of Node.js programmers use this platform to develop enterprise apps. A majority of developers of this percentage were surprisingly working for Fortune 500 companies. With numerous companies claiming that Node.js increases developer productivity, the number of downloads of this platform is steadily increasing by a whopping 40% every year.

Benefits of Node.js

Before delving into the detailed reasons as to why enterprises should hire a Node.Js/ React.Js Development Company for their next app project, let us look at some quick benefits of using Node.js as a platform:

• The Node.js platform facilitates quick development of applications.
• It is fast in executing codes in other languages.
• It is highly efficient when it comes to bridging the gap between frontend development and backend development. 
• It provides LTS or long-term support. 
• It allows developers to reuse application codes and build cross-platform desktop apps.
• It is ideal for microservices in companies which are essentially breaking up the application into smaller parts and then scaling and deploying them individually.

5 main reasons why enterprises should consider Node.js as their app development platform

Node.js provides you with several benefits to prove its case as one of the best platforms for enterprise app development. Here are five top reasons as to why you should consider Node.js for developing speedy and efficient enterprise applications:

• Node.js allows cross-platform app development.

One of the main reasons why companies time and again comes back to Node.js as their preferred platform of app development is due to its ability to build cross-platform desktop apps. Node.js allows users to use platforms such as Electron and NW.js, and make desktop apps that work with any kind of operating system.

Developers, who might not be skilled in languages such as Objective-C, Swift, Java that are used to build native apps, can still create web and desktop apps with the help of Node.js. These developers can reuse their codes from their web applications and implement it on desktop applications which have compatibility with Windows, Linux, and macOS.

Node.js dismisses the need for a Mac machine to build macOS applications or Windows machine for building Window apps with its developer tools. Every significant developer tool which is related to Node is cross-platform. Moreover, Node.js allows its users to get exclusive access to Google Chrome Developer Tools which ultimately gives them all the necessary equipment to debug efficiently and provide better desktop versions of the application. 

• It does not have a steep learning curve.

Node.js is very easy to learn and master. Without a steep learning curve, developers can easily understand the workings of this platform. Furthermore, developers who were using a different platform earlier can easily shift to this platform without particularly extensive knowledge about it. The platform allows developers to use JavaScript to build web applications which is convenient and easy to learn.

With Node.js as the base, developers can easily build scalable applications that work on both Windows and Unix. Enterprises usually turn towards Node.js as their app development platform due to the ease and convenience of finding developers, or app development companies that provide high-quality professionals, adept in developing through Node.js.

• Node.js provides high-quality performance.

The platform is essentially based on V8 runtime of Google Chrome which is written in C++ and which overall, acts as a major advantage for the programmers. They can build applications which are compatible with several operating systems at a time without necessarily re-coding for every new OS. Moreover, both the Node.js platform as well as Google’s V* receive regular updates for utmost optimization of their performances. 

Node.js can easily manage multiple server connections simultaneously due to its single-thread architecture. Thus, the platform will not create any additional threads for each new request that it gets. This not only makes the app operate faster but also prevents excessive usage of RAM.

• The platform is known to provide LTS or Long-Term Support.

The Node.js platform provides its users with a long-term support feature which, according to a survey held in 2018, more than 60% of developers deems to be important. With the IT world’s fickle nature and dynamic changes, it is not a surprise to see technologies falling apart before they could even offer LTS. Node.js is equipped to counter this trend and provides its users with consistency with its support and updates.

• Node.js provides an NPM or Node Package Manager.

For enterprises who are dwelling over the privacy and security concerns over their app development platform, the Node.js platform provides an enterprise version of its Npm. Npm offers a range of benefits to the users. From installing and using small modules to identifying and replacing unsafe code, the enterprise version of npm can improve the developmental process of enterprise apps to great extent. This feature by Node.js was widely acclaimed by developers worldwide and is still growing in terms of its operations and abilities.

While these were only five reasons, Node.js provides several reasons for companies to consider developing their apps on this platform. Node.js facilitates seamless development of apps without adding any kind of complexities. With its fast-paced V8 engines and single code base, Node.js consists of all the prerequisites necessary for developing and deploying comprehensive enterprise apps.

Author Bio:

James Grills is a marketing advisor – currently associated with Cumulations Technologies, a Flutter App Development company. He is a technical writer with a passion for writing on emerging technologies in the areas of mobile application development and IoT technology.

In this special guest feature, Jay Budzik, CTO at ZestFinance, discusses the set of developer tools that Google launched that allow data scientists to create explainable ML models, and also the reality of these new tools and how they are likely to impact the financial services market. ZestFinance is a software company that helps banks and lenders build, run, and monitor fully explainable machine learning underwriting models. As CTO, Jay oversees Zest’s product and engineering teams. His passion for inventing new technologies—particularly in data mining and AI—has played a central role throughout his career. Jay has a Ph.D. in computer science from Northwestern University.

Algorithmic models get a bad rap for being black boxes prone to unfair bias. They’re not—if you have the right tools to explain their decisions.

That’s why Google’s new suite of cloud-based explainable AI (XAI) tools is a step in the right direction for companies looking to adopt AI. These tools decipher, and defend, how specific data factors contribute to the output of machine learning (ML) models built atop complex neural networks.

While Google XAI should draw welcome attention and help bust a few ML myths, it likely won’t spur adoption for all companies—especially financial-services firms that need fully interpretable underwriting models to decide who can borrow and who can’t.

First, Google’s tools (now in beta) require customers to build and implement their models within the Google Cloud. But most companies need explainability tools that can be used in any cloud environment or on local servers.

Second, Google XAI isn’t flexible enough to accommodate more robust models. Like other explainability tools, Google’s are rooted in complex mathematical principles, including a version of the Aumann-Shapley method that uses a series of foils—called “counterfactuals”—to perfect an algorithm’s output. Andrew Moore, head of Google Cloud’s AI unit, described the process to the BBC in London last month: “The neural network asks itself, for example, ‘Suppose I hadn’t been able to look at the shirt color of the person walking into the store. Would that have changed my estimate of how quickly they were walking?’ By doing many counterfactuals, it gradually builds up a picture of what it is and isn’t paying attention to when it’s making a prediction.”

As compelling as that approach sounds, Google XAI won’t work with so-called ensembled models that weave together multiple models using diverse or competing techniques. “Ensembling” boosts the predictive power of AI credit scoring—assuming the collective system can be examined to assess how decisions are being made—and will become standard as lenders embrace ML over the next few years. For now, Google provides limited support for tree-based models and ensembles of trees and neural networks.

Third, while the Google AI “What-If” tool is pretty clever and allows modelers to test different scenarios at a glance, Google’s user interface could be tricky to use. Developers will have to learn a specific coding language and configuration convention to access explainability functions.

Last, Google’s explainability package is aimed primarily at data scientists—not credit analysts working for heavily regulated financial-services firms. In theory, data scientists at banks could build models using Google’s tools. But those same folks would need to build additional tools to test their models for accuracy and fairness and to generate all the compliance reporting required by regulators.

So while Google now offers a solid explainability tool for cloud-based neural networks, the vast majority of end-users will continue to demand more complete solutions that address their regulatory risk. Those solutions should include managed services (including expert help from a dedicated team), risk-management documentation (MRM) and model monitoring tailored for that industry, underwriting expertise and experience working with the largest lenders and, not least, a track record of deploying models that surpass regulatory scrutiny.

Google is taking important first steps to bring more transparency to the world of ML. That’s encouraging because the sooner our industry cracks open those scary black boxes, the better—for lenders, consumers, and the tech community.

Introduction

2019 was a pivotal year for the technology industry. From the maturation of container orchestration to accelerating AI adoption and a global reckoning with data privacy, the year laid significant groundwork for the decade ahead. This recap covers the major IT developments that defined 2019 and gave practitioners plenty to look forward to in 2020 and beyond.

Big Data Meets Kubernetes

One of the defining technical stories of 2019 was the convergence of big data workloads with Kubernetes-based infrastructure. What began as a container orchestration tool for web applications steadily proved its value for data engineering teams running distributed, microservices-oriented pipelines.

Kubernetes, originally designed at Google and released as open source, automates the deployment, scaling, and management of containerised applications. In 2019, data teams increasingly adopted it to manage complex, stateful workloads—tasks that were historically difficult to containerise. Frameworks such as Apache Spark and Apache Flink added or improved native Kubernetes support, making it more practical to run large-scale analytics jobs on container infrastructure rather than dedicated cluster managers like YARN.

At the industry level, major cloud providers and SaaS companies made significant acquisitions in the data analytics and visualisation space, signalling strong commercial confidence in big data’s growth trajectory. Google Cloud and Salesforce were among the prominent names that expanded their data capabilities through strategic purchases during the year.

Accelerating Momentum in AI and Machine Learning

Artificial intelligence and machine learning dominated technology headlines throughout 2019. Investment in AI research centres, university programmes, and enterprise AI platforms reached new highs globally, with notable activity in markets such as India.

Key application areas that gained traction during the year included:

• Facial recognition and public safety: Law enforcement agencies, including in India, began piloting AI-powered facial recognition systems for applications such as identifying suspects and locating missing persons. These deployments also prompted significant ethical debate about civil liberties and accuracy.
• Ride-sharing and delivery optimisation: Platforms such as Uber, Ola, and Swiggy deployed machine learning models to analyse customer behaviour, optimise routing, and personalise recommendations.
• Workplace automation: Organisations across industries used AI to automate repetitive administrative tasks, including initial resume screening, scheduling, and document processing.
• Autonomous vehicles: The automotive sector continued to invest heavily in ML-driven data pipelines for sensor fusion, object detection, and real-time decision-making in self-driving systems.

Industry analysts projected continued strong revenue growth for the AI market into the mid-2020s, though specific forecasts varied by source and methodology.

Enterprise IoT Investment Reaches a New Level

The Internet of Things moved from pilot projects to production deployments at scale in 2019, with several high-profile corporate announcements reflecting growing confidence in IoT infrastructure.

• Microsoft acquired Express Logic, the company behind the ThreadX real-time operating system (RTOS). The move was aimed at extending Microsoft’s Azure IoT reach to highly constrained embedded devices that traditional operating systems cannot support.
• SAP launched its Leonardo IoT platform, designed to bridge the gap between raw IoT sensor data and enterprise business process data. The platform targeted organisations undergoing digital transformation who needed to combine operational technology (OT) data with IT systems of record.
• Cisco announced plans to acquire Sentryo (later completed), a company specialising in industrial network visibility and OT/IoT security. Cisco also signalled a strategic shift toward edge-based networking architectures, processing data closer to the devices generating it rather than routing everything through centralised cloud infrastructure.

These investments illustrated a broader industry recognition that IoT success depends not just on connectivity but on security, real-time data processing, and integration with existing enterprise systems.

Data Privacy Legislation Gains Urgency

2019 was a difficult year for data stewardship. High-profile breaches and misuse scandals affected major social media platforms, healthcare organisations, and government agencies alike, keeping data privacy at the top of the regulatory agenda.

In India, the government advanced the Personal Data Protection Bill, which received cabinet approval and was referred to a parliamentary committee for review. Modelled in part on principles similar to Europe’s GDPR, the bill aimed to establish a legal framework governing how organisations collect, store, and process personal data belonging to Indian citizens. It proposed definitions of what constitutes a data breach and outlined the obligations and liabilities of data fiduciaries.

For DevOps and engineering teams, the implication was clear: data governance could no longer be treated as purely a compliance concern owned by legal departments. Practices such as data classification, access control, audit logging, and breach detection would increasingly need to be built into the software delivery lifecycle itself.

Looking Ahead: What 2019 Set in Motion

The technology shifts of 2019 were not isolated events. Each trend reinforced the others:

• Kubernetes matured into a platform capable of running not just microservices but data and ML workloads, accelerating the path to MLOps.
• AI moved from proof-of-concept to production, raising new questions about observability, model governance, and bias that DevOps practices would need to address.
• IoT deployments at scale created demand for edge computing architectures and security frameworks that treated devices as first-class infrastructure components.
• Data privacy regulations pushed engineering teams to treat compliance as a technical discipline rather than an afterthought.

Taken together, 2019 established the conditions for the broader platform engineering, MLOps, and data mesh conversations that would dominate the early 2020s.

What Is a Containerized Data Center?

A containerized data center is a self-contained, pre-engineered computing facility built into a standard shipping container or modular enclosure. It houses servers, networking equipment, power systems, and cooling infrastructure in a portable, rapidly deployable unit. This architecture is particularly attractive for organizations that need to establish compute capacity in remote locations, expand existing data center footprints quickly, or maintain resilient disaster-recovery sites without constructing permanent facilities.

Key Market Growth Drivers

According to a market study by Transparency Market Research (TMR), several structural forces are fueling adoption of containerized data centers across industries:

• Demand for uninterrupted communication: In competitive business environments, any delay or degradation in data availability can translate directly into client dissatisfaction and revenue loss. Containerized data centers provide a resilient, rapidly deployable compute layer that supports continuous operations.
• Multi-site operations: Organizations running distributed operations across multiple geographic locations require low-latency, reliable data infrastructure. Containerized units can be deployed at or near the point of need, reducing round-trip latency and dependency on centralized facilities.
• Business process streamlining: Enterprises are increasingly leveraging modular data infrastructure to support complex workflows, real-time analytics, and faster decision-making cycles. The portability and scalability of containerized solutions make them well-suited to these demands.
• R&D investment and technological advancement: Leading vendors are investing in research and development to improve power efficiency, thermal management, and integration with hybrid and edge cloud architectures. Collaboration with academic institutions and standards bodies is accelerating these improvements.

End-Use Sectors

Containerized data centers serve a broad range of end-use verticals. Common sectors driving adoption include:

• Defense and government: Military and government agencies value rapid deployment in austere or forward-operating environments where permanent infrastructure is impractical.
• Oil, gas, and mining: Remote extraction sites require on-site compute capacity for operational technology (OT) systems, SCADA, and telemetry data processing.
• Telecommunications: Carriers and network operators use containerized units to extend edge compute capacity, support 5G rollouts, and provision network functions at distributed points of presence.
• Healthcare: Hospitals and healthcare networks use modular data centers to extend IT capacity, support disaster recovery, and meet data sovereignty requirements.
• Retail and financial services: Businesses in these sectors deploy containerized units to support regional data processing, reduce latency for transactional systems, and maintain business continuity.

Regional Analysis: North America Leading Adoption

North America holds a significant share of the global containerized data center market. Several factors contribute to this regional dominance:

• The region is home to a concentration of major containerized data center vendors, creating a competitive ecosystem that accelerates product innovation and cost reduction.
• Strong enterprise demand for edge computing, disaster recovery, and hybrid cloud infrastructure is driving adoption across sectors.
• A mature and extensive telecommunications infrastructure provides the backbone connectivity that containerized edge deployments depend on.
• Favorable regulatory frameworks and robust capital markets support R&D investment by market players.

Other regions, including Asia-Pacific and Europe, are also experiencing growth as digital transformation initiatives expand and edge computing requirements increase in those markets.

Competitive Landscape

The containerized data center market features a fragmented competitive landscape with multiple large and mid-tier vendors competing on technology differentiation, total cost of ownership, deployment speed, and service support. Key competitive dynamics include:

• R&D as a primary differentiator: Vendors investing in next-generation cooling (such as liquid immersion cooling), higher-density compute configurations, and integration with hyperscale cloud platforms are gaining competitive advantage.
• Partnerships and ecosystem development: Collaborations between hardware vendors, system integrators, and cloud providers are creating integrated solution stacks that lower barriers to deployment.
• Modular and scalable architectures: Customers increasingly prefer solutions that can be scaled incrementally rather than requiring large upfront capital commitments.

What the TMR Market Study Covers

According to the referenced Transparency Market Research publication, the study provides data and analysis across the following dimensions:

• Market segments and sub-segments
• Market trends and dynamics
• Supply and demand analysis
• Market sizing and forecasts (2018–2026)
• Current opportunities, challenges, and emerging trends
• Competitive landscape and vendor positioning
• Technological breakthroughs and innovation roadmap
• Value chain and stakeholder analysis

Summary

The containerized data center market is being shaped by the convergence of edge computing demand, distributed enterprise operations, and the need for rapid, resilient infrastructure deployment. North America leads adoption, supported by strong vendor presence and digital infrastructure investment. As R&D continues to drive improvements in density, efficiency, and integration with cloud-native architectures, containerized data centers are positioned to play an increasingly strategic role in enterprise and carrier infrastructure planning through the mid-2020s.

What Separates High-Velocity Engineering Organizations?

Organizations like Netflix, Amazon, and Google deploy changes to production many times per day. Most medium and large companies aspire to that cadence but never reach it. The gap is rarely a tooling problem. The difference is almost always cultural—specifically, how teams relate to their delivery pipelines and what decisions they allow those pipelines to make autonomously.

This article explores the concept of the pipeline-driven organization: a model in which automated pipelines handle tactical, day-to-day software delivery decisions, freeing engineers to focus on strategic work and on improving the pipelines themselves.

Understanding Pipelines

A pipeline is a sequence of one or more automated tasks (often called jobs or steps) that run in a defined order to produce a result. Every pipeline produces two types of output:

• A judgment result: the pipeline either passed or failed. Inconclusive results should be treated as failures—ambiguity is the enemy of automation.
• Artifacts: deployed applications, compiled binaries, test reports, log files, or any other output that the team cares about.

Tasks: The Building Blocks

At the core of every pipeline is a task—a shell script or program that runs automatically on a machine (local or cloud-hosted). The task executes one or more operations (compiling code, running tests, scanning for vulnerabilities) and returns an exit code:

• Exit code 0: success.
• Any non-zero exit code: failure.

This binary signal is the foundation on which all pipeline automation is built. Everything—from a unit test suite to a security scan to a deployment script—ultimately communicates its result through this mechanism.

CI/CD Servers: The Orchestration Layer

Tools such as Jenkins, GitLab CI, GitHub Actions, and CircleCI act as an orchestration layer on top of individual tasks. A CI/CD server:

• Triggers tasks automatically based on code commits, pull requests, schedules, or other events.
• Listens to exit codes and captures log output.
• Presents results in a UI, highlighting failures and surfacing artifacts.
• Runs tasks in parallel across multiple agents or machines.
• Manages dependencies between tasks—ensuring step B only runs when step A passes.

CI/CD servers transform individual scripts into a coherent, observable, and manageable delivery system.

What Does “Pipeline-Driven” Actually Mean?

Being pipeline-driven means deliberately shifting as many tactical decisions as possible from humans to the pipeline itself, and then allowing the pipeline to act on those decisions autonomously.

To understand why this matters, it helps to distinguish between two categories of decisions in software development:

Strategic decisions require judgment, context, business acumen, and creative thinking—all things humans excel at. Tactical decisions require repeatability, speed, and freedom from bias or fatigue—all things machines excel at.

Why Pipelines Make Better Tactical Decisions

Speed

A pipeline can evaluate hundreds of test results and metrics in seconds. A human reviewing the same information takes minutes or hours—and that delay compounds across every release cycle. Additionally, humans hesitate. The psychological weight of approving a production deployment introduces latency that pipelines simply do not experience.

Reliability and Repeatability

Humans forget steps, especially during repetitive, low-stimulation tasks. A pipeline executes the same checklist in the same order every single time. It does not skip the security scan because the release is running late. It does not approve a deployment because it trusts the developer who wrote the code. It checks what it was taught to check, without exception.

Reduced Stress

Consider the pressure on a QA lead who must verbally approve a major production release in front of senior leadership. That person is the last human gate between potentially broken code and millions of users. The stress of that position is real and significant—and it does not improve decision quality. When a pipeline makes that call based on objective criteria, the human element of anxiety, social pressure, and fear of blame is removed from the equation.

Teaching a Pipeline to Drive

Trusting a pipeline to make autonomous decisions requires first teaching it how to judge correctly. This is done by embedding automated steps, scripts, and quality gates into the pipeline—each configured to pass or fail based on specific, measurable criteria.

Common quality gate patterns include:

• Test results: fail the pipeline if any automated test fails.
• Coverage thresholds: fail if code coverage drops below a defined percentage.
• Static analysis: fail if a linter or security scanner reports issues above a severity threshold.
• Performance metrics: fail if a performance test shows regression beyond an acceptable margin.
• Sub-pipeline results: fail if any parallel or child pipeline fails.
• Log analysis: fail if specific error patterns appear in output logs.

Every quality gate you add is one less human bottleneck on the path to production. The goal is to encode the collective knowledge and standards of your engineering organization directly into the pipeline.

Traditional vs. Pipeline-Driven Flows

Traditional (Human-Gated) Flow

In a traditional flow, the pipeline completes a stage and then notifies a human. That human reviews the output, makes a judgment call, and manually triggers the next stage. Each handoff between stages is a potential delay—waiting for the right person to be available, to feel confident enough to approve, or to prioritize the review over other work.

Knowledge is siloed. The Dev team owns the build pipeline. The QA team owns the test approval. The Ops team owns the deployment trigger. Each transition between silos introduces friction, miscommunication, and delay.

Pipeline-Driven Flow

In a pipeline-driven flow, each stage triggers the next automatically if the quality gates pass. The pipeline does not wait for a human to approve the move from testing to staging, or from staging to production. Decision logic that was previously held in people’s heads is now encoded as automated steps within the pipeline itself.

Each knowledge silo—Dev, QA, Ops, Security—contributes its expertise to the pipeline rather than acting as a manual gatekeeper. The pipeline becomes the shared artifact that encodes organizational standards and executes them without delay.

New Skills Required for a Pipeline-Driven World

Moving to a pipeline-driven model changes what skills are valuable across all engineering roles. The silos that typically separate Dev, QA, Ops, and Security must become more permeable.

• Development skills (for everyone): Pipelines are code. Contributing to a pipeline—adding steps, fixing failures, improving performance—requires basic scripting and programming ability. Engineers in all disciplines need enough coding literacy to read and modify pipeline definitions.
• Testing skills (for developers and ops): A pipeline is, at its core, a large automated test suite. Every step either passes or fails. Contributing meaningfully to a pipeline requires a testing mindset: the ability to envision how to verify something automatically rather than checking it manually.
• Operations skills (for developers and QA): Understanding how pipelines are configured, how to read their outputs, how to add or remove steps, and how to debug failures is no longer exclusive to Ops. Anyone who touches the pipeline needs this operational literacy.
• Security skills (for everyone): Security can no longer live exclusively in its own silo. When everyone has the ability to write code that runs in a pipeline, security awareness and secure coding practices must be distributed across all roles. Security specialists shift from gatekeepers to educators and pipeline contributors.

Enabling True Continuous Delivery

Continuous Delivery is the practice of keeping software in a state where it can be reliably released at any time. Many organizations adopt the vocabulary of CD without achieving its substance—because they retain human bottlenecks between pipeline stages.

True continuous delivery requires removing those bottlenecks. If a pipeline passes all quality gates, the deployment happens automatically. If it fails, nothing deploys. No meetings, no approvals, no heroics.

“If a pipeline fails—no deployment. If it passes—we deploy. No humans involved.”

This is the model that organizations operating at scale have embraced. Rather than relying on human judgment at every stage, they invest in teaching their pipelines to make those judgments correctly. Engineers become pipeline educators rather than pipeline gatekeepers.

Summary: The Business Case for Pipeline-Driven Culture

Becoming pipeline-driven is not simply a technical choice—it is a competitive one. Organizations that can deliver software continuously and reliably can respond faster to market changes, resolve production issues sooner, and iterate on product features more quickly than those constrained by manual approval chains.

The path there requires:

1. Identifying every human decision point in your current delivery process.
2. Determining which of those are tactical—repeatable, measurable, automatable.
3. Encoding the decision logic for those tactical steps into the pipeline as quality gates.
4. Building trust in the pipeline by starting with lower-risk environments and progressively extending automation toward production.
5. Upskilling all engineering roles to be able to contribute to, read, and improve the pipeline.

The engineers who will thrive in this environment are those who can code, test, think in systems, and share knowledge across traditional role boundaries. The organizations that will thrive are those that invest in pipeline quality the same way they invest in product quality—because in a pipeline-driven world, the two are inseparable.

Overview: The Global Data Protection Index

Dell EMC released the results of its third annual Global Data Protection Index, a large-scale survey examining how organizations manage and protect their data. The study surveyed 2,200 IT decision-makers from public and private organizations with 250 or more employees, spanning 18 countries and 11 industries. The findings provide a detailed view of data protection maturity, the impact of cloud adoption, and the growing risks posed by data loss and system disruption.

Key headline figures from the India-focused results include a 130% increase in data volumes managed by Indian organizations between 2016 and 2018, and a nearly 50 percentage point jump in data protection “adopters” over the same period.

Explosive Data Growth Is Outpacing Protection Strategies

The Index highlights a dramatic rise in the average volume of data managed by organizations:

Despite this growth, awareness of data’s business value is high: 93% of Indian respondents and 92% of global respondents acknowledged the potential value of their data. The challenge lies in translating that awareness into effective protection practices—most respondents admitted they struggle to adequately protect the data they hold.

Disruption Incidents Are Frequent and Expensive

The volume and business criticality of data make disruption incidents increasingly costly. The research found that more than three-quarters (76%) of respondents—both in India and globally—experienced at least one type of disruption in a 12-month period. More concerning, approximately 30% of Indian businesses (27% globally) reported they were unable to recover data using their existing data protection solutions.

Around 85% of Indian respondents use at least two data protection vendors, compared to 75% worldwide. Among organizations using multiple vendors, the most common disruption types experienced in India were:

1. Unplanned system downtime (51%)
2. Data loss (36%)
3. Local disaster affecting data access for an entire site or group (34%)

The Real Cost of Downtime and Data Loss

While unplanned downtime is more prevalent, data loss carries a significantly higher financial impact. According to the survey:

• Indian organizations that experienced downtime averaged 29 hours of downtime over 12 months, at a cost of approximately $958,583—compared to a global average of 20 hours costing around $526,845.
• Indian respondents lost an average of 3.31 terabytes of data, with an associated cost of approximately $1,287,788—versus a global average of 2.13 terabytes at $995,613.

These figures underscore a growing recognition—95% in India and 81% globally—that organizations need to take data protection more seriously, particularly for data categories with the highest monetary value.

Challenges Surrounding Data Protection

Despite the growing urgency, implementation remains difficult. The research found that 94% of Indian respondents (95% globally) faced at least one significant challenge related to data protection. The top three challenges cited in India were:

1. Lack of data protection solutions for emerging technologies
2. Inability to track and protect all data due to the growth of DevOps and cloud environments
3. Complexity of configuring and operating data protection software and hardware

For organizations struggling to find solutions for newer technologies, the gaps are significant:

• 71% in India could not find adequate data protection solutions for AI and machine learning workloads.
• 58% struggled to protect cloud-native applications.
• 46% lacked solutions for robotic process automation (RPA) data.

Despite these challenges, confidence in future readiness varies sharply by region: only 32% of Indian respondents believe their current data protection solutions will meet all future business needs, compared to just 16% globally—suggesting Indian organizations may be more optimistic, or have different expectations, about their existing tooling.

Cloud Is Reshaping Data Protection Infrastructure

Public cloud adoption has grown significantly among Indian enterprises. According to the Index, public cloud now accounts for 44% of the total IT environment in Indian organizations (up from 34% in 2016), compared to a global figure of 40% in 2018 (up from 28% in 2016).

Notably, 100% of Indian organizations reported using public cloud as part of their data protection infrastructure (98% globally). The top use cases for cloud-based data protection in India include:

1. Backup/snapshot services to protect workloads built on public cloud using modern application architectures (62%)
2. Cloud-enabled versions of on-premises data protection software to protect public cloud workloads (59%)
3. Backup/snapshot services to protect workloads developed using legacy application architectures (52%)

These findings point to a hybrid strategy: organizations are not simply migrating to cloud-native backup tools but are also extending traditional on-premises data protection approaches into cloud environments.

Regulatory Compliance: Confidence Varies Widely

Regulatory compliance is emerging as a key driver of data protection investment. The Index found that 72% of Indian businesses felt very confident that their current data protection infrastructure and processes are compliant with regional regulations—a stark contrast to just 35% of global respondents who expressed the same level of confidence.

This gap may reflect differences in the maturity or stringency of regional regulatory frameworks, or differences in how “compliance” is defined and assessed across markets.

Industry Perspective

“With the rapid change in industry dynamics and the advent of newer technologies, it has become imperative for organizations to focus on the practical application of emerging technologies like AI and IoT to accelerate their digital transformation journey. The key in organizations’ digital transformation journey is the generation and analysis of data. Businesses in India are seeing better potential in the value of data and are monetizing it more than their global counterparts.”

— Ripu Bajwa, Director & General Manager, Data Protection Solutions, Dell EMC India

Key Takeaways for DevOps and IT Teams

• Data volumes are growing faster than protection strategies can keep up. Teams should audit their current backup and recovery coverage against actual data growth rates.
• Multi-vendor complexity is a real risk. Organizations using two or more data protection vendors report higher rates of downtime and data loss—consolidation or tighter integration may reduce exposure.
• Cloud-native and AI/ML workloads remain under-protected. If your stack includes containerized apps, serverless functions, or ML pipelines, verify that your data protection tooling explicitly supports these architectures.
• Regulatory compliance cannot be assumed. The wide gap between Indian and global confidence levels suggests that self-assessed compliance may not reflect actual readiness for audits or incidents.
• Recovery testing matters as much as backup configuration. With 30% of organizations unable to recover data using existing tools, regular recovery drills are essential—not optional.

What Is Build Automation Software?

Build automation software automates the process of compiling source code, running tests, packaging binaries, and deploying applications. For DevOps teams, it is a foundational layer of any CI/CD pipeline, eliminating manual steps that introduce errors and slow down delivery cycles.

The market for these tools spans a wide range of products — from open-source build systems like Apache Maven and Gradle to hosted CI/CD platforms like CircleCI and Travis CI, and configuration management tools like Ansible and Chef that extend into build orchestration.

Key Players in the Build Automation Software Market

The competitive landscape includes tools that serve different parts of the build and delivery workflow. The following are widely recognized names in this space:

• Jenkins — The dominant open-source CI server with a large plugin ecosystem. Self-hosted and highly extensible.
• TeamCity — JetBrains’ commercial CI/CD server, popular in Java and .NET shops for its out-of-the-box feature set.
• CircleCI — A cloud-native CI/CD platform known for fast pipeline execution and container-first workflows.
• Bamboo — Atlassian’s CI/CD server, tightly integrated with Jira and Bitbucket.
• Ansible — Primarily a configuration management and orchestration tool, but widely used to automate build and deployment tasks.
• Microsoft Azure DevOps (formerly Team Foundation Server) — Microsoft’s end-to-end DevOps platform with integrated pipelines, repos, and work tracking.
• Apache Maven — A build lifecycle and dependency management tool predominantly used in Java ecosystems.
• Azure Automation — Microsoft’s cloud-based process automation service, used for runbook and infrastructure automation.
• Codeship — A hosted CI/CD platform focused on simplicity, acquired by CloudBees.
• Travis CI — One of the earliest hosted CI platforms, widely used in open-source projects on GitHub.
• Gradle — A flexible, high-performance build tool popular for Android and JVM projects, and increasingly adopted as a polyglot build system.
• Chef — An infrastructure-as-code platform that automates configuration and deployment, often used alongside CI pipelines.

These tools are not always direct competitors — many DevOps teams use them in combination. For example, Gradle handles the build, Jenkins orchestrates the pipeline, and Ansible manages deployment targets.

Market Segments: Deployment Models

Build automation software is available in two primary deployment models:

Cloud-Based

Hosted SaaS platforms such as CircleCI, Travis CI, and Codeship remove the operational burden of maintaining build infrastructure. Teams get immediate scalability, managed updates, and consumption-based pricing. This model is particularly attractive for startups, small teams, and open-source projects.

Web-Based / Self-Hosted

Self-hosted solutions like Jenkins and TeamCity give organizations full control over their build environments, data residency, and integration with on-premises infrastructure. They typically require more operational investment but offer greater customization and are preferred in regulated industries or air-gapped environments.

Industry Segmentation: Enterprise vs. SME

Demand for build automation exists across organization sizes, but the requirements differ significantly:

• Large Enterprises need scalability across hundreds of teams, role-based access control, audit logging, compliance integrations, and support SLAs. Platforms like Azure DevOps, TeamCity Enterprise, and CloudBees (Jenkins-based) target this segment.
• Small and Medium Enterprises (SMEs) prioritize low setup overhead, predictable pricing, and fast time-to-value. Hosted platforms with generous free tiers — such as GitHub Actions, CircleCI, and GitLab CI — are commonly adopted here.

Geographic Distribution of Demand

Adoption of build automation tools is global, with demand concentrated in regions with mature software development ecosystems:

• North America (United States, Canada) — Historically the largest market, driven by technology industry density and early DevOps adoption.
• Europe (Germany, UK, France, Italy) — Strong enterprise demand, particularly in financial services, automotive software, and manufacturing.
• Asia-Pacific (China, Japan, India, South Korea) — Fast-growing adoption driven by expanding software outsourcing industries and digital transformation initiatives.
• Middle East, Africa, and GCC — Emerging markets with growing investment in cloud infrastructure and government digitization programs.

What’s Driving Growth in This Market?

Several structural trends are increasing demand for build automation software:

• Adoption of DevOps and CI/CD practices — Organizations are investing in faster, more reliable software delivery pipelines, and build automation is a prerequisite.
• Shift to microservices and containers — More deployable artifacts mean more build jobs. Container-native build pipelines have become a standard requirement.
• Cloud migration — Moving workloads to cloud platforms naturally pushes teams toward cloud-hosted CI/CD services.
• Developer experience focus — Engineering productivity is increasingly treated as a business metric, and slow or unreliable builds are seen as a direct cost.
• Security and compliance requirements — Automated build pipelines enable consistent enforcement of security scanning, SBOM generation, and audit trails.

Choosing a Build Automation Tool: Practical Considerations

When evaluating build automation software, DevOps teams should weigh the following factors:

• Language and ecosystem support — Maven and Gradle are JVM-centric; MSBuild is .NET-focused; most CI platforms are language-agnostic at the pipeline level.
• Pipeline-as-code — Look for tools that define pipelines in version-controlled files (Jenkinsfile, .circleci/config.yml, .gitlab-ci.yml) rather than UI-only configuration.
• Integration with SCM and artifact repositories — Native integrations with GitHub, GitLab, Bitbucket, Nexus, or Artifactory reduce configuration overhead.
• Scalability — Consider whether the tool supports distributed agents, ephemeral build workers, or container-based execution to handle peak load.
• Total cost of ownership — Factor in licensing, infrastructure, and the engineering time required to maintain the platform, not just the headline price.

Summary

The build automation software market encompasses a broad set of tools — from low-level build systems like Maven and Gradle to full CI/CD orchestration platforms like Jenkins, CircleCI, and Azure DevOps. Deployment options range from fully managed SaaS to self-hosted installations, with demand spanning small development teams through large regulated enterprises. Continued growth in DevOps adoption, cloud migration, and software delivery velocity requirements makes build automation a central investment area for engineering organizations of all sizes.

For teams evaluating options, the right choice depends on language ecosystem, existing toolchain integrations, compliance requirements, and the balance between operational control and convenience.

DevSecOps Is a Culture, Not a Job Title or a Tool Purchase

The most important word in any DevSecOps conversation is culture. DevOps and DevSecOps are not roles you can hire for, and they are not products you can purchase and deploy. They represent fundamental shifts in how teams think, communicate, and share responsibility. An organization cannot buy its way into DevSecOps any more than it can buy a winning sports team by acquiring expensive equipment without changing how the players work together.

Organizations that want to sustain innovation and treat security as a first-class priority must commit to the cultural change that DevSecOps demands. That commitment starts at the leadership level and must permeate every team that touches the software delivery pipeline.

Why Security Has Historically Been Siloed

Security has traditionally been viewed as a barrier to velocity. Development teams push for speed; security teams push for controls. Because the two groups often have different goals, different vocabularies, and different incentives, they have historically operated in separate silos. Security reviews were bolted on at the end of a release cycle, creating friction, delays, and resentment on both sides.

This structural separation is precisely the problem DevSecOps aims to solve. When security is involved only at the end — or worse, only after an incident — vulnerabilities compound across the codebase and become far more expensive and disruptive to remediate.

Building the Right Team for DevSecOps Success

A successful DevSecOps initiative requires assembling a cross-functional team that spans development, IT operations, and security. No single group can carry the effort alone. The collaboration must be genuine and ongoing, not performative or limited to scheduled meetings.

Early warning signs that a DevSecOps initiative is struggling include:

• Persistent blame and finger-pointing between development, ops, and security teams.
• Siloed communication where teams share information only when forced to, rather than proactively.
• Lack of shared goals — teams optimizing for their own metrics rather than the organization’s overall security and delivery posture.
• Low enthusiasm for cross-functional work, with individuals resisting collaboration outside their traditional role boundaries.

Addressing these symptoms requires more than a reorganization chart. It requires deliberate investment in shared language, shared tooling, and shared accountability.

Automation and Orchestration as Cultural Enablers

While culture is the foundation, the right tooling accelerates and sustains the cultural shift. Automation is particularly powerful because it removes manual handoffs — one of the most common sources of friction between development and security teams. When vulnerability scanning, policy enforcement, and compliance checks are automated within the CI/CD pipeline, security becomes a continuous background process rather than a disruptive checkpoint.

Orchestration layers that normalize outputs from multiple security scanning tools address a related challenge: different tools classify vulnerabilities differently, produce inconsistent reports, and often require dedicated personnel to interpret results. A unified orchestration approach provides a consolidated, real-time view of application and infrastructure risk, allowing teams to prioritize remediation without context-switching between disparate consoles.

Streamlining Vulnerability Management Without Slowing Delivery

One of the persistent challenges in DevSecOps is that security tooling can inadvertently become a bottleneck. If scanning processes slow down builds, or if security findings are reported without clear prioritization guidance, developers begin to route around the process rather than engage with it.

Effective DevSecOps programs address this by:

• Integrating scanning into existing pipelines so security checks run automatically on every commit, not just at release gates.
• Prioritizing findings by risk rather than flooding developers with undifferentiated vulnerability lists that make triage impossible.
• Providing remediation guidance at the point of finding so developers can act without escalating to the security team for every issue.
• Maintaining continuous visibility across both application code and infrastructure, not just the fraction of the environment covered by point-in-time assessments.

Practical Steps to Get Started

DevSecOps adoption is a journey, not a one-time migration. Organizations that try to transform everything simultaneously typically struggle. A more durable approach:

1. Assess your current state. Understand where security currently sits in your SDLC, where the friction points are, and what tools different teams are already using.
2. Build shared goals and metrics. Align development, operations, and security around common KPIs — mean time to remediate, vulnerability escape rate, deployment frequency — rather than team-specific metrics that incentivize conflict.
3. Start automating in the pipeline. Introduce automated static analysis (SAST) or dependency scanning in CI, and expand incrementally to dynamic analysis (DAST), container scanning, and infrastructure-as-code checks.
4. Foster cross-functional communication. Embed security champions within development teams. Run joint post-mortems on security incidents. Make security knowledge accessible to developers, not proprietary to the security team.
5. Iterate and mature. DevSecOps programs improve over time. Treat security tooling and processes with the same continuous improvement mindset you apply to application code.

The Bottom Line

DevSecOps is moving from a forward-looking aspiration to an operational expectation. The organizations that treat it as a cultural transformation — rather than a tool purchase or an org chart adjustment — are the ones that will build security into their delivery pipeline in a way that actually scales. Formulate a practical plan, execute it incrementally, and hold the entire organization accountable to the shared goal of shipping secure software at speed.

Overview of the Global Continuous Delivery Market

The global Continuous Delivery (CD) market has attracted significant analyst attention as organizations across every major industry vertical accelerate their software release cycles. Market research covering the 2019–2024 period highlights growing adoption of CD pipelines driven by the need for faster time-to-market, reduced deployment risk, and tighter integration between development and operations teams.

Research reports in this space typically examine market dynamics, competitive positioning, regional growth patterns, and segment-level revenue distribution. Key metrics tracked include compound annual growth rate (CAGR), market share by vendor, revenue and volume trends, gross margins, and pricing trajectories. The sections below summarize the major dimensions of this market as reported in the 2019 research landscape.

Major Market Players

The Continuous Delivery vendor ecosystem is broad, spanning large enterprise software companies, specialist DevOps tooling vendors, and cloud platform providers. Companies identified as key participants in the 2019–2024 market include:

• Atlassian – Bitbucket Pipelines and integrations with Jira for end-to-end delivery tracking
• IBM – UrbanCode Deploy and broader IBM DevOps portfolio
• XebiaLabs (now Digital.ai) – Release orchestration and pipeline automation
• CA Technologies (acquired by Broadcom) – Release Automation tooling
• Electric Cloud (now CloudBees CD) – Enterprise-grade pipeline orchestration
• Puppet Enterprise – Infrastructure automation feeding CD workflows
• Chef Software – Configuration management and compliance automation
• CloudBees – Jenkins-based and cloud-native CD platforms
• Microsoft – Azure DevOps (formerly VSTS), GitHub Actions
• Flexagon – FlexDeploy for Oracle and hybrid environments
• Micro Focus – ALM and release management tooling
• Clarive – DevOps and release management platform
• Accenture, Wipro – Systems integrators delivering CD transformation services
• VMware – Tanzu portfolio and pipeline tooling
• Red Hat – OpenShift Pipelines and Tekton-based CD
• CircleCI, Shippable – Cloud-native CI/CD services
• Heroku – PaaS with integrated deployment pipelines
• JetBrains – TeamCity CI/CD server
• Bitrise – Mobile-focused CI/CD
• AppVeyor – Windows and .NET CI/CD service
• Infostretch, Kainos, Applariat, Spirent – Niche and specialist delivery tooling

Market Segmentation

By Deployment Mode

• On-premises – Preferred by regulated industries and organizations with strict data sovereignty requirements
• Cloud – Increasingly dominant, offering lower operational overhead and faster onboarding for CD toolchains

By Organization Size

• Small and Medium-sized Enterprises (SMEs) – Typically favor SaaS-based CD tools with low upfront cost and managed infrastructure
• Large Enterprises – Require enterprise-grade orchestration, compliance controls, audit trails, and integration with legacy systems

By Industry Vertical

Continuous Delivery adoption cuts across nearly every sector, though maturity and use-case drivers vary considerably:

• Banking, Financial Services, and Insurance (BFSI) – High compliance demands drive investment in auditable, automated pipelines
• Telecommunications – Rapid feature deployment for customer-facing applications and network functions
• Media and Entertainment – Continuous updates to streaming platforms and content delivery systems
• Retail and eCommerce – Frequent releases tied to seasonal campaigns and personalization features
• Healthcare – Growing adoption balanced against strict regulatory validation requirements
• Manufacturing – Integration of CD into IoT and operational technology software pipelines
• Education – Accelerating delivery of digital learning platforms
• Government, Transport, Logistics, Energy and Utilities – Emerging adoption, often constrained by legacy infrastructure

Geographic Segments

Market research in this period covered four primary geographic regions:

• North America (United States, Canada) – The largest market by revenue, driven by mature DevOps practices and a high concentration of technology firms
• Europe (Germany, France, Italy, and rest of Europe) – Strong growth in BFSI and manufacturing verticals; GDPR compliance is a key CD pipeline consideration
• Asia-Pacific (China, Japan, India, South Korea) – Fastest-growing region, fueled by digital transformation investment and expanding technology talent pools
• LAMEA (Brazil, Turkey, Saudi Arabia, South Africa) – Emerging market with increasing cloud adoption and government-led digitization programs

Key Market Trends Driving CD Adoption (2019–2024)

Several structural trends were identified as the primary forces shaping the Continuous Delivery market across this period:

• Microservices and containerization – Kubernetes and Docker adoption created demand for pipelines capable of managing dozens or hundreds of independently deployable services
• Shift-left security (DevSecOps) – Security scanning, secrets management, and policy enforcement integrated directly into CD pipelines
• GitOps – Git repositories becoming the single source of truth for both application code and infrastructure configuration, with CD tooling watching for changes and triggering deployments automatically
• AI/ML-assisted pipelines – Early exploration of anomaly detection in deployment metrics and intelligent rollback triggers
• Multi-cloud and hybrid deployment targets – CD platforms expanding to orchestrate deployments across on-premises, public cloud, and edge environments simultaneously
• Platform engineering – Internal developer platforms (IDPs) abstracting CD complexity and providing self-service deployment workflows to product teams

What This Market Data Means for DevOps Teams

For engineering and DevOps leaders evaluating CD tooling or building a business case for investment, the market segmentation above is useful context in several ways:

• Vendor longevity risk – The large number of specialist vendors means consolidation is likely. Several vendors listed above have already been acquired or merged (e.g., Electric Cloud into CloudBees, XebiaLabs into Digital.ai, Shippable into JFrog). Evaluating vendor roadmap and financial stability is important.
• Vertical-specific requirements – Healthcare and BFSI organizations should look for CD platforms with built-in compliance reporting, change approval workflows, and full audit logs.
• Cloud vs. on-premises decision – For most SMEs, cloud-hosted CD (GitHub Actions, CircleCI, GitLab CI) offers the fastest path to value. Large enterprises with regulated workloads often maintain on-premises runners even when using cloud-hosted orchestration layers.
• Regional procurement – Data residency regulations in Europe and Asia-Pacific increasingly influence where CD pipeline infrastructure can run, affecting SaaS tool selection.

Summary

The 2019–2024 Continuous Delivery market report landscape reflected a rapidly maturing ecosystem. The core story is one of expanding adoption across all organization sizes and geographies, driven by microservices architectures, cloud-native platforms, and organizational pressure to ship software faster without sacrificing quality or compliance. The vendor landscape has since consolidated somewhat, but the structural trends identified in this period—GitOps, DevSecOps, platform engineering, and multi-cloud orchestration—remain the dominant forces shaping CD tool investment decisions today.

This article summarizes the structure and key segments of Continuous Delivery market research published around 2019. Specific revenue figures, CAGR percentages, and market size numbers from the original report have not been reproduced here, as they could not be independently verified.