Technology advances rapidly, and almost all industry sectors tend to embrace changes to survive amid this troubled time. Emerging technologies like AI, big data, and ML can prepare enterprises for the future while ensuring their growth. However, entrepreneurs must combine technologies to achieve their long-term objectives while effectively addressing intensifying competition.
“Big Data” has become a buzzword in the corporate world. Big Data projects lead the day by offering actionable insights from available data. However, there always are ways to increase their efficiency further. One of them is combining Big Data with DevOps technology. This article will dig deep into the Big Data and DevOps combination. But, before moving ahead, let’s briefly understand both these terms.
Big Data- Brief Introduction
Big data refers to large and complex data sets which are collected from a variety of sources. Their volume and complexity are massive; therefore, traditional data processing software cannot manage them. These data sets are handy for entrepreneurs to resolve various business tasks and make informed decisions in real-time. Standard data cannot serve this purpose effectively.
Extensive data management involves various processes, including obtaining, storing, sharing, analyzing, digesting, visualizing, transforming, and testing corporate data to provide the desired business value. It also contributes to streamlining processes by bringing automation.
Furthermore, as enterprises experience huge pressure for faster delivery in this competitive market, Big Data can assist them with actionable insights. But, when it comes to providing all this with maximum efficiency, DevOps brings the right tools and practices.
Exciting Stats for Big Data
- Experts indicate that over 463 exabytes of data will be created every day by 2025, which is equivalent to around 212,765,957 DVDs
- Poor quality of data can cost the US economy as much as USD 3.1 trillion annually.
- The Big Data market is expected to reach a value of around USD 103 billion by 2027
- Over 97 percent of organizations say they are investing in Big Data and AI
- Around 95 percent of companies say their inability to understand and manage unstructured data holds them back
After knowing the importance of Big Data, let us understand the DevOps concept.
Introduction of DevOps
If we define DevOps, it is a methodology, culture, and set of practices that aim to facilitate and improve communication and collaboration between both development and operations teams. It is mainly focused on automating and streamlining various processes within the development lifecycle of the respective project.
Essential pillars of DevOps are shorter development cycles, increased deployment frequency, rapid releases, parallel work of different experts, and regular customer feedback are significant pillars of DevOps. Today, this concept has gained ground because of its benefits for enterprises.
It significantly increases the speed, quality, and reliability of the software. Most software projects can take advantage of the DevOps concept in agile methodology.
Key Reasons Why DevOps Gaining Widespread Acceptance
Lack of communication between developers and the operations team can slow development. DevOps is developed to overcome this drawback by providing better collaboration among members of both teams, which results in faster delivery. It also offers uninterrupted software delivery by minimizing and resolving complex problems quicker and more effectively.
Most organizations have adopted DevOps to enhance user satisfaction and deliver a high-quality product within a short time while improving overall efficiency and productivity. DevOps structures and strengthens the software delivery lifecycle. It started gaining popularity in the year 2016 as more and more organizations began moving to DevOps usage.
Corporate clients who adopted advanced technologies like Cloud, Big Data, etc., are demanding companies to deliver high software-driven capabilities. A recent survey proved that 86% of organizations believe that continuous software delivery is crucial to their business. Here, DevOps can lend a helping hand to ensure the timely delivery of high-quality software.
Key DevOps Statistics
- The market share of DevOps is expected to increase by over USD 6 billion by 2022
- 58 percent of organizations have witnessed better performance and improved ROI after adopting DevOps
- 68 percent of companies have seen improved customer experience after deploying DevOps
- 47 percent of companies have reduced the TTM (Time to Market) of software and service deployment
DevOps offers benefits like higher reliability, more security, and enhanced scalability besides a speedy development cycle and the capability of delivering faster updates. It also improves ownership and accountability across various teams. DevOps practices have two inherent aspects- CI (Continuous Integration) and CD (Continuous Delivery). They are related to each other and contribute to increasing productivity.
- Continuous Integration (CI) is the practice of merging the code changes from multiple developers into the central repository several times a day.
- Continuous Delivery (CD) is the practice of software code being created, tested, and continuously deployed to the production environment.
Why Big Data needs DevOps
At times, Big Data projects can be challenging in terms of:
- handling the massive amount of data
- delivering the task faster to keep up with the growing competition or due to the pressure from the stakeholders
- responding quickly to changes
The traditional approach to meet these challenges, unlike DevOps, is insufficient. Traditionally, different teams and members work in isolation. This practice creates silos and brings a lack of collaboration. For example, data architects, analysts, admins, and many other experts work on their part of the job, which ultimately slows down the delivery.
DevOps, on the other hand, according to the pillars mentioned above, brings all participants of all stages of the software delivery pipeline together. It removes barriers and reduces silos between different roles to make your Big Data team cross-functional with ease. In addition, you can experience a considerable increase in operational efficiency, resulting in a better-shared goal vision.
Simply put, DevOps tools for Big Data result in the higher efficiency and productivity of Big Data processing. DevOps for Big Data uses almost the same tools as the traditional DevOps environments, like bug tracking, source code management, deployment tools, and continuous integration.
Though the Big Data and DevOps combination offers many benefits to enterprises, it has its challenges, and software companies must address them while combining Big Data and DevOps.
Challenges of Big Data and DevOps Combination
Suppose you have finally decided to integrate DevOps with your Big Data project. In that case, you must understand the different types of challenges that you might experience during the process.
- The operations team of an organization must be aware of the techniques used to implement analytics models, along with in-depth knowledge of big data platforms. And the analytics experts must learn some advanced stuff, as they work closely with different social engineers.
- Additional resources and cloud computing technology will be required if you want to operate Big Data DevOps at maximum efficiency, as these services help IT departments concentrate more on enhancing business values instead of focusing on fixing issues related to hardware, operating systems, and some other operations.
- Though DevOps build strong communication between developers and operation professionals, dealing with some communication challenges is difficult. Also, testing the function of analytic models should be meticulous and faster in production-grade environments.
Benefits of Big Data and DevOps Combination
DevOps is not associated with data analytics, so employing data specialists can be an added advantage for organizations who want to adopt DevOps with Big Data. It can help them to make the Big Data operations more powerful and efficient in combination with Dev Ops. Integration of Big Data and DevOps results in the following benefits for organizations.
- Effective Software Updates
In general, the software combines with data for sure. So, if you want to update your software, you must know your application’s types of data sources. This can be understood by interacting with your data experts while integrating DevOps and Big Data.
Mainly, errors increase when organizations face problems handling data while writing and testing the software. Finding and avoiding those errors remains the top priority in the software delivery pipeline to save time and effort. Data-related errors can be fixed in an application with strong collaboration between DevOps and Big Data experts.
Non-data experts cannot understand the software that runs with Big Data because of the tremendous verification in the types and range of data. Here, data experts can help DevOps professionals to gain knowledge about the kinds of data and challenges they need to deal with to ensure optimum outcomes. It is fair to mention that the DevOps team working in collaboration with the Big Data team results in applications whose performance in the real world is the same as that in the development environment.
Time-consuming processes, like data migration or translation, might slow your project down. But combining DevOps and Big Data helps streamline operations and improve data quality. As a result, executives can focus on other productive and creative tasks.
Like continuous integration (CI), you can benefit from constant analytics by combining DevOps and Big Data. So it is because the combination can streamline the data analysis processes and automate them using algorithms.
When the Big Data software is deployed to production, it’s time to gather real-time and accurate feedback to find its strengths and weaknesses. Again, the close collaboration of DevOps executives and data scientists, thanks to the combination of DevOps and Big Data, can remain handy in this process.
Critical Applications of DevOps in Big Data
Effective Planning for Software Updates
A developer has to get an insight into the data types that will be useful in developing an enterprise-grade application or software. It is also necessary to understand where the data will be used in the application and to what extent.
You want to give this information to your dev early and ensure that your developer works with a data expert.
Your data experts will know the correct code and keep your dev on the right path to designing or updating your company software. You want to maintain the integrity of your system and have everything run smoothly for your updates.
Low Chances of Error
When software is developed, developers tend to test it rigorously, due to which the problem related to data causes constant errors. Moreover, this error rate keeps increasing as the complexity of the software increases in line with the rise in the data. Here, the collaboration of DevOps and Big Data comes into the game.
The Data scientists and developers identify those errors in the early stages, saving both teams time and effort. Moreover, it makes it easier to find other errors in the application.
The DevOps philosophy states that a development-friendly environment should resemble a real-world setting, but it is impossible whenever big data comes into play.
A development-friendly environment is difficult to create when a developer has to involve big data in developing software that consists of many complex data sets and many types of data.
You’ll want your company developers to be well aware of all challenges that will be facing your devs, and your data expert can provide the answers. You can retain a data expert or hire a contract data expert to help your devs produce enterprise-level software.
Though the DevOps concept has grown and is mature enough to deliver software and services faster, it is still not considered a critical approach by many worldwide enterprises. Large-scale enterprises are still following the old approaches because of the false or improper belief that the transition to DevOps might fail.
But the move to DevOps can help businesses deliver high-quality products quickly, and companies can provide better results in the long run after combining Big Data with DevOps.
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