top

DevOps for Big Data - Integration benefits and challenges

DevOps? “DevOps is not a goal, but a never-ending process of continual improvement” - Jez Humble DevOps is the advanced standard of software development and delivery that improves the communication and collaboration between development and operation teams. Collaboration and communication are crucial for DevOps and QA (Quality Assurance) is essential for an effective communication of Dev team and Ops team. DevOps methodologies gaining widespread acceptance Lack of communication between developers and operations team has slowed down the process of development. DevOps was developed to overcome this drawback by providing better collaboration which results in faster delivery. It offers uninterrupted software delivery by minimizing and resolving the complex problems faster. Most of the organizations have adopted DevOps methodologies to enhance user satisfaction, deliver the high-quality product within short time and improve efficiency and productivity. DevOps structures and strengthens the software delivery pipeline. It has become more popular in 2016 as more and more organizations moved to the DevOps usage. Clients who adopted technologies like Cloud, Big Data etc. are demanding companies to deliver the software-driven capabilities that they have ever done before. A recent survey proved that 86% of organizations believe that continuous software delivery is crucial to their business. Need of DevOps for Big Data The process of gaining an accurate and deep understanding of Big Data projects is really challenging. And with lack of communication between Big Data developers and IT operations, it becomes even more tough, which is common for more companies. Because of this, IT developers are facing many difficulties to deliver quality results. This has stimulated analytics scientists to update their algorithms which require infrastructure and resources excessively than originally expected. And on the other hand, with lack of communication, the operations team is kept out of the process until the last minute. This declines the potential competitive advantage of big data analytics which is why DevOps is needed for Big Data to stop this from happening. DevOps tools for Big Data result in the higher efficiency and productivity of Big Data processing. DevOps for Big Data makes use of almost the same tools like the traditional DevOps environments such as bug tracking, source code management, deployment tools and continuous integration. Challenges involved in the integration of Big Data and DevOps If you have finally chosen DevOps to integrate with your Big Data project, then it is crucial to understand the different types of challenges that you might experience in transit. The operations team of an organization must be aware of the techniques that are used to implement analytics models and acquire in-depth knowledge of big data platforms. And the analytics experts must learn some advanced things as well, as they work close to social engineers. Additional human resources and cloud computing will be required if you want to operate Big Data DevOps at maximum efficiency, as these services help IT departments to concentrate more on enhancing business values instead of focusing on fixing provisioning hardware, operating systems, and some other works. Benefits of integrating Big Data and DevOps are leading to more integration challenges. Though DevOps build strong communication between developers and operation professionals, it is not in the data scientist's language. And the testing of the function of analytic models should be meticulous and faster in the production-grade environments because of the high-performance requisites on advanced analytics. Benefits of integrating Big Data and DevOps Employing data specialists can be an added advantage for organizations who are working to adopt DevOps that helps to make the Big Data operations more powerful and efficient, as DevOps is not associated with data analytics. Integration of Big Data and DevOps results in the following benefits for organizations. Updates software in an effective way In general, the software combines with data in any manner. So, if you want to update your software, it is necessary to have knowledge of the types of data sources your application is collaborating. This can be known by interacting with your data experts which is nothing but the integration of DevOps and Big Data. Error rates can be minimized Mostly, data handling problems result in a high chance of errors when the software is being coded and tested. Finding and avoiding those errors in the first place in the software delivery pipeline saves time and effort. Data-related errors can be fixed in an application with strong collaboration between the DevOps and data experts team. Builds strong relationship with production and development environments A software that runs with Big Data can be difficult for non-data experts to understand, as the types and range of data in the physical world are varying tremendously. Data experts help the other teams to gain knowledge about the types of data challenges that their software will experience in production. DevOps team working in collaboration with Big Data team results in applications whose performance in the real world is the same as that of in development and testing environments. Conclusion Though DevOps has grown up and is matured enough to deliver the software and services faster, it is still not considered as a key approach by most of the enterprises worldwide. Large-scale enterprises are still following the old approaches because of the main reason that they believe the transition to DevOps might fail. Most of the industry leaders are responsible for this, as they explained that transit to DevOps is useful and helpful, that will deliver better results in the long run. But actually, the move to DevOps can help the businesses deliver high-quality products within a short time.  
Rated 4.0/5 based on 20 customer reviews
Normal Mode Dark Mode

DevOps for Big Data - Integration benefits and challenges

Ruslan Bragin
Blog
13th Nov, 2017
  DevOps for Big Data - Integration benefits and challenges

DevOps?

“DevOps is not a goal, but a never-ending process of continual improvement” - Jez Humble

DevOps is the advanced standard of software development and delivery that improves the communication and collaboration between development and operation teams. Collaboration and communication are crucial for DevOps and QA (Quality Assurance) is essential for an effective communication of Dev team and Ops team.

DevOps

DevOps methodologies gaining widespread acceptance

Lack of communication between developers and operations team has slowed down the process of development. DevOps was developed to overcome this drawback by providing better collaboration which results in faster delivery. It offers uninterrupted software delivery by minimizing and resolving the complex problems faster. Most of the organizations have adopted DevOps methodologies to enhance user satisfaction, deliver the high-quality product within short time and improve efficiency and productivity. DevOps structures and strengthens the software delivery pipeline. It has become more popular in 2016 as more and more organizations moved to the DevOps usage.

Clients who adopted technologies like Cloud, Big Data etc. are demanding companies to deliver the software-driven capabilities that they have ever done before. A recent survey proved that 86% of organizations believe that continuous software delivery is crucial to their business.

Need of DevOps for Big Data

The process of gaining an accurate and deep understanding of Big Data projects is really challenging. And with lack of communication between Big Data developers and IT operations, it becomes even more tough, which is common for more companies. Because of this, IT developers are facing many difficulties to deliver quality results. This has stimulated analytics scientists to update their algorithms which require infrastructure and resources excessively than originally expected. And on the other hand, with lack of communication, the operations team is kept out of the process until the last minute. This declines the potential competitive advantage of big data analytics which is why DevOps is needed for Big Data to stop this from happening.

DevOps tools for Big Data result in the higher efficiency and productivity of Big Data processing. DevOps for Big Data makes use of almost the same tools like the traditional DevOps environments such as bug tracking, source code management, deployment tools and continuous integration.

Challenges involved in the integration of Big Data and DevOps

If you have finally chosen DevOps to integrate with your Big Data project, then it is crucial to understand the different types of challenges that you might experience in transit.

  • The operations team of an organization must be aware of the techniques that are used to implement analytics models and acquire in-depth knowledge of big data platforms. And the analytics experts must learn some advanced things as well, as they work close to social engineers.

  • Additional human resources and cloud computing will be required if you want to operate Big Data DevOps at maximum efficiency, as these services help IT departments to concentrate more on enhancing business values instead of focusing on fixing provisioning hardware, operating systems, and some other works.

  • Benefits of integrating Big Data and DevOps are leading to more integration challenges. Though DevOps build strong communication between developers and operation professionals, it is not in the data scientist's language. And the testing of the function of analytic models should be meticulous and faster in the production-grade environments because of the high-performance requisites on advanced analytics.

Benefits of integrating Big Data and DevOps

Employing data specialists can be an added advantage for organizations who are working to adopt DevOps that helps to make the Big Data operations more powerful and efficient, as DevOps is not associated with data analytics. Integration of Big Data and DevOps results in the following benefits for organizations.

  • Updates software in an effective way

In general, the software combines with data in any manner. So, if you want to update your software, it is necessary to have knowledge of the types of data sources your application is collaborating. This can be known by interacting with your data experts which is nothing but the integration of DevOps and Big Data.

  • Error rates can be minimized

Mostly, data handling problems result in a high chance of errors when the software is being coded and tested. Finding and avoiding those errors in the first place in the software delivery pipeline saves time and effort. Data-related errors can be fixed in an application with strong collaboration between the DevOps and data experts team.

  • Builds strong relationship with production and development environments

A software that runs with Big Data can be difficult for non-data experts to understand, as the types and range of data in the physical world are varying tremendously. Data experts help the other teams to gain knowledge about the types of data challenges that their software will experience in production. DevOps team working in collaboration with Big Data team results in applications whose performance in the real world is the same as that of in development and testing environments.

Conclusion

Though DevOps has grown up and is matured enough to deliver the software and services faster, it is still not considered as a key approach by most of the enterprises worldwide. Large-scale enterprises are still following the old approaches because of the main reason that they believe the transition to DevOps might fail.

Most of the industry leaders are responsible for this, as they explained that transit to DevOps is useful and helpful, that will deliver better results in the long run. But actually, the move to DevOps can help the businesses deliver high-quality products within a short time.

 
Ruslan

Ruslan Bragin

Author
Ruslan is a passionate in developing data and Machine learning solution. He is currently working on projects related to IoT.

Leave a Reply

Your email address will not be published. Required fields are marked *

SUBSCRIBE OUR BLOG

Follow Us On

Share on

other Blogs

20% Discount