Big Data and DevOps — Winning Combination for Global Enterprises

Big Data- Brief Introduction

  • 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

Introduction of DevOps

Key Reasons Why DevOps Gaining Widespread Acceptance

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
  • 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

  • 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

Challenges of Big Data and DevOps Combination

  • 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

  • Effective Software Updates
  • Minimal Error Rates
  • Builds Relationships
  • Streamlined Processes
  • Continuous Analytics
  • Accurate Feedback

Critical Applications of DevOps in Big Data

Effective Planning for Software Updates

Low Chances of Error

Consistent Environment

Concluding Lines



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