5 Ways To Master DevOps Automation In Big Data


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DevOps and automation has become synonymous with more development tools and processes available to streamline projects. From container registries to Docker images, tasks are no longer position specific, that’s for sure.

In fact, the days of developers simply writing code and testing are over. Now developers and engineers are synergized with DevOps professionals writing code, testing, deploying, implementing, and managing.

Collaboration and automation. These are now the two main keywords people think of when they here DevOps today. This is even true in the space of Big Data, despite the large teams and information involved.

Let’s take a closer look at a few ways to master DevOps automation in big data.

  1. Automate steps in your software delivery utilizing a CI/CD pipeline

Workload automation tools are essential. To do this in the most efficient way, you can utilize job scheduling tools. Continuous integration and continuous delivery should also be a part of your strategy.

A CI/CD pipeline can initiate code builds, run automated tests on code, and deploy code to staging and production environments. Continuous integration and continuous delivery basically take manual errors out of the equation, standardizing development loops for fast iterations.

For instance, continuous integration triggers an automated build and test for every code change. This provides feedback to developers who made the changes with the entire loop taking ten minutes or less.

  1. Big data platforms need to support Big companies

The problem with big data companies is that they are very large in size. Personnel can be huge with many team members spread all over the world. This is simply due to the large amount of information big data companies handle.

Due to the size of information and personnel, DevOps needs to be pretty personalized to a company’s needs. This is where automation for big data is useful. It gives big data companies the ability to utilize proper data management environments and automated distributed work platforms for greater efficiency.

This synergizes developers and operators, regardless of the company’s size. When synergized into one DevOps unit, projects are easier to collaborate on. This can speed up the operations side and bring software development projects to market faster, which is good for business.

  1. New product delivery simplified

A lot goes into the software development lifecycle (SDLS). The aim of any project is to produce the best software in the fastest time frame possible. Common steps in the process often include:

  • Identify issues and plan the software development project
  • Design and build the software
  • Test and deploy, bringing the software to market

These steps are important, and when DevOps has automated processes in place, these steps become simplified and streamlined. One of the main reasons is that all software development project teams can collaborate effectively.

  1. New data adaptation is very important in today’s big data climate

Managing data is certainly a priority in big data companies. However, a strong focus is on ne data as well. Big data companies want to get new data and use it to shift operations quickly, and in real time.

Automation in DevOps can facilitate this management of new data perfectly. Automated systems can learn and adapt when new data comes in, as well as use artificial intelligence (AI) to find patterns in data to change commands.

  1. Get rid of bottlenecks by finding them faster

Collaboration via DevOps automation allows developers and operations to find bottlenecks in the software development process easier and faster. This is very important, since bottlenecks can slow a project to a crawl, causing money to be lost.

Having automated DevOps systems in place for big data showcases continuous improvement and monitoring. ROI takes a front seat, giving decisions in the development process a results oriented framework.

In Conclusion . . .

Big data and DevOps automation go hand-in-hand. Managing all that information and getting teams to collaborate can be very difficult for big data companies, but with a bit of automation, processes can be made easier.

The above ways to master DevOps automation in big data are only the tip of the iceberg. There are plenty of other ways to make automation part of your daily processes when it comes to software development. The above, however, are among the most important to consider.

When big data companies get faster while still capitalizing on the information they receive daily, everyone wins. Big data often equates to big time results, and the technology and innovation can trickle down to the masses pretty quickly. Is automation a must for your business? Take the next step and synergize your DevOps today.