5 Ways Big Data Teams Can Leverage DevOps Automation
BY: EMILY SNELL ON MONDAY, APRIL 29, 2019
There was a time when the development of software was a very position-specific task.
The field used to see individuals sticking to their own jobs and rarely showcasing their skills outside of their own position. Software developers would write and test code, while operational workers deployed it and oversaw the implementation.
But the DevOps movement has changed all that, allowing collaborative efforts from both groups at every step of the workflow. It helps software developers and IT operators coordinate their efforts – and if there’s one characteristic that is used quite often in DevOps processes, its automation.
Big data teams may have a challenge in coordinating their efforts between massive staffs with massive amounts of information to consider. But there are also advantages to this position.
Leveraging DevOps Automation and Seeing the Benefits
The growing movement that is DevOps has quickly become inseparable with the concept of automation. Here are five ways big data teams can capitalize on this trend to produce better products, achieve quicker results, and enjoy a higher level of efficiency.
1. Using the Right Platform to Support Workforces
Big data companies are usually big staff companies. Where there’s a lot of information to be managed, there’s usually a big team to manage it. DevOps processes require a type of operational flexibility that often necessitates its own unique platform.
Yet, about a third of DevOps teams have no such automation program in place yet. Proper data management environments and distributed work platforms allow big data teams to coordinate their efforts more effectively, helping to facilitate synergy among developers and operators.
Not only can this make it easier to work on projects, but it can make it easier to brainstorm new ideas, observe trends among large sets of data, and speed up daily operations simply by giving workers an easier platform to leverage their skillsets.
2. Streamlining the Software Lifecycle for New Products
Delivering new products is a tricky process, because speed must be balanced with quality. Move too slow, and the market could pass you by. Move too fast, and a program could be full of errors upon release.
The software lifecycle, also known commonly as the software development lifecycle or SDLS, is a process designed to produce the best software in the shortest time. While various models exist, best practices include the following steps:
- Identify & Plan: Discover current issues by getting input from experts, customers, stakeholders, and of course team members. Then plan out the requirements of the new software along with cost estimates and release timelines.
- Design & Build: After a plan is put together, the DevOps process sees all members of the core team combine their efforts to generate the code and build the core product.
- Test & Deploy: After testing the core product and all of its intended functions, the team deploys the software into the market for sale, or in some cases for use in a preliminary period of beta testing among users in a real-world environment.
The only step beyond this is to maintain the software – and with the DevOps automation approach, it is easier for teams to collaborate to accomplish these steps in the right order. By combining the first two entries, we see it is possible to achieve better results throughout the SDLC with the right DevOps cloud automation platform. DevOps tools like Docker registry and JFrog can integrate to automate the process of securely creating, deploying and running applications
3. Use a System That Adapts to New Data
Big data organizations aren’t just focused on managing data – they’re focused on being managed by data. When information comes in, they want to use it to alter their operations accordingly.
Consumer feedback can be used to alter product catalogs, beta tester reports can be used to enhance existing products, etc. The only question is how can a company utilize big data in this way?
Automation has made the impossible possible. Specialists have discussed how automated systems can learn and adapt when given new data. Consider how artificial intelligence algorithms can pick out patterns in data and alter their own commands accordingly.
Data pools become goldmines of information when technology is smart enough and fast enough to pick out trends and implement changes accordingly.
4. Identifying (and Overcoming) Bottlenecks
Despite their best efforts, no organization operates at true peak efficiency. There are plenty of areas they may wish to tighten up, although discovering these areas has sometimes proved problematic.
Bottlenecks are defined as those issues that cause progress and efficiency to slow down. Maybe software developers are slow to get certain critical segments of code to IT operators? Perhaps IT operators don’t log all data properly about deployment that coders need to deliver patches?
DevOps automation work environments allow individuals to collaborate easier, discover their bottlenecks quicker, and overcome them.
The key is continual improvement and monitoring. By considering the return on investment of a particular decision and reevaluating the results, big data teams can get more out of their data pools and their product development efforts.
5. Build Consistency Through Shared Standards
One of the most challenging aspects of big data organizations involves maintaining consistency throughout work environments.
Sure, all software companies may want a certain level of quality control on their products. But with dozens, hundreds, or thousands of individuals working on various aspects of software development, it can be easy for inconsistencies to arise.
There have already been success stories involving using automation to build consistent environments where quality control was easier to maintain. Consistency means a reduced change of errors and a better product overall.
DevOps Automation is a Great Fit for Big Data
Big data companies often find themselves struggling to manage their massive data pools, coordinate the efforts of their huge staffs, and release products that have consistent standards across the board.
Automation is about simplifying the mundane tasks and streamlining a great deal of the daily operations organizations face. Combine this with concepts like distributed cloud work environments and machine learning, and it opens many new possibilities. It is easy to see why big data companies are quick to jump on this trend.
When big data companies can sort through things a little faster while still getting the most out of the information, it’s a win-win. Big data can mean big results if companies are able to leverage things like the DevOps automation process.