How big data and predictive analytics improves DevOps practices
DevOps practices are becoming more and more prominent in software development and with good reason. This quiet revolution in software development has allowed them to develop software at a rate that meets consumer expectations. However, we have not discussed the impact of big data and predictive analytics on DevOps. In this blog post, I am going to explain how predictive analytics and big data augments DevOps practices and what this means for software development.
What do we refer to when we say DevOps?
Before diving into how big data and predictive analytics affects DevOps practices, we need to discuss what we mean by DevOps.
DevOps is the new method for software development, it espouses a shift from the traditional waterfall methodology and linear, sequence-based development to a method that is based on collaboration between different team members and automation to develop a continuous process of software development and deployment.
Rather than building a software package one step at a time and releasing a final product, DevOps practices focus on developing and deploying software one feature at a time, starting with the barebones and improving the feature with each iteration. This allows for streamlined software development, more efficient practices and better quality with fewer bugs (even if it is a little thin at first).
As you can imagine, DevOps practices are more than just a production pipeline for software, it is a cultural shift in the software development sector.
Augmenting DevOps practices with big data and predictive analytics
DevOps deploys features at an incredible rate – the average time for software deployment is four seconds. However, as effective as it is now, I have several reasons to believe that DevOps will be even better with predictive analytics and big data.
Improved testing practices
Big data and predictive analytics improve DevOps practices with more efficient and better testing practices. Testing, bug detection and bug fixing are crucial steps in building software under DevOps. Analytics improves this process thanks to machine learning algorithms, which can detect new errors, alert testers and even compile testing libraries to help fix these bugs, making testing more efficient and reducing the chances of bugs slipping through cracks. Data-handling is also a source of problems because the more complex an app is, the larger the data set is and the more likely you are going to find bugs. Tools to handle big data and predictive analytics can detect errors early in the production pipeline, which makes it easier to analyse big data for errors.
Better supervision in production environments
Big data and predictive analytics help developers overcome the challenges of deploying software in the production environment.
One of the core DevOps practices is mimicking the production environment in the development environment. However, this can be incredibly challenging because the production environment is influenced by different sources of data that is not easily found in the development environment, making it difficult for developers to develop the application accordingly. However, this problem is resolved with help from big data and data specialists because they can anticipate the types of data that will affect the software when it launches in the production environment.
When an app launches in the production environment, predictive analytics can monitor the performance of the applications to give a picture of their normal performance. With this monitoring system, analytics can create awareness of fluctuations in performance and change resource levels appropriately. For example, if there is an increase in performance, then the system provides additional resources, if the system is idle, then it will take resources away. This method significantly improves DevOps practices because it prevents several issues, like memory leaks and DDOS attacks.
Better app security
Security is a vital feature any developer needs to invest time in. Predictive analytics can help tremendously in this regard because machine learning algorithms in predictive analytics can study usage patterns of different developers, this data allows them to anticipate anomalies, predict potential data breaches and malicious use. The system can even prevent security breaches in real-time saving millions of dollars and improving security.
Paving the way for better software delivery
DevOps practices are beefier, faster and more efficient with predictive analytics and big data in the mix. This is only going to improve the rate of software delivery and the quality of software will continue to improve exponentially thanks to technologies like predictive analytics and DevOps.