Top 3 Trends In Microservices | Charter Global

By Author: Charter Global
Total Articles: 71
Comment this article

Microservices is a software development methodology, or Service Oriented Architecture (SOA) style responsible for application structuring. From the performance of microservices leading up to 2019, experts have identified some trends that will characterize microservices in 2020.

1: Test Automation

Conventionally, individuals structured test cases for determining whether or not software will work correctly across a wide range of circumstances. QA Automation Testing Services engineers were often responsible for creating and running test cases. The current trend is for software engineers to be in charge of testing, instead of the traditional QA team. This is the result of test-driven development that requires developers to carry out tests throughout the Continuous Integration (CI) pipeline.

Businesses and organizations look forward to a software testing services that automates design and has the ability to run and report the results. So, there should be no friction with this solution. It should smoothly connect to CI systems, add comments as a human engineer would do, and analyze new code in real time

2: ...
... Incident Response

The rise of Site Reliability Engineers is a response to the intricately distributed systems. These systems suffer resiliency challenges often. So, the engineers automate manual processes that were previously performed by system admins. Now, they are in charge of efficiency, performance, latency, availability, capacity planning, and emergency response.

The financial implications of downtime can be quite significant, which necessitates a fast solution. According to Gartner, this financial implication is up to approximately 6k average revenue loss per minute. In fact, big retail sites, such as Amazon, may encounter up to $220k revenue loss per minute. Overall, it is not just money that is lost, the brand’s image is also at risk with such downtime issues.

Solutions are expected to be geared towards providing innovative incident response. Therefore, any centralized platform should also list out the impact and status of the incident, including the event timeline, status and effect on the long run, in addition to assigning roles and kicking off workflows.

3: CD/Verification for Enhanced Productivity

CD simply stands for Continuous Deployment. There’s always pressure on businesses to speed up software release cycles. With CD, any code that passes testing is applied to production in an automated manner. Compared to CI, the task of ensuring code can be safely and rapidly deployed to production is accomplished by a set of design. Continuous Deployment goes further to manage the full deployment.

The manual activities of DevOps engineers are being replaced by CD. Because of this, CD will be aided to become a point of intelligent control for multi-cloud environments. It will make predictive abilities available. Such predictive capabilities include insights into the best region, cloud, as well as configuration for service deployment in line with its circumstances.

Finally, experts have predicted that more developers will be tooling around the deployments of microservices, which would cut down the cost associated with complexity. So, organizations can keep on the trend of migration to microservice architectures.

Read More:https://www.charterglobal.com/3-trends-in-microservices/

Software development continues to develop and change each year. By researching the latest trends and keeping your process relevant, your business has a greater chance at success. If you are looking to build a scalable digital solution for your business, you should approach a software development company that works on the latest technology trends and implements the above technology stack.

Get in touch with our team to discuss IT staffing and software development solutions that can supersede your existing solutions on mobile and web applications.

QA Automation Testing Services microservices microservice architectures QA Automation Testing Automation Testing Automation Testing Services software testing services Software Testing Methodologies

Machine Learning Services: A Valuable Enterprise
Machine Learning is a valuable player in the realm of the Internet of Things. ML and Internet of Things (IoT) have gained tremendous popularity over the past few years, considered by many as revolutionary, game changing tech. Yet, much confusion exists in terms of understanding the purpose of Machine Learning Models, along with it’s benefits and suitability for use.

Here’s a breakdown of Machine Learning, benefits of ML in AI and IoT, when it should be used, and it’s real-world applications today.

When Is Machine Learning Valuable?

In general, machine learning is valuable when you know what you want but you don’t know the important input variables to make that decision. So you give the machine learning algorithm your stated goals, or inputs. Based on Machine learning systems, and then it “learns” from the data which factors are important in achieving that goal.

The data models that are typical of traditional data analytics are often static and of limited use in addressing unstructured, fast-changing, sequestered amounts of data. When it comes to IoT, it’s often necessary to identify correlations between dozens of sensor inputs and external factors that are rapidly producing millions of data points.

In addition, machine learning development services has the ability to accurately predicting future events. Whereas the data models built using traditional data analytics are static, machine learning algorithms constantly improve over time as more data is captured and assimilated. This means that the machine learning algorithm can make predictions, see what actually happens, compare against its predictions, then adjust to become more accurate.

The predictive analytics made possible by machine learning are hugely valuable for many IoT applications. Let’s take a look at a few concrete examples.

How are Machine Learning Applications used in IoT?

1. Cost Savings in Industrial Applications:

Predictive capabilities are extremely useful in an industrial setting. By drawing data from multiple IoT sensors in or on machines, machine learning applications can “learn” what’s typical for the machine and then detect when something abnormal begins to occur.

Predicting when a machine needs maintenance via IoT data is incredibly valuable, translating into millions of dollars in saved costs. A great example is Goldcorp, a mining company that uses immense vehicles to haul away materials.

When these hauling vehicles break down, it costs Goldcorp $2 million per day in lost productivity. Goldcorp is now using machine learning to predict with over 90% accuracy when machines will need maintenance, meaning huge cost savings.

2. Shaping Experiences to Individuals:

We’re actually all familiar with machine learning applications in our everyday lives. Both Amazon and Netflix use machine learning to learn our preferences and provide a better experience for the user. That could mean suggesting products that you might like or providing relevant recommendations for movies and TV shows.

Similarly, in IoT machine learning can be extremely valuable in shaping our environment to our personal preferences.

The billions of sensors and devices that will continue to power connected devices, smart homes, and IoT devices in the coming years will generate exponentially more data. This huge increase in data will drive great improvements in machine learning, opening countless opportunities for us to reap the benefits.

Not only we will be able to predict when machines need maintenance, we’ll be able to predict when we need maintenance too. Machine learning will be applied to the data from our wearable to learn our baseline and determine when our vitals have become abnormal, calling a doctor or ambulance automatically if necessary.

Beyond individuals, we’ll be able to use that health data at scale to see trends across entire populations, predicting outbreaks of disease and proactively addressing health problems.

Although both machine learning and IoT can be over-hyped, the future of machine learning applications in IoT are worthy of that hype. We’re really just scratching the surface of what’s possible.

Read More:

https://www.charterglobal.com/machine-learning-a-valuable-enterprise/

Referene Link:

https://www.charterglobal.com/technology-solutions/qa-automation-testing-services/

Software development continues to develop and change each year. By researching the latest trends and keeping your process relevant, your business has a greater chance at success. If you are looking to build a scalable digital solution for your business, you should approach a software development company that works on the latest technology trends and implements the above technology stack.

Get in touch with our team to discuss IT staffing and software development solutions that can supersede your existing solutions on mobile and web applications.

Contact Us:

Charter Global
One Glenlake Parkway, Suite 525, Atlanta, GA 30328
Phone: (888) 326 9933
Fax: (770) 326 9922
Email: info@charterglobal.com