Eyeing the latest Trends of Java Technology

Moving towards Java 9

Oracle had officially released Java SE (Standard Edition) 9 in September 2017. The current version of Java comes along with more than 150 novel features plus APIs (application programming interfaces).

Real-Time Application Development

Nowadays a huge number of appliances and devices are capable of delivering precise information rapidly by means of real-time software applications. When developing real-time applications, several programmers choose Java than any other language for programming. Moreover, APIs given by the standard edition of Java enables the programmers in writing traditional real-time applications for a number of appliances plus equipment. Further, the programmers might even take benefit of the APIs given by the Java Standard Edition in order to have efficient execution of the real-time applications along with their integration with the 3rd party components smoothly. Java would probably be utilized extensively via developers for the development of applications in the upcoming future.

Modularity Principle of Designing

Programmers can now take advantage of extensively utilizing the modularizing of the design principle when programming in Java 9. Besides, they are now capable of just development of big and composite applications by means of divining the program within the several modules. Also, it helps in deploying modules like effortlessly associated units. 

Diversification of Modules 

Along with dividing the program in different modules, Java SE 9 is even capable of enabling developers to utilize a diversity of modules including unnamed modules, application modules, platform modules, and automated modules. The developers actually can utilize application modules for accomplishing particular working. Similarly, they can use unnamed modules for including JAR files followed by classes over the class trackway. Further, the automated modules help in exporting the essential packages plus ingress other modules. 

Alike these trends there are several other trends a smart Java developers keep an eye on, in order to keep their applications programming relevant and efficient for a longer period of time. Some other trends also include:

  • Augmentation of traditional solutions of Big Data
  • Kotlin for the development of Android Applications
  • Development of IoT Application
  • New Tools and Frameworks for Development

However, these trends will keep updating from time to time and with the changing needs of the people. 

What is impacting Cloud Computing in organizations?

Since the past few years, a number of companies plus several types of organizations have accepted the implementation of cloud computing, moreover, this trend will doubtlessly sustain in upcoming years. 

Besides, cloud computing has got several other advantages for enterprises. For example, it assists enhanced optimization of Information Technology support or services, provided that cloud solutions offer peerless flexibility followed by limitless scalability at a cost-effective rate.

GDPR is mainly responsible for creating harmony within the organizations so as to implement steady guidelines in terms of data protection. Also, boost the legal settings of the concerned companies. Some of the recent updations in GDPR include:

  • Instigating novel obligations with respect to data protection.
  • A notable increment within the penalties levied over the enterprises in case they fail in meeting with the regulation.
  • Essentials in terms of information, as well as transparency, has noticeably heightened peoples’ rights.

Impact of GDPR on Cloud Computing

The New guidelines given by the GDPR not just offer opportunities, nevertheless even has got certain cloud computing challenges. The approach for data protection in the organizations, along with the amount of protection, is a very vital component that actually indicates the company’s readiness in terms of the GDPR.

For a large number of organizations, the rules and regulations are extremely complex. Moreover, organizational, legal, financial as well as technical challenges coming in the way due to the GDPR regulation have only partially been dealt with.

Impact of GDPR on Service Providers

The penalties levied over the service providers failing to meet with the GDPR regulations are undoubtedly extortionate. And since compliance for the regulation has now become compulsory for every single organization having visitors for their website within the European Union. The companies who are into application development are really working hard towards designing their products. All this to make certain that their products are all compliant with GDPR.

Furthermore, it is significant for every application development entity having an explicit understanding of the entire process. That includes acquiring, shifting, stowing as well as handling the data of every client. In addition, they even need to find alternatives to protect their client’s security. This will be followed by the means for enhancing data security to create the apps that are GDPR-compliant.

Any organization that has been involved in stowing or else organizing personal data of the citizens. Especially belonging to the European Union in the EU member states need to meet the regulations of GDPR. 

Preeminent product types of Microsoft Azure cloud services

Microsoft Azure, also known as Microsoft’s public cloud computing platform is extensively assessed as the PaaS (Platform as a Service) and Iaas (Infrastructure as a Service) platform offerings. Also, it offers a variety of cloud services, that includes those for analytics,  compute, storage as well as networking. Users are free to choose from the aforesaid services for developing along with scaling novel applications or else run the current applications within the public cloud platform.

Below is the categorization of leading Microsoft Azure cloud services into major product types:

  • Analytics: Analytics services offer distributed analytics plus storage. This is further being followed by the features for big data analytics, real-time analytics, machine learning, business intelligence (BI), data lakes, data warehousing as well as internet of things (IoT) data streams.
  • Media & CDN (content delivery network): Such types of services are mostly inclusive of digital rights protection, encoding, on-demand streaming and media playback as well as indexing.
  • Networking: This class of service consists of dedicated connections, virtual networks and gateways. Also, services in terms of traffic management & diagnostics, DNS (domain name system) hosting,  load balancing and network protection with respect to DDoS (distributed denial-of-service) attacks come under networking services of the azure.
  • Data storage: Data storage services of Microsoft Azure offers expandable cloud storage for both structured and unstructured data. Besides, it facilitates big data projects, insistent storage and real storage.
  • Compute: Such Microsoft azure services permit the deployment and management of VMs (virtual machines). Besides containers, as well as batch processing to the users also. It also aids access to remote applications.
  • Hybrid integration: Hybrid Integration services of Microsoft Azure are mainly for the site recovery, server backup, and connecting private & public clouds.
  • Web: This particular service facilitates the development, followed by the deployment of web applications. Moreover, it offers certain features such as search, API (application programming interface) management, content delivery, notification as well as reporting.
  • DevOps: This group of services offers the tools for project and collaboration. For instance, Visual Studio Team Services, that accelerates the software development process for DevOps. It even has certain features for DevOps tool integrations, application diagnostics and test labs for build tests & experimentation.
  • Identity and access management: IAM offerings make certain that only licensed users will have access to Azure services. Also, aids the protection of encryption keys along with other sensitive data over the cloud. Some of the services of IAM include Azure Active Directory as well as MFA (multi-factor authentication) support.
  • AI and machine learning: This one offers a broad range of services. Moreover, developers can use that for infusing AI, machine learning & cognitive computing potentiality into data sets and applications.

Why is Spring Boot vital while creating Microservices?

The structural module of Spring Boot helps in creating independent applications that would run instantly like the self-contained deployment units. Furthermore, developers are able to develop a number of configuration profiles within Spring for unlike situations. As well as separate divisions of their application configuration easily. Additionally, Spring Boot offers reliance descriptors of starters in order to lessen the necessity for hunting. By the means of illustrative codes along with identifying these descriptors.

Then the automatically Spring Boot configures Spring as well as libraries of 3rd-party throughout the process of development. Also, it offers relevant features, that includes the understanding of building patterns that are alike distributed systems.

Developers are capable of promptly deploying their applications utilizing Spring Boot’s basic upfront configuration. Generally, an individual requires to store the configurations for applications of microservice within the environment.

Besides, Spring Boot manages configurations for whole the services by the means of a point-to-point call for service so as to regain those particular configurations. By using its in-built features of auto-configuration, this structure would automatically start applying entire in-house dependencies that are necessary by the application.

One probable risk associated with the Spring Boot is the rising binary size of deployment owing to non-functioning dependencies. On the other hand, as the configurations are twain in one external as well as central places, developers are capable of organizing form controls. Along with reviewing deprived of the requirement for reinstating a service for the changes in configuration.

Further, Discovery Service feature Spring Boot’s preserves the record of service samples obtainable for operation in the cluster. As soon as the developer recognizes service for the contact followed by providing ID for the service, then Spring auto wires entire mappings as well as descriptions. Similarly, API gateway feature of the Spring Boot functions also is capable of automatically rerouting API requests towards the service illustrations that is responsible for the route being solicited via HTTP.

Even though, automation features of Spring Boot simplifies the overall development. However, converting prevailing or else legacy projects of Spring Framework to Spring Boot is quite challenging. Developers are incapable of deploying several web apps under alike process. This means that they wouldn’t be able to share controlled resources like connection pools.

AI with Analytics Eliminating IT Silos

Almost every vendor of Infrastructure offers some kind of analytics tool in order to manage as well as track their sales such as tools for Wi-Fi troubleshooting, WAN utilization analytics, MDM (mobile device management) systems and APM (application performance monitoring). There is one thing for every section of IT. Further, whichever technology or even tool that lessens across the network for delivering functional analytics for unlike constituents of IT is a huge accomplishment for the business. Thus, context is really a big concern.

For example, users frequently start blaming their Wi-Fi connection when they are not able to connect to the internet. Thus, it is important to know those efficient connections not just depend over associating along with the WI-FI access points. But even authenticating operators, rectifying the requests for domain, procuring IP addresses, accepting prompt responses from the application along with frequently crossing WAN links. That also towards every single application that is cloud-based.

Where should an organization look for Problems related to security or performance?

Aforesaid situations generally develop certain isolated IT silos as soon as things are wrong. A number of such silos come up with the processes of the businesses spread across the organization. Hence, moving towards the model of the IT model will not just kill a lot of time and efficiency but will be very costly.

Moreover, the worsening of the silo effect is nothing else but the overflow of novel IoT devices connected via Wi-Fi-. Thus, for the vital sequence of business missions, companies are nowadays littering networks of the enterprise.

According to certain recent reports, IoT devices will be exceeding the human populace across the globe. Also, this is probably the very first instance. Moreover, by the end of 2020, there are chances that there are almost 3 times more connected devices than human beings over the ecosystem.

Hence, with the rising pressure, networking, IT as well as security staff needs to search for an alternative in order to arrange the infrastructure management followed by the security operations. Leading players within the market landscape will be breaking down all these silos.

But the question is how?

Answer to the question is the merger of analytics with AI that will offer the operational assurance. The consolidation of such technologies offers security, services, Wi-Fi, networking and WAN to the application teams across the organization access to a sole source of IT accuracy. That is driven through quantitative data analysis.

Furthermore, new solutions for infrastructure management like dubbed AIOps edge platforms, have been strictly following the process of analysis infrastructure, device as well as data all over the network heap from the viewpoint of vendor-agnostic.

Rather than depending upon the discrete tools of a vendor, human interpretation as well as manual inspection. These platforms robotize the critical processes of IT by the means of assimilating large volumes. Moreover, of different types of data and continually determining the usual performance virtually for every single aspect of the network.

How does this Function?

Now all the new structures that have been deployed out of the chain are utilizing big data analytics & cloud computing. In order to measure the application, network service, Wi-Fi connectivity, device plus performance of WAN. Bunch of all the networks are in connection with the receiver that is further linked with other elements of a network. This includes AAA systems, WLAN controllers and routers applying basic protocols as well as APIs. One of the major usages of this data is advising and connecting details in terms of the functioning of the client device. Along with the other services, devices as well as applications all over the network.

In a nutshell, by the means of establishing the network data analysis across the entire IT organizations. Moreover, utilizing the new spell of AI along with analytics, traditional IT silos start disappearing.  Also, the inter-functional alliance becomes efficient so that IT professionals are capable of embracing it.

Recruitment Market Buzzing with Innovation

Nowadays, Artificial Intelligence has already started taking over the world of Human resource by a storm. The past year has been a game-changer with AI-driven solution in the recruitment industry for multiple arising problems including automated candidate sourcing, diversity hiring, hiring remote workers, enhance candidates experience as well as eradicated bias. Thus, it won’t be wrong to predict there will AI-powered solution will soon completely kick off almost every segment of the recruitment funnel.

Moreover, integrating ML to the solution along with AI can enable getting fascinating outcomes for job seekers.  For instance, this will assist any user to get automated job recommendations depending upon the keywords they have used in their Resume like experience, technology, industry, location and others.

Let’s have a look at one of the very first  AI-powered job portal that is integrated with ML and Microsoft Azure cloud. This offers a one-stop solution for all job seekers as well as organizations seeking the best potential regardless of the location. We are at the final stage of developing this solution for one of our clients in the US. The solution will provide the topmost matching jobs with the candidates with the assistance of artificial intelligence. Furthermore, one can link their Linkedin accounts that will enable them to get the analysis of their profile. 

In addition to job recommendations, the solution can help candidates in brushing up their skills with the most trending questions asked in the interview. 

This is just the start of revolution within the Human Resource or recruitment industry with the arrival of the first AI-based career service. And aforementioned are a few features of it that will change the lives of active job seekers as well as people seeking for a switch in their current position.

Key Strategies of Enterprise Data Management (EDM)

As per recent research conducted an amount of data being managed by businesses is mounting at an average rate of approximately 40 per cent a year. Moreover, along with the rising data that is being handled by the company, there has been a huge expansion in terms of types of data.

Data streams consist of everything right from inventory stats & financial information to images, videos as well as other unstructured data coming in from various sources including Internet of Things (IoT).  Hence, enterprises need a solution known an enterprise data management that will centralize, organize, and make the varied data accessible as well as useful to the business. 

Enterprise Data Management (EDM) narrates an organization’s capability to govern, integrate, secure and disseminate data from several data streams. Also, this has the potential to seamlessly and safely move the data amongst different systems. On the other hand, efficient EDM is not an easy task and can merely be accomplished via completely understanding the data as well as carrying out an inventive EDM strategy. 

Here are some of the best strategies for EDM:

Assessment – Businesses require a clear understanding of the flow and type of data that is already available with them to shape an efficient data management strategy. This process can possibly be time-taking, however, it is advantageous, a vital process that might assist in making certain that the management methods employed are well suited with the data.

Characterize Deliverables – Data management might sound like a vague term. It is very important for the enterprises to shape what they anticipate the accomplishment via implementing the enterprise data management. 

Describe Standards, Policies, and Procedures: Standards, guidelines, as well as procedures, are invaluable regulation, positioning data where it should be followed by assisting them to avoid corruption, loss of data and security breaches. The success of standards, as well as guidelines, adds a lot to the process in place to allow them. Procedures provide staff members with tools & methods that can be used to meet the needed standards. 

Quality Control – It is better to have no data than having corrupted data. Embracing the culture of data quality might help in protecting the data security along with integrity, ultimately preserving the worth of it. This is the place that brings data stewardship in. Also, for the businesses, it is crucial to keep in mind how worthy their data actually is followed by responsibly maintaining its quality.

Challenges Faced by Quality Assurance engineers in Microservice Ecology

Microservices provide unprecedented benefits within a software development environment. They assist in breaking bigger monolithic applications to smaller code & functionality blocks. Several microservices collectively construct a business application, where every single microservice communicates with another one via APIs.

On the other hand, this method has got its own challenges. The reality that there are multiple microservices creating an application means, followed by testing every single microservice individually, the entire application should also be approved to make certain that it is performing as required.  This makes the entire process of testing very complex. 

Moreover, along with every microservices, there is a need for getting APIs also tested, to ensure the seamless communicate amongst them.

Thus, the process of testing requires comparatively more testing engineers with detailed planning. However, nowadays, there are a number of solutions evolving to make the task smoother.  

Service Virtualization Products

The traditional method for testing cannot perform the detailed testing of every single inputs and output. At most, it can simulate definite end-points utilizing data. This merely offers fundamental verification of code & functionality. Also, isn’t reliable for simulating the real-world atmosphere where the application will actually be used.

This challenge can be beaten utilizing products of third party service virtualization that have become popular with time. These can simulate the end-point of an API in a manner that the overall call tree will get utilized, similar to that of the business environment. 

Unit & Integration Tests

CUnit Tests, as well as Integration Tests, make certain that each and every piece of the code even at the lowest levels is tested. Also, it ensures that every single microservice has been tested.

Likewise, other hacks that make the jobs of the QA engineers easier include System & User Acceptance Tests as well as Validating and Monitoring API Runtimes.

How are Machine Learning models Disrupting the Banking industry?

Machine learning (ML) techniques have been around for decades. However, big-data revolution along with the diving costs of computing power are currently developing them correctly with exceptional & non-theoretical analytical tools within banking all over a diversity of use cases, consisting of credit risk.

Algorithms of machine learning might seem very multifaceted as well as futuristic, however, the process of their working is very simple. Basically, they incorporate a colossal set of decision support tools to build a definite model. By churning via these trainees at great speeds, ML models are capable of discovering “hidden” arrangements, mainly in unstructured data that is commonly missed by statistical tools.

Outfitting (the analytical depiction of irregular errors in place of basic relationships) of the model is a common concern in terms of ML. But, this can easily be avoided by making a careful selection of input variables as well as a particular algorithm. One of the methods to safeguard against overfitting is to utilize the famous Random Forest algorithm. This is a collection of a number of deliberately “weakened” decision support tools. Basically a limited set of variables along with respective iteration of the model, by lessening the reliance over the specific variables.

In the second instance, the performance of the ML model is further tested over a holdout sample. That wasn’t utilized in the course of the model development. In case the performance of the model of sample is notably degraded, that’s the signal of overfitting.

ML plays a very important in evaluating the long-tail data that normally accounts for half of the bank’s portfolio. However, are not well perceived via traditional methods. Just consider the accounts with a lower share of the wallet. Generally, very little is known about them, also method to influence them seems quite reactive. On the other hand, ML has the capability to bring about insights within their attitude. In order to vigorously target potentially profitable accounts.

IoT Disrupting Manufacturing Industry

The IoT (Internet of Things) has had a substantial effect on the manufacturing industry. The inter-dependability of sensors, equipment, machines, materials, processing units, plants, software, cloud technology, mobile devices, departments, and processes is aiding attain innovative outcomes along with driving value within the manufacturing industry.

IoT is disrupting a lot of industries, however, it is the manufacturing sector that is gaining a maximum of it.

Here are some of the ways in which IoT is aiding manufacturing industry to advance:

  • Supply chain Visibility

Supply chain visibility is important as well as challenging, mainly for the food industry that includes a huge number of suppliers, who need to comply with the new regulations mandate for transparency. IoT within the industry assures real-time visibility to all the manufacturing procedures.

  • Data Storage

It is very important to keep the data collected from sensors as well as machines secured for analysis & application. The cloud facilitates resilience and productivity in leveraging the data to create it actionable for manufacturing companies.

  • Smart assembly

To fill the gap between enterprise networks and manufacturing, these organizations have started deploying smart networks. This lets them lessen the downtime through allowing the remote access to the systems as well as partners, followed by delivering precision, resilience and reliability from plant to enterprise.

  • Enhanced visibility

It is crucial for every manufacturing company to get improved visibility in terms of requirement of resources, the performance of the equipment and safety threats. They might build dashboards showcasing the details of plant ecology, efficiency, safety, as well as ROI (return on investments).

  • Extensive Plant view

Integrated systems of production are very crucial for manufacturers along with geographically scattered sites. This will also enable in reducing the lead times. Moreover, now for quick data transfer, improved responsiveness of the market, and prompt decision-making, people of this field can leverage IP (Internal Protocol) network technology that connects enterprise applications along with the device-level production data within real-time.