It Fields on the Rise in 2022
In the ever-changing world of technology, advancements and innovations are continuously occurring in trying to attain a better version of the current technology being implemented today. This comes with different technology fields that arise due to people’s needs and demands. Knowing and learning the popular and in-demand IT Fields today will surely land an IT Practitioner a job in whatever field that is currently needed. These IT fields are the following:
- Blockchain
- DevOps
- Snowflake
- AI as a Service
- HyperAutomation
- Cybersecurity
- Full Stack Development
- Edge Computing
- Internet of Behaviors
- Predictive Analytics
Blockchain
It is a method of storing information in such a way that it is difficult or impossible to change, hack, or cheat the system.
A blockchain is a digital ledger of transactions that is replicated and distributed across the blockchain’s entire network of computer systems. Each block in the chain contains transactions, and whenever a new transaction occurs, a record of that transaction is added to the ledger of each participant.
Distributed Ledger Technology refers to a decentralized database managed by multiple participants (DLT).
Blockchain is a type of distributed ledger technology in which transactions are recorded with an immutable cryptographic signature known as a hash.
DevOps
The term DevOps is a combination of the terms development and operations, and it refers to a collaborative or shared approach to the tasks performed by an organization’s application development and IT operations teams.
DevOps, in its broadest sense, is a philosophy that promotes better communication and collaboration among these teams – and others – within an organization. It, refers to the use of iterative software development, automation, and programmable infrastructure deployment and maintenance. Building trust and cohesion between developers and system administrators, as well as aligning technological projects to business requirements, are examples of culture changes.
DevOps has the potential to transform the software delivery chain, services, job roles, IT tools, and best practices.
Snowflake
Snowflake is a fully managed SaaS (software as a service) that was developed in 2012 to provide a single platform for data warehousing, data lakes, data engineering, data science, data application development, and secure sharing and consumption of real-time / shared data. To meet the demanding needs of growing enterprises, Snowflake includes out-of-the-box features such as storage and compute separation, on-the-fly scalable compute, data sharing, data cloning, and third-party tool support.
Snowflake’s cloud data platform is built on the following components:
- Cloud services
- Query Processing.
- Database Storage
AI as a service
Artificial intelligence as a service (AIaaS) refers to off-the-shelf AI tools that allow businesses to implement and scale AI techniques at a fraction of the cost of a full-fledged in-house AI.
Because it is based on cloud computing, the concept of everything as a service refers to any software that can be accessed across a network. In most cases, the software is readily available. You buy it from a third-party vendor, make a few changes, and start using it almost immediately, even if it hasn’t been completely customized for your system.
AIaaS is the solution for businesses that are unable or unwilling to build their clouds and build, test, and deploy their artificial intelligence systems. The most appealing aspect is the opportunity to benefit from data insights without requiring a large upfront investment in talent and resources
AIaaS, like other “as a service” options, offers the following advantages:
- Maintaining focus on the core business (not becoming data and machine learning experts)
- Keeping investment risk to a minimum
- Increase in the value you derive from your data
- Increasing strategic adaptability
- Increasing cost flexibility and transparency
hyperAutomation
It is a business-driven, disciplined approach to rapidly identifying, validating, and automating as many business and IT processes as possible. Hyperautomation entails the coordinated use of multiple technologies, tools, or platforms, such as:
- Artificial intelligence (AI)
- Machine learning
- Event-driven software architecture
- Robotic process automation (RPA)
- Business process management (BPM) and intelligent business process management suites (iBPMS)
- Integration platform as a service (iPaaS)
- Low-code/no-code tools
- Packaged software
- Other types of decision, process and task automation tools
Cybersecurity
It is the practice of safeguarding critical systems and sensitive data against digital attacks. Cybersecurity measures, also known as information technology (IT) security, are intended to combat threats to networked systems and applications, whether they originate within or outside of an organization.
A strong cybersecurity strategy must defend against cyber crime, such as cyber attacks that attempt to access, change, or destroy data, extort money from users or the organization, or disrupt normal business operations.
Countermeasures should address the following:
- Critical infrastructure security
- Network security
- Application security
- Cloud security
- Information security
- End-user education
- Disaster recovery
- Storage security
Full Stack Development
Full stack technology refers to the entire depth of a computer system application, and full stack developers work in both the front end and the back end of web development.
Everything that a client or site viewer can see and interact with is on the front end. The back end, on the other hand, refers to all of the servers, databases, and other internal architecture that power the application; the end user rarely interacts with this domain directly.
Edge Computing
Edge computing is a networking philosophy that focuses on bringing computing as close to the data source as possible in order to reduce latency and bandwidth consumption. In layman’s terms, edge computing entails running fewer processes in the cloud and relocating them to local locations, such as a user’s computer, an IoT device, or an edge server. By bringing computation to the network’s edge, the amount of long-distance communication between a client and server is reduced.
When it comes to Internet devices, the network edge is the point at which the device, or the local network that contains the device, communicates with the Internet. The term “edge” is a bit of a misnomer; for example, a user’s computer or the processor inside an IoT camera can be considered the network edge, but so can the user’s router, ISP, or local edge server. The key takeaway is that the network’s edge is geographically close to the device, as opposed to origin servers and cloud servers, which can be very far away from the devices with which they communicate.
Internet of Behaviors
The Internet of Behaviour refers to the collection of data (BI, Big Data, CDPs, and so on) that provides valuable information about client behaviors, interests, and preferences (IoB).
The IoB attempts to comprehend data obtained from users’ online activities from the perspective of behavioral psychology. It seeks to answer the question of how to interpret data and how to apply that knowledge to the development and promotion of new products, all from the standpoint of human psychology.
The term “IoB” refers to a method of analyzing user-controlled data from the perspective of behavioral psychology. The study’s findings have an impact on new ways to create a user experience (UX), search experience optimization (SXO), and how a company’s final products and services are advertised.
As a result, while doing IoB is technically simple, it is psychologically difficult. It is necessary to conduct statistical studies that record everyday routines and behaviors without completely revealing customer privacy for ethical and legal reasons.
Predictive Analytics
Predictive analytics is a type of technology that makes predictions about future unknowns. To make these determinations, it employs a variety of techniques, including artificial intelligence (AI), data mining, machine learning, modeling, and statistics.3 For example, data mining entails the analysis of large sets of data to detect patterns. Except for large blocks of text, text analysis works in the same way.
Predictive models are used for a variety of purposes, including:
- Weather forecasts
- Creating video games
- Translating voice to text for mobile phone messaging
- Customer service
- Investment portfolio development
All in all, these said arising IT Fields right now are all brand new to us people, but this is what technology seeks for. It is to always strive for brand new ideas and concepts which will contribute to the overall development of the world, specifically in its technological aspect.
References:
https://www.euromoney.com/learning/blockchain-explained/what-is-blockchain
https://searchitoperations.techtarget.com/definition/DevOps
https://www.snaplogic.com/blog/snowflake-data-platform
https://www.bmc.com/blogs/ai-as-a-service-aiaas/#:~:text=Artificial%20intelligence%20as%20a%20service,it%20relies%20on%20cloud%20computing.
https://www.gartner.com/en/information-technology/glossary/hyperautomation#:~:text=Hyperautomation%20is%20a%20business%2Ddriven,Machine%20learning
https://www.ibm.com/topics/cybersecurity
https://bootcamp.learn.utoronto.ca/blog/what-is-a-full-stack-developer/#:~:text=Full%20stack%20technology%20refers%20to,can%20see%20and%20interact%20with.
https://www.cloudflare.com/learning/serverless/glossary/what-is-edge-computing/
https://www.analyticssteps.com/blogs/introduction-internet-behaviour-iob
https://www.investopedia.com/terms/p/predictive-analytics.asp
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