What Does a Cloud Engineer Do?

As companies worldwide have shifted away from using onsite data centers and server rooms, cloud computing platforms have been in high demand. According to the technology news site TechRepublic, about two-thirds of large companies are moving business applications and data storage to cloud services. For more than half of those companies, the transition to cloud services is the top strategic priority for their IT departments. 

Companies need highly skilled engineers to manage their use of the cloud, including application development, resource allocation and maintenance, and effective use of the features offered by the industry’s primary cloud services—Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. 

Top Responsibilities of a Cloud Engineer

Cloud engineer is less a specific job title and more of an umbrella term used to describe a number of cloud computing roles that focus on engineering, architecture, development, and administration. Here are some of the typical responsibilities of professionals in the most in-demand cloud computing roles. 

Cloud Engineering

Those in cloud engineering roles assess an organization’s technology infrastructure and explore options for moving to the cloud. If the organization elects to move to the cloud, a cloud engineer is responsible for overseeing the process, referred to as migration, and maintaining the new system. 

Along with these technical skills, cloud engineering requires managerial skills. Engineers are often called upon to negotiate with vendors, coordinate with other IT team members, and communicate with senior leadership about the progress of a cloud migration project. 

Cloud Architecture

These roles focus primarily on assembling the cloud infrastructure. Within a cloud environment, there are numerous computing, networking, and security services that all need to be configured properly. Configuration serves two key roles: To ensure that the right users have access to the right services (depending on their role within the organization) and that the company doesn’t incur unexpected or unnecessary charges. 

Contracts to use cloud services can be as concrete as charging to rent hardware to store data, or as abstract as charging to execute a function within a line of code. This variability means architects need to pay close attention to the fine print of cloud contracts and compare that to how their organization intends to use a cloud-based service. 

Cloud Development

These roles are responsible for creating the functions, applications, or databases that run on the cloud. Many of the best practices—fast load times, support for multiple Internet browsers, using as little memory as necessary—are analogous to more traditional software and database development. 

“But now, [these individuals] also need to understand the cloud environment, the tools, and how that’s different than working on a single machine or a private data center,” For example, these developers must understand how an application will respond when accessing databases in different locations or how to run functions or queries efficiently when renting hardware.

Cloud Administration

These roles are similar to the traditional system administrator function that manages an organization’s on-premise hardware and software, but with an emphasis on cloud-based services. Primary responsibilities include developing and implementing policies for the use of cloud services, managing requests for new technology, establishing a secure cloud environment, and ensuring appropriate availability of services, also known as uptime. 

Security and availability require careful attentions. The cloud platforms use a “shared model” where they guarantee for some but not all security measures. For example, an individual organization is responsible for building a firewall around the network that’s used to access cloud services with sensitive data and business applications. 

The Value of Education in Cloud Engineering

Cloud engineers must refine specific cloud computing skills in order to be successful in their roles. These skills range from software development and database administration to change management and data security. Paying attention to details and working as part of a team is also important.

These skills are similar to what a student in a typical computer science course may learn, or what a professional in a traditional on-premise computing environment may need. Many other computer science principles are also applicable to cloud computing, including computation, data structure, and system architecture. 

While there is often overlap between computer science and cloud computing coursework, there is value in education and training that is specifically tailored to a career in cloud engineering. Cloud engineers can especially benefit from specialized training in two key areas: gaining hands-on experience with cloud platforms and understanding how cloud resources are allocated and paid for. 

Understanding Cloud Resource Management

It’s also critical for a cloud engineer to understand how resources are used differently than in traditional computing environments. 

When an organization uses on-premise servers to run applications and store data, the costs are largely fixed. Before anything can happen, hardware must be purchased, network connections must be set up, and users’ accounts must be created. If more resources are needed, the organization’s leaders need to come together to decide what to buy.

Cloud platforms offer much more flexibility. An organization can start with the resources it needs at that particular time and add services as those needs evolve. This flexibility is possible because another entity—in this case, a multinational tech giant like Amazon, Google, or Microsoft—has invested in the resources and opted to allocate them to thousands of customers on a pay-per-use basis. 

This flexibility also brings complexity because of the way cloud platforms charge for their resources. For example, for some services, an organization needs to consider whether it makes sense to pay an hourly rate (for on-demand usage) or an annual rate (for more continuous usage). Users should also be prepared to pay higher rates if they need more data storage or intend to run applications with complex calculations. Finally, each platform also offers a variety of discount options that require consideration.


Top 10 Most Popular Programming Languages

Whether you’re new to programming or looking to brush up on your skills, it helps to know which languages are in high demand. Here are 10 of the most popular programming languages based on the number of job postings listed on job search sites, and factors such as ease of use and potential for growth.

Top 10 Most Popular Programming Languages

1. Python

Top 10 Popular Programming Languages Chart

Benefits: Python is widely regarded as a programming language that’s easy to learn, due to its simple syntax, a large library of standards and toolkits, and integration with other popular programming languages such as C and C++. In fact, it’s the first language that students learn in the Align program, Gorton says. “You can cover a lot of computer science concepts quickly, and it’s relatively easy to build on.” It is a popular programming language, especially among startups, and therefore Python skills are in high demand.

Drawbacks: Python is not suitable for mobile application development.

Common uses: Python is used in a wide variety of applications, including artificial intelligence, financial services, and data science. Social media sites such as Instagram and Pinterest are also built on Python.

2. JavaScript

Benefits: JavaScript is the most popular programming language for building interactive websites; “virtually everyone is using it,” Gorton says. When combined with Node.js, programmers can use JavaScript to produce web content on the server before a page is sent to the browser, which can be used to build games and communication applications that run directly in the browser. A wide variety of add-ons extend the functionality of JavaScript as well. 

Drawbacks: Internet browsers can disable JavaScript code from running, as JavaScript is used to code pop-up ads that in some cases can contain malicious content. 

Common uses: JavaScript is used extensively in website and mobile application development. Node.js allows for the development of browser-based applications, which do not require users to download an application.

3. Java

Benefits: Java is the programming language most commonly associated with the development of client-server applications, which are used by large businesses around the world. Java is designed to be a loosely coupled programming language, meaning that an application written in Java can run on any platform that supports Java. As a result, Java is described as the “write once, run anywhere” programming language.

Drawbacks: Java is not ideal for applications that run on the cloud, as opposed to the server (which is common for business applications). In addition, the software company Oracle, which owns Java, charges a licensing fee to use the Java Development Kit.

Common uses: Along with business applications, Java is used extensively in the Android mobile operating system.

4. C#

Benefits: Microsoft developed C# as a faster and more secure variant of C. It is fully integrated with Microsoft’s .NET software framework, which supports the development of applications for Windows, browser plug-ins, and mobile devices. C# offers shared codebases, a large code library, and a variety of data types.

Drawbacks: C# can have a steep learning curve, especially for resolving errors. It is less flexible than languages such as C++. 

Common uses: C# is the go-to language for Microsoft ad Windows application development. It can also be used for mobile devices and video game consoles using an extension of the .NET Framework called Mono.

5. C

Benefits: Along with Python and Java, C forms a “good foundation” for learning how to program, Gorton says. As one of the first programming languages ever developed, C has served as the foundation for writing more modern languages such as Python, Ruby, and PHP. It is also an easy language to debug, test, and maintain.

Drawbacks: Since it’s an older programming language, C is not suitable for more modern use cases such as websites or mobile applications. C also has a complex syntax as compared to more modern languages.

Common uses: Because it can run on any type of device, C is often used to program hardware, such as embedded devices in automobiles and medical devices used in healthcare. 

6. C++

Benefits: C++ is an extension of C that works well for programming the systems that run applications, as opposed to the applications themselves. C++ also works well for multi-device and multi-platform systems. Over time, programmers have written a large set of libraries and compilers for C++. Being able to use these utilities effectively is just as important to understanding a programming language as writing code, Gorton says.

Drawbacks: Like C, C++ has complex syntax and an abundance of features that can make it complicated for new programmers. C++ also does not support run-time checking, which is a method of detecting errors or defects while software is running. 

Common uses: C++ has many uses and is the language behind everything from computer games to mathematical simulations.

7. Go

Benefits: Also referred to as Golang, Go was developed by Google to be an efficient, readable, and secure language for system-level programming. It works well for distributed systems, in which systems are located on different networks and need to communicate by sending messages to each other. While it is a relatively new language, Go has a large standards library and extensive documentation.

Drawbacks: Go has not gained widespread use outside of Silicon Valley. Go does not include a library for graphical user interfaces, which are the most common ways that end-users interact with any device that has a screen.

Common uses: Go is used primarily for applications that need to process a lot of data. In addition to Google, companies using Go for certain applications include Netflix, Twitch, and Uber.

8. R

Benefits: R is heavily used in statistical analytics and machine learning applications. The language is extensible and runs on many operating systems. Many large companies have adopted R in order to analyze their massive data sets, so programmers who know R are in great demand. 

Drawbacks: R does not have the strict programming guidelines of older and more established languages. 

Common uses: R is primarily used in statistical software products. 

9. Swift

Benefits: Swift is Apple’s language for developing applications for Mac computers and Apple’s mobile devices, including the iPhone, iPad, and Apple Watch. Like many modern programming languages, Swift has a highly readable syntax, runs code quickly, and can be used for both client-side and server-side development. 

Drawbacks: Swift can only be used on newer versions of iOS 7 and will not work with older applications. As a newer programming language, the code can be unstable at times, and there are fewer third-party resources available to programmers.

Common uses: Swift is used for iOS and macOS applications. 

10. PHP

Benefits: PHP is widely used for server-side web development, when a website frequently requests information from a server. As an older language, PHP benefits from a large ecosystem of users who have produced frameworks, libraries, and automation tools to make the programming language easier to use. PHP code is also easy to debug.

Drawbacks: As Python and JavaScript have gained popularity, PHP’s popularity has dropped. PHP is also known for its security vulnerabilities. According to Indeed, most PHP programmers take short-term roles that last less than one year.

Common uses: PHP is the code running content-oriented websites such as Facebook, WordPress, and Wikipedia. 

7 Other Programming Languages to Consider

The following programming languages aren’t quite as popular as the 10 listed above, but they are also worth considering if you’re looking to expand your programming options.

  • Dart is optimal for programming applications that need to run on multiple platforms, such as Windows and iOS. 
  • Kotlin is used to develop applications for the Android OS. 
  • MATLAB is a proprietary language developed by MathWorks and used for scientific research and numerical computing. 
  • Perl got its start for programming text, which makes it easy to learn and popular for developing a proof of concept. 
  • Ruby is losing traction as compared to other languages, but the Ruby on Rails framework was influential to other, later Web application frameworks for Python, PHP, and JavaScript.
  • Rust emphasizes high performance and security and is useful for applications where many things are happening concurrently.
  • Scala, named as a play on scalable language, is compatible with Java and is useful for cloud-based applications. 
Unique qualities of a low-code mobile app

Software 2.0 – No code / Low code platforms

Remember when software was eating the world? The trendy observation these days is that artificial intelligence (AI) is eating software.

Even Google CEO Sundar Pichai has spoken about software that “automatically writes itself.” And certainly, if you consider software development to be little more than the creation of oft-repeated segments of code, then the rapid advances in AI would give software engineers a pause.

Traditionally, developers have written software as a series of hard-coded rules:

If X happens then do Y. The human instructs the machine, line by line. That’s Software 1.0. But Software 2.0 recognizes that — with advances in deep learning — we can build a neural network that learns which instructions or rules are needed for a desired outcome.

The argument right now is that would we really write more codes Or  We’ll be just finding data and feeding it into machine learning systems. In this scenario, we can imagine the role of software engineer morphing into “data curator” or “data enabler.” Whatever we call ourselves, we’ll be people who are no longer writing code.

However, software engineering is not going away anytime soon. Even if a new role evolves — be it Software 2.0 engineer, data scientist 2.0, etc. — there are ways in which this technology shift will empower the practitioner of Software 1.0 or existing software engineers.

The three evolutions of no-code technology

The term “no-code movement” is fairly new in our common vocabulary. The concept that became no-code began in the 1990s as the software was starting to pick up. 

Phase 1: The all-in-one platform and single-function software (the 1990s)

In the early 1970s, computers were entirely command-line driven. If you couldn’t code, you couldn’t use a computer. By the 1980s, early graphic user interfaces (GUIs) designed by IBM made it possible to operate basic functions on a computer without needing to know code, but building and using most software was still code-driven.

By the 1990s, innovators like Microsoft and Adobe had built all-in-one programs that massively democratized how many people could use the software. Programs like Word, Excel, and Photoshop were built so that end-users didn’t need to know code in order to use the platform’s core feature set. 

These platforms made it easy to accomplish a specific task without code, but you would have to buy another piece of technology to accomplish a different task. Further, if you wanted these systems to connect or communicate in any way, you needed to know application programming interface (API) development and code the connections manually. 

Phase 2: Extended functionality, plugins, and app ecosystems (the 2000s)

At the turn of the millennium, new players democratized who could use – and even build on – computers. Tech startup Automattic launched WordPress in 2003, revolutionizing how people built their own websites. With pre-built themes with pre-built customization options, the average person could buy a theme (or even access dozens of free themes) to get a basic website live in minutes. 

A year later, Automattic launched WordPress Plugins, which were one-click features that you could install on your WordPress site to extend its functionality. According to reports, the first plugins allowed you to sell products via your WordPress site and embed forms to capture visitor information. Salesforce brought this concept to the business world, launching its AppExchange in 2005. Marc Benioff famously purchased the rights to the name “App Store” ahead of Salesforce’s launch, but ultimately chose the name “AppExchange” instead. He gave the “App Store” rights to his mentor, Steve Jobs, as a gift, which Apple used when it launched the App Store in 2008. E-commerce giant Shopify followed suit with the Shopify App Store in 2009.

The promise of apps and plugins was that people could easily extend the functionality of their site without code (or with only a few lines, instead of having to build whole features by themselves). Each app offered standard functionality and many offered premium options. Becoming an app or plugin developer not only became a full-time job for many companies but became a lucrative independent business model that paved to way for the future of no-code.

Phase 3: The ability to build apps without code (the 2010s – Present)

Throughout the 2010s, no-code platforms like WordPress, Salesforce, and Shopify continued to upgrade their core systems to make it easier to build and customize their platforms without code. App creators on these ecosystems also focused more heavily on additional features and customizability – whether in their free plans or adding these features as premium upsells.

But another innovation happened: no-code platforms started popping up to empower people to build whole apps without code. Instead of relying on plugin developers to make the feature you needed, you could build it yourself. Even API connections got the no-code treatment from no-code platforms like Zapier. 

The future of no-code movement

The earliest iterations of no-code builders helped with wireframes and prototypes, but people assumed you’d need to move to code eventually. Modern no-code builders, on the other hand, are capable of scaling up to thousands of users without ever needing to touch a line of code. This opens up significant opportunities for the future of no-code.

1. The no-code tech company

No-code is reaching levels of scalability and customizability that we’re likely to see whole tech companies running on products built without code. This not only democratizes who can build tech companies, but it also creates more opportunities for freelancers and other independent entrepreneurs to build “micro-SaaS” products – like online marketplaces, e-learning platforms, custom client portals, or subscription communities – that bring in great lifestyle revenue streams. 

2. Internal apps will be no-code

No-code makes it easy to build custom apps that are secure and user-friendly with no coding required. That means more companies are likely to turn to no-code tools for employee-use-only applications such as internal communications, time tracking, and even task tracking. This could especially be the case in startups that don’t have the budget to buy existing SaaS platforms or have unique needs because of the innovative nature of their company.

3. Entrepreneurship accelerated

New founders won’t need to go through the old process of building a wire frame and fundraising to build their MVP. Instead, future no-code tools will make it easier than ever to launch a tech company. You can build a prototype or even a full version one in a few days completely solo, then start testing and selling the product immediately. This will have profound effects for people that want to solve local business problems with technology, but that don’t have a large enough market to interest venture capitalists or software engineers to build a code-based solution. 

4. Going beyond business

Beyond business use cases, the no-code movement will make it easier than ever for community groups, nonprofits, and charities to build purpose-built apps for their causes. Outside of organizations, individuals can also build no-code apps for their own life – communicating with friends, managing their personal community, or building automation to make parts of their life easier or more powerful.

Evolution of low-code/no-code tools

Low-code/no-code platforms stem from earlier rapid application development (RAD) tools such as Excel, Lotus Notes and Microsoft Access that likewise put some development-like capabilities into the hands of business users (i.e., non-IT professionals).

However, those tools required users to thoroughly understand the business apps and their development environments in order to build capabilities. In contrast, with low-code and no-code options’ drag-and-drop features, users need either minimal or no knowledge of the tools or development in general.

Furthermore, development with RAD tools generally produced capabilities used by the individual who created the functionality, or by a limited number of users associated with the creator (usually a work group or business unit). Apps produced with low-code or no-code platforms, on the other hand, are robust enough to be used across departments and throughout the entire enterprise, and even by external users such as customers and business partners.

Unique qualities of a low-code mobile app

Low-code vs. no-code development platforms: What are the differences?

Low-code and no-code systems offer the same fundamental benefits, but their names indicate the key difference between these two methods of application development.

Low-code development requires users to do some level of coding, albeit much less than is required with traditional application development. Professional developers and programmers use low-code to quickly deliver applications, and to shift their efforts away from commodity programming tasks to more complex and unique work that has bigger impact and more value to the organization. Non-IT professionals with some programming knowledge also use low-code tools to develop simple apps or expanded functions within an app.

No-code development targets nontechnical users in various business functions who understand business needs and rules, but possess little or no coding experience and programming language skills. These citizen developers can use no-code to easily and quickly build, test and deploy their business apps, as long as the chosen tools align with these commodity functions and capabilities.

There are also some distinctions in how users apply no-code and low-code. No-code is typically used to create tactical apps to handle simple functions. Low-code can be used in those cases as well, but additionally to create apps that run processes that are critical to a business or to an organization’s core systems, such as certain integrations and digital transformation initiatives.

The line between no-code and low-code isn’t always clear — and this carries over into the low-code and no-code platforms themselves. Many technology product analysts consider no-code part of the low-code market, as even the strongest platforms require some level of coding for parts of the application development and deployment process. Vendors drive much of the distinction between low-code and no-code platform capabilities as they position their products for different groups of customers.

Generally speaking, no-code platforms are a specialized type of low-code cloud platform in which the required visual components address industry-specific functions, a specific line of business (LOB) or support a specific company’s corporate branding. Low-code platforms, on the other hand, may require the assistance of in-house developers to make small changes to back-end code so the new app will align with other business software.

Low-code development platform vendors

Dozens of mainstream and niche software vendors offer low- or no-code platforms, many of which run in the cloud. Gartner ranks nearly 20 in its “2020 Magic Quadrant for Enterprise Low-Code Application Platforms.”

An incomplete list of some of the most common low-code platform vendors and tools includes:

  • Appian
  • Claris FileMaker
  • DWkit
  • Google AppSheet
  • Looker 7
  • Mendix
  • Microsoft PowerApps
  • OutSystems
  • Robocoder Rintagi
  • Salesforce Lightning
  • Sisense
  • Skyve Foundry
  • Temenos (formerly Kony)
  • SIB Visions VisionX
  • Wix Editor X
  • Yellowfin 9
  • Zoho Creator

No-code development platform vendors

Many low-code platforms offer no-code functionality as well, such as Appian, Mendix, Microsoft PowerApps, OutSystems and Salesforce Lightning.

Other vendors that offer no-code development platforms include:

  • Airtable
  • AppGyver
  • AppSheet
  • Appy Pie
  • AWS Honeycode
  • Betty Blocks
  • Bubble
  • Carrd
  • Glide
  • Gumroad
  • Kissflow
  • Memberstack
  • Nintex
  • Notion
  • Outgrow
  • Payhere
  • Quickbase
  • Shopify
  • Stripe
  • Umso (formerly Landen)
  • Voiceflow
  • Zapier
  • Zudy Vinyl

BI & Data Visualization

For every business, there is a lot to deal with data. Data visualization becomes essential on every business and there is large demand in this area.

Data Visualization is a process that represents information visually so it can be learned quickly and easily. Business Intelligence is a process that focuses on gathering, organizing, recognizing patterns in, and deriving meaning from the information that is gathered by a business. As one of the essential steps in the business intelligence process, data visualization takes the raw data, models it, and delivers the data so that conclusions can be reached.

The Best Data Visualization Tools and Software


Platform: ChartBlocks

Description: ChartBlocks offers an online chart building tool that lets you import your data, design a chart, and then share it with ease. No coding is required to enable users to build charts via a simple designer. You can also choose from dozens of different chart types and pull data from almost any source. ChartBlocks supports creating charts that pull data from multiple sources as well. The tool touts a data import wizard that takes you through the entire process step-by-step.


Platform: Cyfe

Description: Cyfe offers a business dashboard creator that lets you monitor and visualize important data (social media, analytics, marketing, sales, support) in one place. The product features a number of pre-built dashboard templates that can be modified to meet specific needs. Users can also pull data from popular sources, and Cyfe touts more than 100 integrations and 250 metrics out-of-the-box. There are automated reports and widget alerts as well, which can be downloaded or scheduled.


Platform: Databox

Description: Databox offers a business analytics platform for KPI dashboards that pulls organizational data into one place so users can track performance and discover insights in real-time. The product lets you mix and match metrics from different sources into one dashboard. Databox features a DIY Dashboard Designer that enables users to pull the metrics they need, visualize KPIs in a number of ways, and build dashboards without the need for special coding or design skills.


Platform: Datawrapper

Description: Datawrapper provides a web tool for creating a variety of interactive charts and maps. No code or design skills are required, and charts can be embedded in your website. Users can simply copy data from Excel or Google Sheets or upload CSV files or link to a URL for live-updating. The basic (free) version allows for 1 user, 1 locator map, and 10,000 chart views per month. There are also several paid offerings that include access for additional users and more advanced capabilities.Learn more and compare products with the Solutions Review Buyer’s Guide for Analytics and Business Intelligence Platforms.

Easy Insight

Platform: Easy Insight

Description: Easy Insight offers a cloud business intelligence tool that enables you to create reports and dashboards and visualize data through charts and tables. The product also enables the combination of different services in databases so users can create a single view of customers across multiple apps. Easy Insight also touts the ability to create and update data right from your reports, as well as drill from charts into detailed data and click right back to the source.


Platform: Exago BI

Description: Exago BI is completely web-based and a prime embedded solution for software companies looking to provide ad hoc reporting, dashboards, and business analysis to their clients. Integration with web-based SaaS and or on-prem applications allows non-technical users to create reports and dashboards with no IT intervention. Exago BI offers a wide variety of advanced features including interactive data visualizations, geo-maps, drill-downs, user-defined formulas, and data merging into predefined templates.

Learn more and compare products with the Solutions Review Buyer’s Guide for Analytics and Business Intelligence Platforms.

Golden Software

Platform: Grapher

Description: Golden Software’s Grapher is a full-function graphing application for scientists, engineers, and business professionals. The product touts more than 80 unique and customizable 2D and 3D graph styles. Graphs can be enhanced with legends, titles and labels, summation plots, graph magnifiers, and statistical reports. Grapher also includes extensive analysis charts, tools, and statistical features to identify trends, find new insights and provide a holistic view of data.


Platform: Geckoboard

Description: Geckoboard offers a data dashboard product that makes it easy for you to share important data, metrics and KPIs clearly. The tool helps to streamline data from spreadsheets, databases and other tools for presentation purposes. Real-time metrics and more than 60 integrations make it easy to create live dashboards. Geckoboard also touts an unmissable summary of key performance indicators. Users can easily share dashboards with team members through email, chat, documents, or bookmark them for quick access.

Platform: Grow

Description: Grow offers a no-code, full-stack business intelligence and data visualization tool. The product features data integration capabilities that enable users to connect, store, and blend data from hundreds of data sources. Grow then provides the ability to marry and transform disparate data sources so you can filter, slice, and explore different visualizations. The built-in data explorer defines how you want to navigate data via charts and graphs which are displayed in metrics and dashboards.


Platform: GoodData

Description: The GoodData platform provides creation, delivery, and automated management of analytics at scale that allows organizations to deliver insights to customers, partners, and other users. GoodData is designed to support any analytical product roadmap and enables customers to offer their partners any data source, visualization, interaction pattern, or integration they need. The tool offers analytics and expertise to operationalize intelligent decisions within business applications and business processes.


Platform: iDashboards

Description: iDashboards offers a data visualization platform for customers in a wide array of vertical industries. The product features customizable and flexible dashboard tools that integrate with popular data sources. iDashboards lets users quickly view and analyze critical performance metrics as well. Dashboards can be created on any device while the solution touts hundreds of chart options for data visualizations of any kind. Users can share dashboards internally or publicly to keep the back-end secure. Role-based permissions and other integrations are also available.


Platform: Infogram

Description: Infogram offers an intuitive data visualization tool for users of varying skill levels. The product features ready-to-use templates and custom templates with your brand, colors, fonts, and logo. Infogram also touts interactive content via object animations like setting objects to zoom, bounce, flip and fade, as well as enhanced interactivity through tooltips, tabs, clickable legends, and linking. Teams can be organized into groups and permissions can be set by role.


Platform: Klipfolio

Description: Klipfolio offers a custom dashboard and reporting tool that combines formulas, functions, and data modeling to help users build actionable reports. Customers can choose from pre-built visualizations in the Klip gallery or incorporate proprietary branding with custom themes. Klipfolio enables the sharing of dashboards and reports via view-only links, PDFs, scheduled emails, or Slack messages. The solution provider offers free add-on products like PowerMetrics (lightweight data analytics) and MetricHQ (online dictionary of metrics and KPIs) as well.


Platform: Power BI

Related products: Power BI Desktop, Power BI Report Server

Description: Microsoft is a major players in enterprise BI and analytics. The company’s flagship platform, Power BI, is cloud-based and delivered on the Azure Cloud. On-prem capabilities also exist for individual users or when power users are authoring complex data mashups using in-house data sources. Power BI is unique because it enables users to do data preparation, data discovery, and dashboards with the same design tool. The platform integrates with Excel and Office 365, and has a very active user community that extends the tool’s capabilities.


Platform: pi

Description: pi is a three-in-one solution that combines business intelligence with simple reporting and predictive analytic capabilities via self-service dashboards. Users can white label the software and embed it into existing SaaS products or hook it up to cloud data warehousing and ETL tools for a more seamless experience. pi also centralizes various sources of data and uses AI analytics and automation to make insights readily available while dashboards can be completely customized for each client without the need to write code.

Tableau Software

Platform: Tableau Desktop

Related products: Tableau Prep, Tableau Server, Tableau Online, Tableau Data Management

Description: Tableau offers an expansive visual BI and analytics platform, and is widely regarded as the major player in the marketplace. The company’s analytic software portfolio is available through three main channels: Tableau Desktop, Tableau Server, and Tableau Online. Tableau connects to hundreds of data sources and is available on-prem or in the cloud. The vendor also offers embedded analytics capabilities, and users can visualize and share data with Tableau Public.


Platform: Toucan Toco

Description: Toucan offers a data storytelling platform designed for the non-technical decision-maker. The product lets users communicate actionable insights via a built-in no-code framework. Toucan Toco adapts to usage habits by deploying apps to all devices quickly without additional work, installation, or post-deployment maintenance. Included collaboration capabilities can be utilized via native integrations with Teams and Slack. The tool also touts an adaptive design and the ability to embed charts to websites or other platforms.


Platform: Visme

Description: Visme is an all-in-one visual communication tool that lets users create presentations, infographics, reports, social graphics, and more. The tool features more than 40 different chart and diagram types bar graph, pie chart, flowcharts, diagrams, line graphs, histograms), as well as live data integration and an easy-to-use graph maker. Graphs can be easily shared, downloaded, or embedded as well. Visme touts a free library of chart templates, millions of stock photos and icons, the ability to customize brand fonts and colors, and a drag-and-drop interface.



Platform: Zoho Analytics

Description: Zoho Analytics is a self-service BI and data analytics tool that lets you incorporate data from a wide range of sources, blend it together, and create cross-functional reports and dashboards. The product features a drag-and-drop designer, as well as different visualization tools to drill down to specifics. A “smart” assistant called Zia can answer questions in the form of reports and KPI widgets via AI, machine learning and natural language processing. Users can share and publish reports and smart data alerts ping you when outliers or anomalies happen.


Full Stack Development

In technology development, full stack refers to an entire computer system or application from the front end to the back end and the code that connects the two. Ideally the expectations are that Full stack developer is someone who knows UI / UX, Frontend, Business logic, APIs, Backend and databases

The front end includes everything that a client, or site viewer, can see and interact with. By contrast, the back end refers to all the servers, databases, operating system and other internal architecture that drives the application; usually, the end-user never interacts with this realm directly.

Front End Development

Front end, or client-side, development is the practice of producing an interface that end users can view. A front end developer’s primary goal is to develop a visually pleasing and easy-to-use platform.

This universal front end skill set includes:

  • HTML
  • CSS
  • JavaScript
  • DOM Manipulation

Back End Development

Back end development refers to the work that goes on behind the scenes of a site or application — the underlying architecture of the product. The term encompasses the database, server, and other intermediary proprietary software.

To accomplish this goal, back end developers must have a robust set of foundational skills. Including a few like:

  • Java
  • Object-Oriented Programming
  • Python

Technology related to full stack development:

Front end: It is the visible part of website or web application which is responsible for user experience. The user directly interacts with the front end portion of the web application or website.
Front end Languages: The front end portion is built by using some languages which are discussed below:

  • HTML: HTML stands for Hyper Text Markup Language. It is used to design the front end portion of web pages using markup language. HTML is the combination of Hypertext and Markup language. Hypertext defines the link between the web pages. The markup language is used to define the text documentation within tag which defines the structure of web pages.
  • CSS: Cascading Style Sheets, fondly referred to as CSS, is a simply designed language intended to simplify the process of making web pages presentable. CSS allows you to apply styles to web pages. More importantly, CSS enables you to do this independent of the HTML that makes up each web page.
  • JavaScript: JavaScript is a famous scripting language used to create the magic on the sites to make the site interactive for the user. It is used to enhancing the functionality of a website to running cool games and web-based software.

Front End Frameworks and Libraries:

  • AngularJS: AngularJs is a JavaScript open source front-end framework that is mainly used to develop single page web applications(SPAs). It is a continuously growing and expanding framework which provides better ways for developing web applications. It changes the static HTML to dynamic HTML. It is an open source project which can be freely used and changed by anyone. It extends HTML attributes with Directives, and data is bound with HTML.
  • React.js: React is a declarative, efficient, and flexible JavaScript library for building user interfaces. ReactJS is an open-source, component-based front end library responsible only for the view layer of the application. It is maintained by Facebook.
  • Bootstrap: Bootstrap is a free and open-source tool collection for creating responsive websites and web applications. It is the most popular HTML, CSS, and JavaScript framework for developing responsive, mobile-first web sites.
  • jQuery: jQuery is an open source JavaScript library that simplifies the interactions between an HTML/CSS document, or more precisely the Document Object Model (DOM), and JavaScript. Elaborating the terms, jQuery simplifies HTML document traversing and manipulation, browser event handling, DOM animations, Ajax interactions, and cross-browser JavaScript development.
  • SASS: It is the most reliable, mature and robust CSS extension language. It is used to extend the functionality of an existing CSS of a site including everything from variables, inheritance, and nesting with ease.
  • Some other libraries and frameworks are: Semantic-UI, Foundation, Materialize, Backbone.js, Express.js, Ember.js etc.

Back end: It refers to the server-side development of web application or website with a primary focus on how the website works. It is responsible for managing the database through queries and APIs by client-side commands. This type of website mainly consists of three parts front end, back end, and database.
The back end portion is built by using some libraries, frameworks, and languages which are discussed below:

  • PHP: PHP is a server-side scripting language designed specifically for web development. Since, PHP code executed on server side so it is called server side scripting language.
  • C++ It is a general purpose programming language and widely used now a days for competitive programming. It is also used as backend language.
  • Java: Java is one of the most popular and widely used programming language and platform. It is highly scalable. Java components are easily available.
  • Python: Python is a programming language that lets you work quickly and integrate systems more efficiently.
  • JavaScript: Javascript can be used as both (front end and back end) programming languages.
  • Node.js: Node.js is an open source and cross-platform runtime environment for executing JavaScript code outside of a browser. You need to remember that NodeJS is not a framework and it’s not a programming language. Most of the people are confused and understand it’s a framework or a programming language. We often use Node.js for building back-end services like APIs like Web App or Mobile App. It’s used in production by large companies such as Paypal, Uber, Netflix, Walmart and so on.

Back End Frameworks: The list of back end frameworks are: Express, Django, Rails, Laravel, Spring etc.

  • The other back end program/scripting languages are: C#, Ruby, REST, GO etc.

Note: JavaScript is essential for all stacks as it is dominant technology on Web.


Database is the collection of inter-related data which helps in efficient retrieval, insertion and deletion of data from database and organizes the data in the form of tables, views, schemas, reports etc.

  • Oracle: Oracle database is the collection of data which is treated as a unit. The purpose of this database is to store and retrieve information related to the query. It is a database server and used to manages information.
  • MongoDB: MongoDB, the most popular NoSQL database, is an open source document-oriented database. The term ‘NoSQL’ means ‘non-relational’. It means that MongoDB isn’t based on the table-like relational database structure but provides an altogether different mechanism for storage and retrieval of data.
  • Sql: Structured Query Language is a standard Database language which is used to create, maintain and retrieve the relational database.

Few Popular Stacks:

  • MEAN Stack: MongoDB, Express, AngularJS and Node.js.
  • MERN Stack: MongoDB, Express, ReactJS and Node.js
  • Django Stack: Django, python and MySQL as Database.
  • Rails or Ruby on Rails: Uses Ruby, PHP and MySQL.
  • LAMP Stack: Linux, Apache, MySQL and PHP

Internet of Things (IoT)

One of the biggest tech trends that’s emerging in recent years is the Internet of Things. Simply put, the Internet of Things (abbreviated IOT) is the idea that all technological devices can be connected to the internet and to each other in an attempt to create the perfect marriage between the physical and digital worlds.

Well, basically it’s adding on to the internet by integrating physical things into the network. So you do this by adding sensors to physical items.

Now, the Internet of Things creates cyber physical systems, which autonomously exchange information, trigger actions and control each other independently.

Top 10 Popular IoT Development Tools

Here are the top-class IoT development tools that can be employed by both developers and for all those who wish to pursue IoT as a DIY hobby.

1. Tessel 2

This is a hardware provider that can be used to build basic IoT solutions and prototypes. Tessel 2 lends a helping hand through its numerous sensors and modules. This is a board which can hold up to a dozen modules including the RFID, camera, GPS and the accelerometer.

All those Java developers who are proficient with Node.JS can use this device as Tessel can be programmed using Node.JS. This way, Tessel can be used to churn out a host of server and hardware firmware IoT solutions. Tagged as a robust IoT platform, you can leverage all the libraries of Node.JS to unveil a host of devices; within a matter of minutes.

It comes with two processors, the Tessel hardware makes use of a 580MHz Mediatek MT7620n and 48MHz Atmel SAMD21 coprocessor. While one can be used to run your firmware applications at a rapid speed, the other finds its use in exercising better input/output control and the efficient management of power.

2. Eclipse IoT

If you as an IoT developer are ordained to build IoT devices, Cloud platforms and Gateways, then Eclipse IoT can be your top bet. Recognized as a collaboration of various companies and individuals who are striving towards the development and establishment of IoT open technologies, Eclipse IoT can make all your IoT dreams come true.
Allowing you to develop, promote and adopt open source IoT technologies, Eclipse IoT is an instrument that can help you gain technical expertise. Simplylook into the vast assembly of services and projects delivered by the Eclipse team and you are all covered.

3. Arduino

If you are looking to build a computer that can sense and exercise a better control over the physical world when compared to your normal stand-alone computer, then Arduino can be your intelligent choice. Offering a perfect blend of IoT hardware and software, Arduino is an open-source prototyping and simple-to-use IoT platform. Arduino operates through a set of hardware specifications that can be applied to interactive electronics. The software of Arduino comes in the form of the Arduino programming language and Integrated Development Environment (IDE).

4. PlatformIO

Next in the list of top IoT development tools and platforms is PlatformIO which is a cross-platform IoT development environment. This platform comes with a build system, supported by a library manager and IDE. You have a choice to port the IDE on top of the Atom editor or you can go ahead and install it as a plug-in.

The best part of PlatformIO is that it is compatible with more than 200 boards. Coming with a wonderful debugging integration, PlatformIO is conspicuous of a serial port monitor. All those who are employing PlatformIO unanimously express one feeling and that is – “PlatformIO hastens up the development process of an IoT application, allowing us to deliver IoT solutions in record time”.

5. IBM Watson

Last in the list of top IoT development tools, but definitely not the least is IBM Watson, an API that allows you to attach a host of cognitive computing features to your IoT applications. This is an innovative tool that can also be used to predict the future.

Simplifying the tasks of IoT developers, IBM Watson through its numerous services helps to unveil chatbots which can understand the natural language. These chatbots can then be deployed on to messaging platforms and websites which can be viewed on various devices. It is through IBM Watson that IoT developers can successfully and swiftly build cognitive search and content analytics engines.

6. Raspbian

This IoT IDE is created for Raspberry Pi board by IoT tech enthusiasts. With over 35,000 packages and numerous examples along rapid installation with the use of pre-compiled software makes it an essential IoT development tool. Often, it is regarded as the best tool for Raspberry Pi for IoT app development. Another best part about this tool is that it is under constant development and has widened the reach for computing so that users can gain maximum benefits.

To learn about Raspberry Pi for home automation you can explore the different sections of “From 0 to 1: Raspberry Pi and the Internet of Things” online tutorial. It teaches you about the basics of IoT, Pis anatomy, Coding with Python, Physical Computing with Raspberry Pi and so much more!

7. OpenSCADA

This tool is a part of the SCADA project by Eclipse IoT industry groups. It is independent to any platform and is known for its security and flexibility along with a modern design. OpenSCADA also supports editing and debugging and comes with front-end applications, back-end applications, libraries, configuration tools and interface applications. Its different tools can be combined with the development of advanced IoT applications. Unlike other IDEs, OpenSCADA supports various programming languages and consists of sub-projects like Atlantis, Utgard, Orilla and others.

8. Node-RED

Node-RED is a simple visual tool that is built on Node.js, a server-side JavaScript platform that is widely used in IoT projects. It is an open-source tool mainly used to connect devices, services and APIs together with an integrated browser-based flow editor. With over 60,000 modules, it was developed by IBM with the aim of providing a user-friendly interface for developers allowing them to connect devices very quickly and easily.

For learning Node.JS from the very beginning you can opt for “NodejS from Ground Up for Beginners” online tutorial. It unfolds with the installation, NPM, serving files, real-time HTTP requests, Express, MongoDB and other core concepts.

9. Kinomo Create

It is a device that can connect two devices without extensive knowledge of JavaScript. Kinoma Create consists of everything which is required for developing small IoT applications like connecting light, temperature or movement sensors for a specific purpose with mobile notifications in case of any alterations. You can also find numerous tutorials on its practical applications at Kinoma’s website. It has been widely used for developing a synthesizer, a camera trap which takes a photo if an animal or any other objects disturbs the laser beam and an automatic alarm bell for alerting individuals in case of any situations.

10. Device Hive

Device Hive is a free open source machine to machine (M2M) communication framework which was launched in 2012. It is a Data Art’s AllJoyn based device and is considered one of the most preferred IoT app development platforms. It is generally a cloud-based API that you can control remotely without the need for network configuration. The same implies to the libraries, portals and management protocols. Mostly, it is used for security, automation, smart home tech and sensors. Additionally, it also has a dynamic community and myriads of online resources available to help you out.

Bonus one

11. Home Assistant

It is an open-source tool that is primarily aimed at home automation and functions with a Python-based coding system. The IoT system developed with this tool can be easily controlled with mobile or desktop browsers. Furthermore, its set up is easy and is trusted for operations, security and privacy. The software supports any systems which are running on Python 3 and all the systems get regular updates within 2 weeks. Despite the lack of cloud components, its ability to protect the data during this internet age gives it an edge over others.

5 YouTube channels IoT practitioners must subscribe to

The Internet of Things is one of the leading technologies that is ruling the market. It can connect devices with the internet and work remotely easily. This opens up opportunities for developers to dive deeper into the world of technology. Here are some of the YouTube channels that the IoT developers can subscribe to.

1. Microsoft IoT Developer
This channel offers the latest updates and news about the Internet of Things for developers. The challenges help developers to deep dive into the technologies or services of Microsoft IoT. The channel was started on July 3, 2014, with more than 1,545,525 views.

2. Stacey On IoT
This channel is all about the latest internet-connected gadgets by Stacey Higginbotham. She explores the IoT gadgets, showing an exciting and interesting way of putting light on the technology. The channel was started on July 14, 2017, with more than 235,733 views.

3. IoT Now
IoT Now, started on February 15, 2021, with more than 50,288 views is a famous channel that covers experts opinions and Global IoT news. The channel covers the IoT industry covering IoT videos, IoT magazines, IoT news, IoT experts, and IoT talking heads.

4. The Things Network
The channel was founded on March 11, 2016, and has obtained 656,754 views. LoRaWAN and LoRa unveil the major aspects of the Internet of Things covering up the global open crowdsourced data network.

5. IoT World Series
The channel includes education, animated, entertaining, informative, and onsite content for the Internet of Things. Founded on December 5, 2014, with 264,254 views, IoT World Series covers Embedded IoT World and IoT World as the global leading event series.


Data Science vs Data Analytics vs. Machine learning vs. Artificial Intelligence – Key Differences

In today’s day and age, the biggest asset for businesses is data. The more data they have access to, the more insights they can generate. Through data, they can see patterns that no one knew existed. This further helps them to make more informed decisions and stay ahead of the curve. Data science is basically a multidisciplinary field that essentially focuses on extracting insights from large data sets – both raw and structured. 

What Is Data Analytics?

By defining data analytics we’ll come a little closer to understanding data science vs data analytics. Data analytics comes under the purview of data science. It essentially processes and performs statistical analysis on the existing sets of data. So data analytics is not about finding questions but finding answers and gaining insights for problems that we know.

Unlike data science, here we already have a set of questions around which we are supposed to work. Data analytics, though related to data science, is much limited in its scope and is much more specific. It does not aim to look for connections between the data but ways to support the goal in mind. Precisely, data analytics analyzes raw data to make conclusions about that information. The techniques of analytics are used in organizations for making better and informed decisions and by scientists for verifying or disproving theories and scientific models.

What is Artificial Intelligence?

Artificial intelligence is nothing but the simulation of human intelligence in machines. AI enables machines to think, learn, and find solutions (solve problems) just like human brains do. AI possesses the power to rationalize like us and take actions that are most likely to accomplish a goal. Through artificial intelligence, machines can execute the desired tasks by imitating human intelligence.

What is Machine Learning?

Machine learning is a subset of artificial intelligence. It essentially gives machines the ability to learn and improve through experiences – without the need to program them explicitly. ML aims to develop programs that can access data and utilise it to learn for themselves.

Data Science vs Data Analytics vs Machine Learning vs Artificial Intelligence- Careers

AspectsData ScienceData AnalyticsMachine LearningArtificial Intelligence
Job rolesData Engineer, Data Scientist, Data Analyst, Data Architect,Database Administrator, Machine Learning Engineer, Statistician,Business Analyst, Data and Analytics Manager.Sales Analyst, Operations Analyst, Customer Success Analyst, Market Research Analyst, Marketing Analyst, Business Analyst, Financial Analyst, and more.Machine Learning Engineer, Data Architect, Data Scientist, Data Mining Specialist, Cloud Architect, and Cyber Security Analyst, and more.Machine Learning Engineer, Data Scientist,Business Intelligence Developer,Big Data Architect, Research Scientist.
SkillsProgramming Skills.Statistics.Machine Learning. Multivariable Calculus & Linear AlgebraData Visualization & CommunicationSoftware Engineering.Data Intuition.Mathematical skills, Programming languages- SQL, Oracle and Python.Ability to analyse, model and interpret data.
Problem-solving skills.
StatisticsProbabilityData ModelingPrograming SkillsApplying ML Libraries & Algorithms, Software Design, PythonMathematical and Algorithms skills, Probability and Statistics knowledge, Expertise In Programming – Python, C++, R, JavaWell-versed with Unix Tools, Awareness about Advanced Signal Processing Techniques.

Artificial Intelligence vs Machine Learning

  • Artificial intelligence essentially makes machines simulate human intelligence while ML deals with learning from past data without being explicitly programmed.
  • AI focuses on making systems that can solve complex problems while ML aims to make machines learn from available data and generate accurate outputs.
  • Subsets of AI – machine learning and deep learning while a subset of machine learning – deep learning.
  • AI works towards maximizing the chances of success while ML is concerned with understanding patterns and giving accurate results.
  • AI involves the process of learning, reasoning, and self-correction while ML deals with learning and self-correction only when introduced to new data.
  • Artificial Intelligence deals with structured, unstructured, and semi-structured data while Machine learning deals only with structured and semi-structured data.

Data Science vs Artificial Intelligence

  • Data science deals with pre-processing, analysing, visualizing, and predicting the data. Whereas, AI implements a predictive model used for forecasting future events.
  • Data science banks on statistical techniques while AI leverages computer algorithms.
  • The tools used in data science are much more in quantity than the ones used in AI. The reason for this is – there are multiple steps for analyzing data and extracting insights from it.
  • In data science, the focus remains on building models that use statistical insights, whereas, for AI, the aim is to build models that can emulate human intelligence.
  • Data science strives to find hidden patterns in the raw and unstructured data while AI is about assigning autonomy to data models.

Data Analytics vs Machine Learning

  • Analytics relies on existing information to find patterns that ultimately shape decisions. Whereas machine learning leverages existing data that provides the base for the machine to learn for itself.
  • Analytics reveals patterns through the process of classification and analysis while ML uses the algorithms to do the same as analytics but in addition, learns from the collected data.
  • Data analytics ultimately aims to find patterns whereas ML aims to learn from data and make estimates and predictions.

What Does an AI Engineer Do?

An AI engineer builds AI models using machine learning algorithms and deep learning neural networks to draw business insights, which can be used to make business decisions that affect the entire organization. These engineers also create weak or strong AIs, depending on what goals they want to achieve. 

AI engineers have a sound understanding of programming, software engineering, and data science. They use different tools and techniques so they can process data, as well as develop and maintain AI systems.

The next section of How to become an AI Engineer focuses on the responsibilities of an AI engineer.

Responsibilities of an AI Engineer

As an AI engineer, you need to perform certain tasks, such as develop, test, and deploy AI models through programming algorithms like random forest, logistic regression, linear regression, and so on.

Responsibilities include: 

  • Convert the machine learning models into application program interfaces (APIs) so that other applications can use it
  • Build AI models from scratch and help the different components of the organization (such as product managers and stakeholders) understand what results they gain from the model
  • Build data ingestion and data transformation infrastructure
  • Automate infrastructure that the data science team uses
  • Perform statistical analysis and tune the results so that the organization can make better-informed decisions
  • Set up and manage AI development and product infrastructure
  • Be a good team player, as coordinating with others is a must

Skills Required to Become an AI Engineer

Professionals who are finding how to become an AI engineer should also know about the skills required in this field. Some of them include:

Technical Skills

  • Programming Skills 

The first skill required to become an AI engineer is programming. To become well-versed in AI, it’s crucial to learn programming languages, such as Python, R, Java, and C++ to build and implement models. 

  • Linear Algebra, Probability, and Statistics 

To understand and implement different AI models—such as Hidden Markov models, Naive Bayes, Gaussian mixture models, and linear discriminant analysis—you must have detailed knowledge of linear algebra, probability, and statistics.

  • Spark and Big Data Technologies 

AI engineers work with large volumes of data, which could be streaming or real-time production-level data in terabytes or petabytes. For such data, these engineers need to know about Spark and other big data technologies to make sense of it. Along with Apache Spark, one can also use other big data technologies, such as Hadoop, Cassandra, and MongoDB.

  • Algorithms and Frameworks 

Understanding how machine learning algorithms like linear regression, KNN, Naive Bayes, Support Vector Machine, and others work will help you implement machine learning models with ease. Additionally, to build AI models with unstructured data, you should understand deep learning algorithms (like a convolutional neural network, recurrent neural network, and generative adversarial network) and implement them using a framework. Some of the frameworks used in artificial intelligence are PyTorch, Theano, TensorFlow, and Caffe.

Non-Technical Skills

  • Communication and Problem-Solving Skills 

AI engineers need to communicate correctly to pitch their products and ideas to stakeholders. They should also have excellent problem-solving skills to resolve obstacles for decision making and drawing helpful business insights.

Let us explore the career and roles in AI in the next section of the How to become an AI Engineer article.

Few ways / roles to Build a Career in AI

Since several industries around the world use AI to some degree or the other, including healthcare and education, there has been exponential growth in the career opportunities within the field of AI. Some of these job roles are:

AI Developer 

An AI developer works closely with electrical engineers and develops software to create artificially intelligent robots.

AI Architect 

AI architects work closely with clients to provide constructive business and system integration services. They also create and maintain the entire architecture.

Machine Learning Engineer 

Machine learning engineers build predictive models using vast volumes of data. They have in-depth knowledge of machine learning algorithms, deep learning algorithms, and deep learning frameworks.

Data Scientists 

Data scientists collect, clean, analyze, and interpret large and complex datasets by leveraging both machine learning and predictive analytics.

Business Intelligence Developer 

They’re responsible for designing, modeling, and analyzing complex data to identify the business and market trends.


Robotic Process Automation (RPA)

Robotic Process Automation, or RPA, is another technology that is automating jobs. RPA is a software robot that mimics human actions, whereas artificial intelligence is the simulation of human intelligence using computer software.

Robotic process automation (RPA) is a software technology that makes it easy to build, deploy, and manage software robots that emulate humans actions interacting with digital systems and software.

General functions of RPA include:

  • Opening different applications like emails, moving files, etc.
  • Integration with the existing tools.
  • Collecting data from different web portals.
  • Processing data which includes calculations, data extraction, etc.

Some key Points to consider while selecting the RPA tool:

  • Platform independence
  • User-friendliness
  • Cost
  • Scalability
  • Industry-specific
  • Maintenance and support services by a company
  • Tool Smartness: It should act as an end-user.

Few Top Use cases of RPA applications

1. Customer Service

RPA automation changes how businesses can deal with their customers. Namely, it makes ticket resolution significantly easier and faster.

A common example of this is using RPA for automation of customer interactions.

90% of employees are burdened with menial tasks which can be easily automated, which essentially translates to a lot of lost time and money

Rather than having an employee manually go through the timely process of seeking information for customers on their own, businesses are more frequently using RPA to automate this process.

Companies use RPA to automate every step of validating the data associated with a customer’s profile and integrates the data sets necessary, removing the need for a human worker to switch between applications.

Customer service RPA in action

A company is receiving a stream of complaints, but until now has been dealing with them manually, one at a time, with a human agent.

They install an RPA bot, which is programmed to sort and process the information gathered from the complaints into determined categories.

If complaints fall into categories that have pre-determined courses of action, those actions can then be undertaken by the bot.

These are all actions that would otherwise be performed by a human, but RPA allows for quicker resolution.

2. Invoice Processing

Financial processes are crucially important to the everyday functions of any company.

Invoices can be difficult be process. When dealt with manually, people can expect to deal with file formats that are disparate, awkward email attachments to deal with—these routine tasks take up a lot of time and energy.

While larger enterprises have already been pouring hundreds of millions of dollars into RPA and automation strategies for a number of years now, many SMBs have had to wait patiently for the technology to become an affordable and viable option

An automated RPA bot can help by recognizing files and dissecting data from them without any human input.

Invoicing would be considered a prime candidate for automation. This is because invoicing by it’s nature is a rules-based process; it’s simply reporting a state of affairs to the customer, which makes it a perfect job for an unattended RPA bot.

Invoice RPA in action

The way it works is that a bot is programmed to check on a particular folder. When an invoice file is put into the folder, the bot is alerted, and proceeds to read and document all the relevant necessary information and disseminate it to where it needs to go.

This vastly reduces the time it takes for a customer to receive the information they need, improving their satisfaction. It also removes the necessity for an employee to spend their day performing invoicing tasks that are demonstrably automatable.

3. Boost Productivity

When you’re employing a specialist who excels in their field, and they’re having to spend hours on end inputting or gathering data, that is a waste of your money and their time.

The principal advantage of automation with RPA is that you’re effectively hiring another employee to do that job at a small fraction of the price.

These bots never have to take a break, never make a mistake, and work 24/7.

The effect this has on human workers is substantial.

Newly freed up from manual, monotonous tasks, employees can now spend their time doing a job that needs their specialized skillset.

In a Deloitte survey, they found that 86% of respondents agreed that RPA improved productivity within their workplaces

There is also the additional advantage of making your employees happier and improving their job satisfaction. No one wants to spend their time doing manual data entry, especially not when automation alternatives are readily available.

In these instances, RPA really pays for itself by removing the labor costs of paying an employee to do a task that can be automated, while also giving you the opportunity to redistribute that worker and allowing them to perform a task that needs them more.

4. Employee Onboarding 

Onboarding is often littered with administrative tasks that need completing—often mundane and repetitive.

RPA automation can be used to automatically generate and send offers and trigger an automated workflow when the employee’s account is created.

As most business owners will know, paper is expensive and wasteful.

Decision makers are increasingly keen to implement RPA in their organizations, with up to 40% of larger enterprises adopting some kind of RPA software by next year, up from 10% in 2018

RPA can also be used to reduce the amount of paperwork you process, by having an electronic capture system that removes the need to create paper copies of documents by receiving and dealing with them digitally.

Of course, you’ll additionally make your HR team a lot happier and reduce the time it takes to onboard employees by as much as ten times.

5. Payroll 

Most HR employees understand how tedious and time-consuming payroll processing can be.

Bots can be used extensively to help your payroll team by standardizing your processes.

Instead of studiously looking over spreadsheets and writing out checks by hand, get a system that does it for you.

An automated payroll can be designed to do a large swathe of payments, like salaries, overtime, commissions, bonuses, raises, wage deductions, and expenses.

Your HR staff will be better served with an automation system to help them so they can deal with tasks that need them.

79% of the respondents in an HR survey about payroll indicate that they are not familiar with RPA, or RPA is not part of their current strategy.

Payroll RPA in action

RPA can be used for a variety of payroll-related purposes.

Attendance management, which can automatically track employee hours and adjust earnings accordingly with no human input necessary.

It’s estimated that a company of 50 employees can stand to save at least $20,000 a year by implementing RPA for their time and attendance systems.

In addition to this, you have automated payroll administration, processing, deductions, and more.

Many of these processes are considered low-hanging fruit—in other words, common, automatable processes in a workflow which can be resolved relatively easy and the benefits of which are realized quickly by the business.

6. Storing Information

Big data is a huge aspect of modern business.

SMBs preside over more data now than ever before, and the volume of data is growing exponentially.

Because of this, being able to effectively sort through your mountains of data is vital.

RPA is invaluable to organizations because it can sift through large amounts of data tirelessly, with no errors, and around-the-clock.

This is where RPA automation is at its best—it can streamline the process that would take a human a lot longer and allow them to do something that needs a human touch.

Automation like this is most successful in situations where the task is predictable and manual, as bots can be specifically assigned to do that one simple task.

As the amount of data within SMBs increases over time, which it will, businesses will need to utilize methods such as RPA which will help organize this influx of information and store it in a central repository quickly and without the chance for error that is common with humans.

Data governance is a key challenge that businesses want to solve, with 80% of data analytics and big data leaders in a survey responding that it was very important to them.

This use case for the exchange of data is also useful for the internal IT department, as RPA can help in improving the monitoring of network devices.

For example, for a business with a large amount of remote workers—a common occurrence today—process automation will feed and store relevant information and flag any concerns that IT should be made aware of.

This makes troubleshooting for tens, sometimes hundreds of devices, a much more simple and effective task than previously possible. RPA use among mobile device management (MDM) solutions can help enormously where batch processing is necessary for large data sets.

7. Analytics

Removing the human element from data gathering is also important when it comes to providing you with data analytics and insights.

The amount of data in business provides great opportunities for valuable insights for what can be improved within your processes.

For decision makers, quality data is an important tool. Unfortunately, because of human error, these business leaders can have to deal with data that is of poor quality.

With RPA, not only can they tirelessly go through great amounts of data, but they can be programmed to spot information that is useful to humans.

Bots excel at this, and it ensures that the data you have is high quality and defined by the rules that you yourself set—meaning you only get exactly what you need.

So, as far as trawling large data sets goes, RPA is completely unparalleled in performing these tasks.

And this is no small thing either—companies rely on data more now than they ever have done, whether it’s for market research or assessing the performance of their employees.


Augmented Reality & Virtual Reality

Virtual Reality:  is the term used to describe a three dimensional computer generated environment, which can be explored and interacted by a person.

Augmented Reality: is where we overlay the real world with virtual 3D graphical digital objects and interfaces. (It’s augmenting the real world)

5 Top Augmented Reality Tools for App Development

1. Vuforia

Vuforia AR SDK

Vuforia is an augmented reality SDK that sits near the top of most “Top AR” lists, and with good reason. Vuforia offers a range of products for developing AR experiences, including Vuforia Engine, Studio, and Chalk. The software supports the creation of both marker-based and markerless AR and has several key features that make it one of the best for object recognition and 3D modeling. These features include Ground Plane (for adding content to horizontal surfaces), Visual Camera (expands supported visual sources beyond mobile phones and tablets), and VuMarks (custom markers that can be used in Vuforia face recognition and also encode data).

2. Wikitude

Vikitude AR SDK

Wikitude is another great choice for AR software development. It is a relative newcomer to the market, with the company being founded in 2008, but has already earned a solid reputation. In fact, the debate of Vuforia vs Wikitude has become quite active recently. Wikitude is suitable for developing AR apps for iOS, Android, and Smart Glasses devices. Like most top AR development programs, Wikitude supports a variety of tracking methods and technologies, but also incorporates geolocation, cloud recognition, and distance-based scaling features.

3. ARKit


ARKit is the go-to application for the development of AR software developed by Apple, so it is not possible to use ARKit on android phones. The software relies on camera sensor data and additional data (e.g. from a gyroscope and accelerometer) to detect and analyze users’ surroundings for AR visualization. ARKit also supports fast motion tracking, face tracking, Quick Look (displaying models and scenes that can be moved and scaled easily), and various rendering effects. Apple has worked hard to keep AR easy and understandable for developers, releasing ARKit tutorial materials and updates (ARKit 1.5 , ARKit 2 , ARKit 3) with every new version of iOS.

4. ARCore


ARCore is in many ways an analog of ARKit – another application developed by the creators of a platform for devices using this platform. In this case, the company is Google, and the platform is Android. ARCore comes with the standard bundle of AR features (motion tracking, surface detection, light estimation), as well as several advanced features, such as augmented images (custom responses to specific types of 2D shapes and objects) and multiplayer (rendering of the same 3D objects on different devices simultaneously. It is also worth mentioning that ARCore is compatible with Vuforia, which allows users to make the best of functionality offered by the 2 types of software at once. Additionally, an ARCore Unity pairing is often applied to create efficient Android apps. Google has certain restrictions on which devices can run apps built on the software, and you can check whether a phone/tablet is compatible by looking at the list of ARCore Supported Devices on the Google website.

5. ARToolKit


ARToolKit is an open-source and free-to-use SDK available for AR development for devices on different platforms. Apart from Android and iOS, ARToolKit is used for AR apps on Windows, Linux, and OS X. Initially released in 1999, ARToolKit has undergone various updates. Some of the features included in the latest build are tracking of planar images and simple black squares, natural feature marker generation, real-time speed support, and easy camera calibration. ARToolKit also comes with several optional plugins for development with Unity and OpenSceneGraph.

10 Great Tools for VR Development

1. Unity

Unity is famous for game development, however, it helps you to build VR solutions for many other sectors too. E.g., you can create VR solutions for automotive, transportation, manufacturing, media & entertainment, engineering, construction, etc. with Unity.

2. Amazon Sumerian

Amazon Sumerian is the VR engine from AWS, and you don’t need 3D graphics or VR programming skills to use it. Sumerian works with all popular VR platforms like Oculus Go, Oculus Rift, HTC Vive, HTC Vive Pro, Google Daydream, and Lenovo Mirage, moreover, it works with Android and iOS mobile devices too.

Amazon Sumerian supports various VR use cases like employee education, training simulation, field services productivity, retail & sales, and virtual concierge.

3. Google VR for everyone

Google, the technology giant offers a wide range of VR development tools, and you can use them to create immersive VR experience for your stakeholders. You can access these tools on the Google VR developer portal.

You can use these tools to develop VR apps for multiple platforms, e.g., Unity, Unreal, Android, iOS, and web. To access the guides to develop VR apps for each of these platforms, first navigate to “Choose your development environment” on the Google VR developer portal.

4. Unreal Engine 4 (UE4)

Unreal Engine 4 (UE4) offers a powerful set of VR development tools. With UE4, you can build VR apps that will work on a variety of VR platforms, e.g., Oculus, Sony, Samsung Gear VR, Android, iOS, Google VR, etc.


Well-known to 3D game developers, CRYENGINE is a robust choice for a VR software development tool. You can build virtual reality apps with it that will work with popular VR platforms like Oculus Rift, PlayStation 4, Xbox One, etc.

6. Blender

Blender is an open-source 3D creation suite, and it’s free. At the time of writing, Blender 2.80 is its latest release. The Blender Foundation, an independent organization for public benefit governs the development of Blender.

7. 3ds Max

3ds Max is a popular 3D modeling and rendering software from Autodesk, and you can use it for design visualization, creation of video games, etc. 

8. SketchUp Studio

SketchUp Studio is a powerful 3D modeling tool focused on the construction industry and architecture, and you can use it for virtual reality app development. It’s useful for use cases like architecture, commercial interior design, landscape architecture, residential construction, 3D printing, and urban planning.

9. Maya

Maya is yet VR software development tool from Autodesk. With Maya, you can create 3D animations, motion graphics, and VFX software.

10. Oculus Medium

Oculus, the well-known provider of VR platforms like Oculus Rift S, Oculus Quest, and Oculus Go also offers powerful VR development software, named Medium. It’s a comprehensive tool, which allows you to create 3D assets.

You can sculpt, model, and paint the VR assets you create. Even if you are a beginner, with Oculus Medium you can quickly create an immersive environment.


Everything You need to know in making your career in Mobile Apps

Mobile application development is the process of creating software applications that run on a mobile device, and a typical mobile application utilizes a network connection to work with remote computing resources.

Presently, more than one-third of the human population on Earth has a mobile smart device such as an Android phone, iPhone or iPad. This statistic represents a new way undreamed of few years ago to communicate with new customers / consumers (because we are all consumers in one way or the other)

Since we all use mobile apps at present, it eases my role now and wouldn’t spend much time of what and how a mobile app works.

Top Programming Languages for Android App Development

While Kotlin is the official language for Android, there are many other languages that can be used for Android App Development. Details about these are provided below to help you make an informed decision. 

1. Java

Firstly Java was the official language for Android App Development (but now it was replaced by Kotlin) and consequently, it is the most used language as well. Many of the apps in the Play Store are built with Java, and it is also the most supported language by Google. In addition to all this, Java has a great online community for support in case of any problems (And trust me, there will be problems!). 

However, Java is a complicated language for a beginner to use as it contains complex topics like constructors, null pointer exceptions, concurrency, checked exceptions, etc. Also, The Android Software Development Kit (SDK) increases the complexity to a new level! 

All in all, Java is a great language to experience the full joys of Android App Development. However, it may be a little complex for beginners who would prefer to start with something easier and then return to it. 

2. Kotlin

Now Kotlin is the official language for Android App Development declared by Google in 2019. Kotlin is a cross-platform programming language that may be used as an alternative to Java for Android App Development. It has also introduced as a secondary “official” Java language in 2017. Kotlin can interoperate with Java and it runs on the Java Virtual Machine. 

The only sizable difference is that Kotlin removes the superfluous features of Java such as null pointer exceptions. It also removes the necessity of ending every line with a semicolon. In short, Kotlin is much simpler for beginners to try as compared to Java and it can also be used as an “entry point” for Android App Development. 

3. C++

C++ can be used for Android App Development using the Android Native Development Kit(NDK). However, an app cannot be created totally using C++ and the NDK is used to implement parts of the app in C++ native code. This helps in using C++ code libraries for the app as required. 

While C++ is useful for Android App Development in some cases, it is much more difficult to set up and is much less flexible. It may also lead to more bugs because of the increased complexity. So, it is better to use Java as compared to C++ as it does not provide enough gain to offset the efforts required. 

4. C#

C# is quite similar to Java and so it is ideal for Android App Development. Like Java, C# also implements garbage collection so there are fewer chances of memory leaks. And C# also has a cleaner and simpler syntax than Java which makes coding with it comparatively easier. 

Earlier, the biggest drawback of C# was that it could run only on Windows systems as it used the .NET Framework. However, this problem was handled by Xamarin. Android (formerly Mono for Android) is a cross-platform implementation of the Common Language Infrastructure. Now, Xamarin. Android tools can be used to write native Android apps and share the code across multiple platforms. 

5. Python

Python can be used for Android App Development even though Android doesn’t support native Python development. This can be done using various tools that convert the Python apps into Android Packages that can run on Android devices. 

An example of this is Kivy which is an open-source Python library used for developing mobile apps. It supports Android and also encourages rapid app development (which is a win-win situation according to me!). However, a downside to this is that there won’t be native benefits for Kivy as it isn’t natively supported. 

6. HTML, CSS, JavaScript

Android apps can be created using HTML, CSS, and JavaScript using the Adobe PhoneGap framework that is powered by Apache Cordova. The PhoneGap framework basically allows the usage of web development skills to create hybrid apps that are shown through “WebView” but are packaged like an app. 

While the Adobe PhoneGap framework is enough for basic tasks in the realm of Android App Development, it hardly requires much programming except for JavaScript. And since it needs a lot of work to even create a decent app, it is better to use the other languages in this list if you want to be called a true Android developer(Yes…That’s a thing!). But if you are comfortable with Javascript, you can learn React Native, which is an open-source framework that is now highly in demand. You can develop beautiful and powerful  Hybrid apps with react native means your app will be on android and ios both. Hybrid app development is getting so much popular so learning react-native can help you make your carrier in Software development.

7. Dart 

Ignoring Dart as a programming language in today’s context would be like ignoring the gorilla in the room (because the elephant is java). Dart is an open-source programming language that powers the Flutter framework, which is getting a lot of traction these days because of its ability to deliver beautiful and performance apps for the web, desktop, and mobile in lesser time. The key selling point of dart is that it is designed by Google as a client-optimized language for fast apps on any platform. Dart mainly focuses on making UI development easier for developers with features such as hot-reload, which lets developers see changes instantly while working on the app. Dart is also known for its fast performance, it compiles to ARM and x64 machine code for mobile, desktop, and backend. And to JavaScript for the web apps.

We have one more programming language for Android App Development i.e.


Corona is a software development kit that can be used for developing Android apps using Lua. It has two operational modes, namely Corona Simulator and Corona Native. The Corona Simulator is used to build apps directly while the Corona Native is used to integrate the Lua code with an Android Studio project to build an app using native features. 

While Lua is a little limited as compared to Java, it is also much simpler and has an easier learning curve. Moreover, there are build monetization features as well as various assets and plugins that enrich the app development experience. Corona is mostly used to create graphics applications and games but is by no means limited to that. 

Top Programming Languages For iOS App Development

Top 6 programming languages for iOS app development. Let’s have a look at them one-by-one. 

1. Swift

Since its release, Swift has become one of the most popular languages used for crafting iOS, iPadOS, macOS, WatchOS, itvOS. It was introduced in 2014 by Apple as a substitute for Objective C and ever since it has been highly used by iOS developers. According to the PYPL Index (Popularity of Programming Language), Swift ranks at number 8 worldwide in terms of popularity and it captures a market share of about 2.03%. The reason that makes it so popular for its three major characteristics:

  • Speed: Apple introduced Swift as a replacement for Objective-C, C, C++, and if you’ll compare them Swift is way too fast and smooth and throws astonishing performance for most of the tasks.
  • Security: Working on swift is much cleaner and safer as compared to others. If you’re working for a project then safety is the biggest concern and keeping this in mind swift cannot be “nil” by default and its compiler will terminate you from using “nil” and will prevent your app from any unwanted crashes.
  • First Language choice: If you’re at a beginner level, swift can be a good choice for you as it offers free tutorials for developers in a very interactive way and is available for an iPad currently named Swift Playground.

2. C#

It was introduced by Microsoft in 2000 as a standing competitor of Java. It’s an object-oriented programming language that is integrated with the .Net framework. Over the period of time, C# has gained popularity due to its simple and high-level architecture and has been ranked 7th ranking globally, and has been used by 31.4% of programmers today. Besides this, there are certain factors that will let you decide more about this programming language.

  • Open Source: Due to its demand among developers it became open-source in 2014
  • Responsive: When it comes to execution speed, C# delivers extensive fast response timing during compilation
  • Library: C# offers an extensive range of libraries with pre-defined functions which makes it faster compared to others.

3. Objective-C

Objective-c was introduced back in 1984 by Tom Love and Brad Cox with an aim to provide an extension of OOPS along with similar properties. Today, it is one of the best solutions for iOS app development and has been actively used among developers way before Swift was introduced. Besides this, just because there is a lot more potential in the iOS market in monetary terms, an Objective can likewise be implemented while developing an iOS app after taking several things into consideration. 

  • Derived from C/C++: The very fundamental of using Objective C is that it is being derived from C/C++ which enables it to compile any C program using its compiler and you can also add C language code within an Objective-C class which makes it simple, flexible, and scalable for mobile app development. 
  • Community Support: It’s possible that you’re working on any language and there can be sometimes when you’re stuck somewhere and cope with such a situation. Since Objective-C came into existence way before swift language, it offers extensive support of the forum,  and every day thousands of queries are being posted and you get instant support for your query. 
  • Easy to Go: Due to its simplicity and superior run time, Objective-C is one of the most basic programming languages today especially if you’re using powerful SDKs then Objective-c is the right choice. 

4. Flutter

What if we say that you get a chance to pick a language that is extremely fast and can fit in today’s digital era. Yes, Flutter offers a fast experience and instant reload which enables the user to view all the recent changes without losing any relevant data. Those who are looking to get into iOS app development might consider learning this comprehensive app development kit a.k.a SDK which was introduced by Google in 2015 and was launched in 2017.

There has to be a solid reason while using Flutter, have a look at them:

  • Widgets: Flutter offers a wide range of ready-to-go widgets which allows developers to implement them while developing any iOS application.
  • Implementation Flexibility: Flutters offers any user to use any complex implementation via custom UI which can be deployed cross-platform. It enables developers to save time and effort.
  • Less Code: Google introduced Flutter in 2017 with the concept of saving time and business manpower. Flutter was prepared on Dart programming language which uses JIT and AOT which enables it to save extra time. Not only this, flutter enables UI to refresh without making any unwanted changes.

5. React Native

React Native is widely used to create apps for iOS platforms. It’s offered by Meta and has a large community base. It also allows you to deploy your app to the Android platform. Besides this, being one of the most easy-to-learn programming languages makes it the first choice for developers who wish to become an ios app developers. Because it uses well-known javascript and typescript. So developers who are already familiar with these programming languages can easily shift to React Native to build mobile apps. You can build beautiful iOS apps using React Native.

React Native comes with many advantages:

  • Large Community base: Whenever you are stuck somewhere, you can find solutions online. 
  • Reusable: In React Native, everything is a component which makes it reusable.
  • Hot Reloading: It allows you to see your changes in real-time. You don’t need to compile your app again and again just to see small changes.

6. HTML5

Last on this list is HTML5 (HyperText Markup Language) which holds accountability for about 89% of the websites as of now. It is used to create applications by adjoining CSS & Jscript. Today, small-scale startups are using this language as it does not consume time and is also considered a highly cost-effective toolkit. Being a beginner, you can also start with HTML5 for learning and developing iOS apps development. However, before you jump to its conclusion, you might check out some of these highlighters beforehand.

  • Limitations: As I’ve mentioned earlier, it can be a good fit for beginners so it comes up with limited functionality and can be trouble while working on complex items.
  • Popularity: As per a survey by Stackflow, it is considered the 2nd highly used language by developers globally.
  • Accessibility: If you’re looking for technology that can create applications in less time and can be accessible offline then HTML5 is for you.
  • Compatibility: Although HTML is highly used among developers but still cannot be a perfect choice due to its compatibility issues with old browsers and also HTML5 support to build Native apps. This makes it issues with flexibility and with limited functionality with custom designing.

App Certifications That Convey Talent, Experience and Reliability

Here are a few certifications you should look for when hiring an app developer:

  • Android Certified App Developer;
  • MCSD: Windows Store Apps;
  • MTA Mobile Development Fundamentals;
  • MTA Developer;
  • Oracle Java ME Mobile Application Developer;
  • Mobile Development Institute (MDI); and
  • And Java Technology for Wireless Industry (JTWI).