8 Industries Embrace Blockchain Innovation Sumeet Shah February 9, 2023

What is blockchain?

  • A blockchain is a distributed database that manages and stores data. Blockchain technology does not have a centralized control or database. It stores information in blocks that hold data and are linked to previously filled blocks, creating a data blockchain.
  • There are mainly two types of blockchain systems – open & closed. Open blockchains allow the general public or huge audiences to access all the ledger data. And is also known as the ‘public’ blockchain. Closed blockchains are designed to restrict who can see the information stored within them. It is also known as a ‘private’ blockchain.
  • When people use a blockchain-based app like cryptocurrency, they all work with the same database stored on many computers called “nodes”. Specific blockchain procedures are used to confirm these transactions to ensure everything is accurate and secure. 
  • Bitcoin is the best example of a decentralized digital currency that records and verifies transactions using blockchain technology. Did you know that the first Bitcoin transaction in real life cost 10,000 Bitcoin in exchange for two pizzas?
  • 90% of US and European banks and financial institutions have started exploring blockchain technology. Walmart, Pfizer, AIG, Siemens, Unilever, and a plethora of other companies have already adopted blockchain.
  • So far, over 400 applications have been developed on the blockchain platform, including identity management, SCM, and gaming. The community also offers several tools for customizing blockchain implementations for various decentralized use cases in SCM, healthcare, and DeFi.

8 most popular blockchain applications of 2023

1. Gaming

The video game industry is quickly adopting blockchain technology, specifically through non-fungible tokens (NFTs). In-game economies and unique items are frequently used in games, and NFTs provide a way to facilitate that economy while also ensuring that players’ items are accessible across games. GameFi is the best example of blockchain integration within the gaming industry. GameFi is a combination of gaming and decentralized finance (DeFi) that describes integrating blockchain applications in the gaming sector, other than, say, for monetization purposes. Small-scale publishers, in particular, take advantage of the ability to monetize their games by issuing such non-fungible tokens.

2. Manufacturing

Blockchain technology has several potential applications in the manufacturing industry, from supply chain management to quality control and compliance. One practical example of how blockchain is used in manufacturing is tracking product and component authenticity. By capturing each step of a product’s journey through the supply chain (on a blockchain), manufacturers can ensure that the products they receive are genuine and not counterfeit. They can better track the movement of goods and optimize their operations by creating a decentralized, secure ledger of supply chain data. Also, the combined power of blockchain and IoT can transform how businesses manage their products in this industry. With these two game-changing technologies, companies can unlock many benefits, including enhanced product safety, streamlined tracking capabilities, and more efficient warranty management. Industry analysts estimate that nearly 30% of manufacturers (with a revenue of over $5 billion) will incorporate blockchain into their industry 4.0 projects by 2023. 

3. Supply chains

Using blockchain technology to track items moving through a logistics or supply chain network can benefit greatly. For starters, it facilitates partner communication because data is available on a secure public ledger. Second, because the data on the blockchain cannot be altered, it provides greater security and data integrity. As a result, logistics and supply chain partners can collaborate more easily, with greater confidence that the data is accurate and up-to-date. IBM Blockchain is used in the supply chain industry to improve supply chain operations’ transparency, traceability, quality control, provenance, payment processing and efficiency.

4. Finance

The world of finance has always been driven by innovation, from the invention of paper money to the advent of credit cards. However, despite all these advances, the system still has many inefficiencies, particularly regarding settlement and clearing times. In the stock trading industry, for example, it can take up to 3 days (or longer if trading internationally) for the settlement and clearing process to complete, which means that the money and shares are essentially frozen. This can incur high costs and risks for banks, given the magnitude of the sums involved. This is where decentralized finance (DeFi) comes in. Using secure distributed ledgers similar to the ones used by cryptocurrencies, DeFi represents a new and exciting way of conducting financial transactions. With DeFi, settlement and clearing times are reduced to near-instant speeds, meaning that money and shares can be transferred in a matter of seconds.

5. Healthcare 

Blockchain’s smart contracts have significant potential to impact the healthcare industry. The blockchain platform codes these contracts to execute automatically when certain conditions are met, allowing parties to create contracts without intermediaries. They can also encode wearable health records to ensure privacy and security, as only authorized providers with a key can access them. In this way, smart contracts can aid in enforcing the HIPAA Privacy Rule, which is crucial for protecting patient information. Blockchain’s smart contracts represent a new era of innovation for the healthcare industry with the potential to streamline processes and improve patient outcomes.

6. Advertising

The digital advertising industry is currently plagued by issues such as ad fraud, lack of transparency, and a complex supply chain involving multiple intermediaries. Blockchain technology allows advertisers to create a decentralized, transparent, and tamper-proof platform for buying and selling digital ads. For example, the blockchain-based advertising platform, AdEx, uses a smart contract system to automate the buying and selling of ad space. Advertisers can create digital ads stored on the blockchain and set up automated bidding rules that are executed in real-time. Publishers can then monetize their ad space by selling it to advertisers directly, cutting out intermediaries and reducing costs. Eventually, it can also increase trust and confidence in the advertising industry, as advertisers can be confident that their ads are being displayed to real people securely and transparently.

7. Insurance

Traditionally, insurance policies and claims are stored on centralized databases vulnerable to data breaches and hacking. But with blockchain, insurers can create a secure and decentralized record of insurance policies and claims resistant to tampering and fraud. Blockchain can easily streamline the claims processing system, which we all know can be a real headache. The process is usually time-consuming and involves multiple parties. But with blockchain, the shared and transparent ledger allows all parties to access real-time information, speeding up the process and reducing errors. Enterprises can also leverage blockchain for risk management to analyze data from various sources and create predictive models that help in risk management. Insurance companies might anticipate a breakthrough from Ethereum’s smart contracts and decentralized apps in the forthcoming years.

8. Cyber Security

Blockchain has the potential to enhance cyber security in many ways, including the secure storage and transmission of data, verification of identities, and protection against fraudulent activity. It can generate a decentralized identity management system that eliminates the need for a central authority to verify identities. This ensures that users have control over their data and can securely share it with only those they trust. Utilizing a distributed ledger can render it extremely difficult for a hacker to manipulate any information kept on the blockchain. One of the best examples is DNS. The Domain Name System (DNS) is a public directory that connects domain names to their IP addresses. Over time, hackers have attempted to gain access to the DNS and exploit these links, resulting in site crashes. But with blockchain’s immutability and decentralized systems, the DNS can be securely stored.


Despite being a relatively new technology, blockchain has already shown great potential in revolutionizing businesses’ operations. In the last 15 years, companies have experimented with blockchain in various ways, yet the possibilities for its application are still being explored. As the world increasingly relies on digital technology, there is a growing demand for secure, transparent, and tamper-proof data storage and sharing. Blockchain technology has emerged as a promising solution to meet these needs. Its ability to ensure data integrity, establish trust, and facilitate decentralized transactions can transform industries ranging from finance and cyber security to supply chain management and more.

Python vs. SQL: What is the difference? Sumeet Shah January 25, 2023

What is Python?

Python is a programming language that people sweepingly use in various fields, including data analysis, machine learning, and artificial intelligence. It is known for being beginner-friendly and has a wide range of applications, including scripting and automating tasks. Python is often used to integrate different systems and can help programmers work more efficiently.

Python has become a popular choice for developers due to its many useful features, including its effective typing system, built-in data structures, and extensive library of powerful frameworks. Python’s syntax, which focuses on readability, makes it simple for a programmer to code with fewer lines and errors. Moreover, it has a sizable and active user base, so many resources are accessible to individuals who are learning the language or need assistance with a particular project. Several versions or “variants” of Python are available, each optimized for different use cases and applications. These include CPython, JPython, IronPython, Brython, RubyPython, PyPy, and MicroPython. 


What is SQL?

Structured Query Language, or SQL, is a computer language that allows developers to manage and retrieve information from databases and create their own. Many industries store data in relational databases, which organize and link data using tables, columns, and rows. SQL is the most commonly used language for creating and maintaining these databases. It can generate quick insights, analyze data, and retrieve records from large databases. But what does a database imply? A database is a structured record of data created for easy access, storage, and retrieval of both old and new data and access to them in various ways. They provide a foundation for websites, applications, and enterprise software packages. Many popular database management systems, such as MySQL, Oracle, and Microsoft SQL Server, use SQL as their primary language for interacting with databases.


The Key Differences between Python and SQL

  1. IT companies commonly use Python for creating various types of applications. In contrast, companies use SQL for managing and retrieving data from databases.
  2. Python and SQL have very different syntaxes. Python is an object-oriented language with a syntax familiar to programmers from other languages like C++ or Java. SQL has a more declarative syntax, which means you specify what you want to do rather than how to do it.
  3. Python can create a range of sophisticated computer programs and applications, including consumer applications, corporate tools, and industrial automation systems. SQL is used mainly for creating and managing databases that store and retrieve electronic data.
  4. Python and SQL have functions but serve different purposes. Python functions are blocks of code that can be called multiple times, with or without parameters. SQL functions are pre-defined operations that can be used to manipulate data in the database.
  5. The popularity of Python has been increasing in recent years as many companies are transitioning from older programming languages to Python. On the other hand, SQL is a widely used tool for managing data in business and is essential for the growth of internet-based companies.


Which language should you use first: Python or SQL?

Python is a general-purpose programming language, while SQL is a specialized language for working with databases. In general, it’s a good idea to learn both languages, as they are both very useful and are frequently used together. Python is a great language for prototyping and building out the logic of a program, while SQL is great for storing and querying data. SQL allows you to connect (or merge) multiple data sets as a developer. Python is particularly akin to structured (tabular) data that can be obtained using SQL but requires additional manipulation. Deciding which language to learn first depends on your goals and interests. Mastering both languages might just be a feather in your cap.


Which language is more popular: Python or SQL?

Python VS SQL graphSQL is a programming language used to manage and manipulate databases by performing basic functions such as creating, reading, updating, and deleting data. Many businesses use SQL to extract valuable insights from large amounts of data. MySQL is a specific type of SQL database that consists of related tables. It is pronounced “My Sequel” and is the default configuration for WordPress installations. MySQL allows for up to 64 indexes per table, each of which can have between 1 and 16 columns and has a maximum index size of 1000 bytes. The above graph displays that SQL experienced success in 2013 but has since experienced fluctuations, with a gradual decrease starting in 2019. Its popularity, compared to Python, has been declining over time.

Python is the world’s fastest-growing and most-liked programming language. It is evident from the graph that Python’s popularity has surged explicitly among developers over recent years. From 2013 to 2015, Python’s popularity and usage among developers were close to nil, with only 20-23% to its credit. However, there is an exponential increase in its use of nearly 30% until 2023. Thus, the graph shows that Python’s rise as a prominent language compared to SQL is accelerating day by day. It also proves that it is the most widely used and known programming language in the business world right now.


SQL is an open-source relational database management system that anyone can download and use, from a beginner to a highly experienced data scientist conducting research.

Python is a computing language for web development, data analysis, prototyping, and other technical tasks. It is known for its high-level data structures and dynamic typing, which can streamline development. On top of that, it is often used with cutting-edge technology and can reduce the maintenance cost of programs.


Can you use Node.js for Mobile App Development? Sumeet Shah December 1, 2022

What is Node.js?

  • It is a free, open-source, cross-platform JavaScript runtime environment. It is a full-stack development environment that divides tasks into fully separate nodes. Node.js has a large and active community, with many open-source libraries and frameworks available that can be used to simplify and accelerate development. This makes it an attractive platform for building web applications, especially for smaller teams or those with limited resources.
  • JavaScript is used to write Node. JavaScript is a popular programming language that almost every developer is familiar with. As a result, even for a novice JavaScript programmer, learning Node.js requires less effort and time.
  • Nodes make this environment useful to programmers, whether you’re developing iPad, iPhone, or Android apps. Because each task is divided into independent, separate node paths, tasks can run concurrently and seamlessly without clogging the server or consuming too much capacity.
  • Node.js also enables developers to share data between the server-side and client-side easily.  It is a popular platform for developing fast and scalable mobile and web applications.


Node.js frameworks to use for mobile app development:

1. Ionic

One of the cross-platform app development frameworks for Node.js is Ionic. The framework mainly focuses on building hybrid mobile apps. The Ionic framework requires the developer to create apps using HTML, CSS, and JavaScript. Additionally, this framework supports SaaS and AngularJS for developers. Developers can employ this framework to add UI elements to their applications. The Ionic framework is adaptable and can be used for iOS and Android. Officially, the Ionic framework integrates with Angular, but it also offers support for the frameworks Vue.js and React.js. Ionic also provides several independent yet robust methods for creating mobile applications that outperform current HTML5 development frameworks.

2. Xamarin

Xamarin apps are native apps! The apps will operate in the manner that people anticipate, whether it’s designed in a consistent UI across platforms or creating a native user interface. To construct a mobile application, Xamarin employs C# and .NET. It is a free, open-source application development platform for creating Node.js-based Android and iOS apps. With more than 60k community members, Xamarin is a member of the .NET dynasty, which continuously improves its framework. When it comes to creating multi-platform apps, Xamarin is typically used. It allows developers to write a single piece of code once, then utilize it across other platforms. CA Mobile for mobile banking and Novarum DX (medical app) are the two popular application examples with Xamarin.

3. React Native

React Native is one of the best solutions for developing mobile Node.js apps. It is among the largest and most popular GitHub projects of all time. Therefore, even if you run into issues with the framework, they may be simply fixed. The framework enables the development of successful iOS and Android app versions using the same codebase. React components incorporate pre-existing native code and communicate with native APIs using JavaScript and the declarative UI paradigm of React. As a result, completely new teams of developers can create native apps and work more quickly. Well-known apps like Uber Eats, Discord, and even Airbnb use the React Native framework for creating and managing mobile applications.

4. Mobile Angular UI

It is employed for creating hybrid Node.js applications. Making and dividing the sections on your smartphone screen uses this open-source framework. A front-end framework called AngularJS is quickly rising in popularity. The developer community that uses it commends its capabilities and versatility. This makes it the ideal answer to the problems associated with developing mobile applications. The AngularUI team seized the chance to design a framework to aid programmers in producing outstanding mobile applications. It is designed specifically for mobile apps and allows for simple app navigation with scrolling that maintains the header and footer positions. It offers interactive UI capabilities, including switches, modals, sidebars, navbars, and overlays.


Is Node.js good for mobile apps?

  1. It is a server-side engine based on JavaScript that allows it to easily scale to accommodate its massive growth.
  2. It is a much better choice for high-performance, real-time apps and is used for everything from website to mobile app development.
  3. The Netflix team chose Node.js to create a fast, lightweight, and modular application. As a result, their new app’s startup time was reduced by 70%.
  4. Node.js developers can constantly publish and deploy new code since programs can be examined in place, and faults can be fixed without needing to restart. Additionally, an active open-source community continuously improves the technology, making it better almost on its own. It also implies that app upgrades can be completed as needed.


Did you know?

  1. As of January 2021, it is the 8th most popular language on GitHub, according to the number of pull requests.
  2. Currently, Netflix uses Node.js to manage the numerous requests it gets every day. Making it a network application that is always scalable.
  3. According to the 2019 Stack Overflow Developer Survey, Node.js was the third most popular technology in the “Frameworks, Libraries, and Other Technologies” category, and it has consistently ranked in the top ten in this category in previous years.


Summing it up:

Over time, the Node.js framework has significantly increased the accessibility of developing mobile applications. Node.js framework and its application have significantly helped enterprises to grow in the market. It has made it easier to develop high-performance applications with considerable user interface flexibility.

Python for Mobile App Development Sumeet Shah November 23, 2022

What is Python?

Python is a major scripting language that includes compelling semantic features. Its clear, easy-to-learn syntax emphasizes readability, lowering the program’s maintenance costs. And the syntax is nearly identical to that of English. Guido van Rossum, a Dutch programmer, designed Python and released it in 1991. Python 2 is the most extensively used as compared to its latest version, Python 3. The language codes are written in a text editor or an integrated development environment, such as Thonny, Netbeans, or Pycharm (computer programs). So what does Python do?

  1. It can handle massive data and perform complex calculations
  2. It can connect to database systems and read or modify the files
  3. When used alongside a software application, it can create workflows
  4. If used on a server, it establishes web applications
  5. It is also used in the development of software prototypes

Did you know: Python has been a crucial part of Google since its establishment as a search engine?


Why use Python coding in mobile app development?

Python is an outstanding language for mobile app development because:

  1. It is straightforward, easy to read and write, and powerful when solving complex problems. 
  2. Its huge standard library allows you to integrate the language with third-party APIs or modules.
  3. It can be used on various platforms, including iOS, Windows phones, and desktop computers.
  4. It also works on Windows, Linux, Macintosh, and Unix and has been ported to Java and virtual machines (.NET)
  5. Although Android does not support native Python, it can be used in mobile app development. It is possible by using various tools that convert Python apps into Android Packages that run on Android devices.
  6. Python frameworks aid in the development of mobile apps in a shorter time.
  7. You only need a capable device, such as a laptop/desktop computer running Ubuntu, macOS, or Fedora. Alternatively, you can use mobile phones, such as the Galaxy S4, Nexus 5, etc., for Python coding.

Did you know?: Python is used in developing applications like YouTube, Instagram, Spotify, Quora, Dropbox, Pinterest, Reddit, and many others.

Frameworks to build mobile apps with Python:

Python does not support the interpreted languages by iOS or Android. In fact, Python apps cannot run natively. And at this point, various frameworks emerge to bridge the gap, allowing these apps to run on mobile devices with a native appearance and functionality. 

The preferred implementation language for Android is always Java. So, to write an Android application in Python, you need a way to run its code on a Java virtual machine. The steps for creating an app for iOS are comparatively more complex than those for Android.

So which frameworks can help you to develop an actual application on your device? Let’s take a look-


VOC (Vestigial Output Compiler) is a component of the BeeWare suite that allows you to access Java native objects and implement Java interfaces in Python classes. It takes Python’s source code, compiles it to CPython bytecode, and then transpile it into Java-compatible bytecode (machine code). The result is that your Python source code files are compiled directly into a Java .class file, which is then packaged into an Android application. It will allow you to write Android applications now to the native API of Android. Once you’ve registered your native Android application, you can use Briefcase for packaging your Python code as an Android application.


It is a tool for integrating Python with Android. Its primary function is to create Android APKs that can be installed or uploaded to marketplaces such as Play Store. The first function of the tool is to compile the Python interpreter, its dependencies, backend libraries, and Python code for Android devices. By totally customizing this stage, you can install whatever components you like. This results in a standalone Android project used for generating any number of different APKs, even with other names, icons, Python code, etc. The second function of Python-for-Android is to give a simple interface to these distributions.


Kivy is a cross-platform framework that lets you create apps and games for Windows, Linux, iOS, and Android. Kivy has an extensive Python library with many features, such as multitouch events and so on. These multitouch gestures aid in developing rich user experiences (UX) with less effort. Kivy also supports the use of native libraries on various platforms. You can directly call functions from its central platform and also access features that Python does not offer.


PyJNIus allows you to create Android apps without any prior knowledge of Java. It gives access to Java classes and methods using Python, making it a powerful tool for mobile app development. Its framework even allows Python to interact with the Android API. Developers use this API to obtain information from various parts of their mobile devices through pause, vibration, etc., functions.

In a nutshell:

Python has become one of the most eminent programming languages. It’s easy to learn, and there are lots of free online tutorials available to learn Python. You can use Python to write programs for almost any application, including web applications, mobile app development, desktop apps, games, scientific computing, and much more.

Is Python worth learning in 2022 Sumeet Shah July 20, 2022

What Is Python Used For? A step-by-step guide for Beginners

What is Python?

Python is a dynamically semantic, interpreted, object-oriented high-level programming language. Python is a general-purpose programming language, which means it can be used for a variety of tasks such as data research, software and web development, automation, and just getting crap done in context. Python’s concise, easy-to-learn syntax prioritizes readability, which lowers software maintenance costs. Modules and packages are supported by Python, which fosters program modularity and code reuse. Guido van Rossum created Python, and it was published in 1991.

What is Python used for?

1. Web Development:

Python is a popular choice for complicated web development projects because of its versatility, which allows for the creation of sophisticated online utilities with ease. Web developers that use it save a lot of time and energy because of its straightforward syntax, which is quite comparable to the English language. Python is a very flexible backend language. Backend or server-side functionality can be handled more easily using Python-based web frameworks like Django. Python’s prominence in web development is largely due to frameworks and libraries such as Django and Flask, which enhance the language’s capabilities.

2. Data Analytics/Data Science:

Python is used to create several of the most popular data mining tools. As a result, it’s a fantastic data science tool. Data scientists and analysts alter data using coding languages like Python and R for reporting, predictive analysis, and other purposes. The Pandas library is a major advance forward from the outdated Excel files that have been used for so long for financial research. NumPy also allows you to perform linear algebra, scientific computing, and a variety of other specialized tasks. Python’s features enable programmers to distinguish between crucial and relevant data. Organizations may learn more about themselves by interpreting big data well.

3. AI and ML:

These days, artificial intelligence and machine learning are buzzwords, but the fact is that it all boils down to algorithms, code, and logic. Machine learning is the process of teaching computers to learn on their own using algorithms that are continually updated based on incoming data. Python is often regarded as the greatest programming language for Artificial Intelligence (AI) due to its clear syntax and ease of learning. Given the diversity and capability, it’s no wonder that some genuinely world-class tools for producing intelligent behavior exist in Python.

4. Financial Analysis:

When recruiting engineers, the most popular programming language considered by FinTech organizations is Python. Python is gaining popularity as a result of its potential financial services. Python’s utility in applications such as data regulation, analytics, risk management, and quantitative rate difficulties is the reason for its appeal. More than only FinTech companies use Python programming. Python is widely used everywhere in the financial industry due to its data processing skills and various third-party financial analysis tools.

5. Scripting:

The script language generates code and is very easy to learn, even for newbies. Python is widely used in programming and is an interpreted language that occurs during runtime. It becomes more flexible and adaptable than many other scripting languages since it translates to code. In a program, a script is used to automate particular operations. It can run on its own and requires less code, but modules in Python are referred to as libraries that cannot run on their own. It must be imported before it may be used. Python is classified as a scripting language since it is an interpreted language and its popularity in scripting relates to its simple scripting interface.

Frequently-asked Question

What is Pandas Python?

Pandas is a widely used open-source Python library for data science, data analysis, and machine learning activities. It is developed on top of NumPy, a library that supports multi-dimensional arrays. It provides quick, simple, and adaptable data structures for working with structured (table form, multivariate, possibly heterogeneous) and time-series data. Pandas is a Python data science module. It works well with many other Python data science modules and is normally included in every Python installation. Among other things, Pandas supports re-indexing, repetition, filtering, data analytics, sequences, and representations.

What is DevOps – Introduction to Architecture & Benefits of DevOps Sumeet Shah June 14, 2022

What is DevOps?

The simplest way to introduce-DevOps is an approach for coalescing Development and Operations, and there could be various other ways to introduce it too. Broadly, it is a philosophy to ensure coordination between the development and the operations team. Facilitated by DevOps, agile development allows seamless integration and delivery. Leading to extremely productive ends, DevOps tools have proven results in increased production speed.

DevOps architecture is utilized for cloud-based applications as well as big distributed applications. Various components that are used in the architecture are: Development-Plan, Test, Code, Build; Operations- Release, Operate, Deploy, Monitor. DevOps allows both the departments to improve their productivity by allowing them to modify their flaws.

A well-implemented DevOps strategy has a lot of advantages. The following are a few of those that we chose to highlight:

1. Speed:

To become more productive at generating organizational outcomes, DevOps enables you to move at a high rate. This allows you to develop quicker for clients, and adapt well to the changing markets. Your development and operations teams can achieve these goals using the DevOps paradigm.

2. Continuous and Rapid Delivery:

Continuous delivery is the foundation of the DevOps. By increasing the number of releases and the frequency with which they are released, you can innovate and upgrade your product more quickly. The faster you deploy new features and solve bugs, the better you’ll be able to respond to your consumers’ needs and gain a competitive edge.

3. Decreased cost:

DevOps allows firms to keep their costs to a minimum, that is a key concern for corporations. These methods promote productivity, resulting in increased software production and lower overall production costs for enterprises.

4. Reliability:

 DevOps helps you sustain competition by maintaining the quality of application updates and infrastructure modifications. You can deliver at a faster rate while still providing a great customer experience. To ensure that each change is functional and safe, it uses approaches such as continuous integration and continuous delivery. Practices such as monitoring and logging allow you to keep track of performance in real time.

5. Improved collaboration:

 Development teams have more agility and produces at a faster pace in isolation. Operations teams find it difficult to keep up and are unable to release software at the same rate. However, DevOps brings these teams together and speeds up the delivery of software. It shortens the development timeline.

6. Customer Satisfaction:

The DevOps approach values user experience and, by design, user feedback. Because you’re focusing on smaller releases and there’s greater room for fast feedback, this naturally puts you in the shoes of the consumer.

When clients’ expectations and requirements are met, customer satisfaction reaches new heights by getting information from them and acting on it.


The easiest way to get started with DevOps is to identify a small value stream and try out some practices. Transforming a single stream with a small number of stakeholders is much easier than trying to move your organization to a new way of working all at once. Now is the best time to explore this area!

Best DevOps Tools to Use in 2022 Sumeet Shah June 10, 2022

What is DevOps? DevOps is a set of tools and practices that bring together software development and operations, bridging the gap between the two. This approach is witnessing a rise in its demand due to provision of high quality software delivery on a continual basis. The aim behind using this approach is to make sure that flaws are reduced to the minimum and productivity is increased to the maximum. 

For this purpose, although there are a number of DevOps tools that are used in the market, few of them are exceptionally good namely, AWS, Jenkins, Terraform, and Kubernetes. These tools automate the process of software development by focusing primarily on collaboration and communication between the professionals with different roles and working in different teams. 

This blog will illustrate the best tools with their respective categories to use for DevOps processes:

1. Containers- Docker:

Containers are software development platforms that enable developers to create, test, and deploy products in resource-independent conditions. Each container contains the whole runtime environment, which includes the software, its modules, source code, settings, and all of its resources. Platforms for containers provide orchestration, automation, security, governance, and other functionalities. For fast application development and deployment, DevOps mainly relies on containerization, with Docker being the most extensively used container technology. The Docker engine was created to make developing, deploying, and managing containerized applications on single nodes easier. Docker is free and open-source software that works with cloud services and runs on Windows and Linux.

2. Infrastructure as Code (IaC)-Terraform:

Hashicorp developed Terraform as an open source tool to automate the provisioning of infrastructure resources. It supports all public and private cloud infrastructure provisioning like Networks, Servers, managed services, and Firewall. Infrastructure as a coding philosophy is used to create, manage, update, and destroy infrastructure resources such as virtual machines, containers, networking, and others. Terraform uses a notion called state files to keep track of the state of your infrastructure. HCL (Hashicorp configuration language) is Terraform’s own domain-specific language.

3. Container Orchestration System – Kubernetes:

Kubernetes is a prominent container orchestration platform and an open-source DevOps solution for automating the deployment and administration of containerized applications. To achieve effectiveness and quality in production, developers use Kubernetes to automate tasks like container initialization, scalability, communication, security, and more. Kubernotes is unique because it has the ability to heal itself. It makes modifications to your application or its setup while also monitoring its health. Rollouts and rollbacks get automated. It also provides a set of Pods with their own IP addresses and a single DNS name for Service Delivery and load balancing. 

4. Continuous Integration/Delivery (CI/CD)- Jenkins:

Jenkins is a Java-based open-source automation platform with plugins designed for Continuous Integration. If you want to integrate a certain tool, you need to install the tool’s plugins. It also enables you to deliver your software on a continuous basis by interacting with a variety of tests and deployment technologies. Jenkins, which was created by Kohsuke for Continuous Integration (CI), is now the most extensively used Continuous Delivery (CD) solution. It has thousand-plus plugins that allow it to integrate with almost any tool. Jenkins continues to gain traction as the most widely used continuous integration and delivery tool in the world.


In order to actualize the benefits of DevOps, choosing the best tools is imperative. Right tools help realize the advantages by breaking down communication silos and improving productivity. It must be noted that it takes time to implement the culture shift and not just a night.

Top 4 Benefits of Data Engineering Sumeet Shah May 31, 2022

Data Engineering’s purpose is to offer an orderly, uniform data flow that enables data-driven models like machine learning models and data analysis. Clive Humby stated, “Data is the new oil.” Unfortunately, many companies have been accumulating data for years but have no idea how to profit from it. What can be accomplished is just unclear. Data Engineering improves the efficiency of data science. If no such domain exists, we will have to devote more time to data analysis in an attempt to address difficult business challenges. 

Let us check out the Top 4 Benefits that Data Engineering offers businesses.

1. Helping Make Better Decisions:

Companies may leverage data-driven insights to better influence their decisions, resulting in improved outcomes. Data engineering allows Identifying types of customers or products that make for more targeted marketing. Your marketing and advertising activities will be more effective as a result of this. For example, a company might simulate changes in price or product offers to see how these affect client demand. Enterprises can utilize sales data on the revised items to gauge the success of the adjustments and display the findings to assist decision-makers in deciding whether to roll the changes out throughout the company. Companies’ managers may comprehend their consumer base using both older and newer technologies, such as business intelligence and machine learning. Furthermore, modern technology allows you to gather and evaluate fresh data on a constant basis to keep your understanding up to date as situations change.

2. Checking the Outcomes of Decisions:

In today’s turbulent marketplace, it’s critical to examine how previous decisions worked. Any time a data-driven decision is taken, additional data is generated. This data should be evaluated on a regular basis to see how new data-driven decisions may be made better. This is where data engineering is incorporated. As a result of the end-to-end perspective and assessment of important decisions, optimal data use will also ensure that continual improvements are implemented on an ongoing basis. You waste less time on decisions that do not fit your audience’s interests when you have a better grasp of what they want. Self-improvement is an ongoing process in data science. This results in reflecting the impact of prior decisions. Without self-reflection, no process is complete. It will be easier to make future decisions now that this has been accomplished.

3. Predicting the User Story to Improve the User Experience:

Products are the lifeblood of every company, and they are frequently the most significant investments they undertake. It would not be wrong to say that data engineering helps identify new scopes. The product management team’s job is to spot patterns that drive the strategic roadmap for new products, services, and innovations. Predictors are one of the most powerful aspects of machine learning. You may use machine-learning algorithms to peek into the future and forecast market behavior based on previous data. Machine-learning algorithms look for patterns that humans can’t see and use them to forecast the future based on historical data. Companies can stay competitive if they can anticipate what the market wants and deliver the product before it is needed. In today’s economy, a company can no longer rely on instinct to be competitive. Organizations may now develop procedures to track consumer feedback, product success, and what their competitors are doing with so much data to work with.

4. New Business Opportunities Identification:

Products are the lifeblood of every company, and they are frequently the most significant investments they undertake. It would not be wrong to say that data engineering helps identify new scopes. The product management team’s job is to spot patterns that drive the strategic roadmap for new products, services, and innovations. Predictors are one of the most powerful aspects of machine learning. You may use machine-learning algorithms to peek into the future and forecast market behavior based on previous data. Machine-learning algorithms look for patterns that humans can’t see and use them to forecast the future based on historical data. Companies can stay competitive if they can anticipate what the market wants and deliver the product before it is needed. In today’s economy, a company can no longer rely on instinct to be competitive. Organizations may now develop procedures to track consumer feedback, product success, and what their competitors are doing with so much data to work with.


It’s an important aspect of implementing data science and analytics successfully. The sorts of tools and technology that are available are changing all the time. As we’ve seen, data engineering is concerned with the tools and technology parts of a data science or analytics project framework. If you’re serious about your software startup being data-centric, the most critical first step is to manage your data platform. Not simply to scale, but also because data security, compliance, and privacy are major problems right now. After all, it’s because of their data that you’ll be able to develop so rapidly, so invest in it first before focusing on analytics.

Introduction to Data Engineering and its Importance Sumeet Shah May 26, 2022

What is Data Engineering?

Data is the new oil’. Heard of it? Well, it’s tough to describe data engineering accurately. Data Engineering is a field that deals with data analysis and tasks such as obtaining and storing data from various sources. It entails planning and building the data infrastructure required to gather, clean, and format data so that it is accessible and usable to end users. This procedure guarantees that data is both valuable and accessible. Data engineering is primarily concerned with the practical applications of data collecting and processing. Data Engineering improves the efficiency of data science. Although it appears to be an easy job, it necessitates a high level of data literacy.

Importance of Data Engineering

Technological advancements have had a significant influence on the vitality of data throughout time. We used to create traditional data warehouses, give BI reporting, and perform upgrades and maintenance on such platforms. In today’s world of innovative startups and businesses, we’re building with new tools for the modern world. Cloud computing, open-source initiatives, and the massive expansion of data are all examples of these breakthroughs.

Data is there at every stage of the route, whether business teams are dealing with sales data or evaluating their lead life cycles. We no longer construct data warehouses. We’re creating data lakes and real-time data streams instead. In the era of big data, more controlled data implies more accurate forecasts. Data engineering is critical because it enables companies to optimize data for usability. There is no data without data engineering. There is no machine learning or AI without data. Data science requires data to run algorithms on.

If you don’t want to fall behind, focus on data engineering today so you can move on to deep analytics and data science before it becomes too late.

Role of Data Engineers

The “engineering” element is the key to knowing what data engineering is. For operational usage, data engineers construct up analytics databases and data pipelines. Much of their work entails preparing large amounts of data and ensuring that data flows are as smooth as possible. These pipelines must collect data from a variety of sources and store it in a single warehouse that represents it consistently as a single source of truth. The objective of data engineers is to ensure that data is not just abundant but also clear. Part of the work entails formatting both structured and unstructured data. Data that is structured can be stored in a database. They require essential abilities such as programming, mathematics, and computer science, as well as experience and soft skills in order to convey data patterns that aid corporate success. Data engineers are in charge of overseeing an organization’s analytics. Your data is given mobility by data engineers.

Data Engineers also handle some of the on-and-off duties, such as:

  1. Interaction with management to have a better understanding of the company’s goals
  2. New data validation procedures and data analysis tools are being developed
  3. Ensuring that data governance and security standards are followed
  4. Identifying ways for increasing data accuracy and efficiency
  5. Discovering tasks that will be automated using the required data


“Without a systematic way to start and keep data clean, bad data will happen.” — Donato Diorio

 It should go without saying that data is useless unless it can be read. As a result, data engineering is the initial stage in turning data into meaningful information. Nearly every goal of the business necessitates the use of data engineering. To prepare and analyze data for future study, data engineers employ a variety of skills and technologies. Analysts and scientists will be unable to access and operate with data without the infrastructure established by data engineers. And as a result, organizations risk losing access to one of their most precious assets. For company scalability, Incentius recognises the necessity of data engineering. That’s why we offer quality Data Engineering services to take your company to the next level.

Why is DevOps Important? Challenges and Benefits of DevOps explained Sumeet Shah May 17, 2022

What is important is to focus on collaboration and reduction in barriers between development and operations. Equipping them with the DevOps tools and methods, they need to collaborate as a single interdisciplinary team that maximizes productivity with Automation, thereby optimizing the entire business process. 

Benefits of DevOps

You have a better chance of outrunning your competitors if you have a shorter time to market. DevOps allows you to deliver software faster because of optimized processes, proper automation, and methodical delivery management. This philosophy also enables the team to actively propose ground-breaking solutions and share their thoughts in actual time. The customer-centric attitude, continuous feedback, shorter product life cycles, and continual improvement lead to the most fulfilling outcomes.

DevOps does seem like a reasonable investment. The before mentioned advantages result in a measurable ROI for your company. In the long run, this approach will allow you to grow your revenue by accelerating your business velocity and competitiveness.

Few of the challenges faced by today’s highly competitive IT industry that are overcome to a great extent by adopting DevOps model are explained as follows:

1. Poor Feedback and Transparency:

The consumer is only introduced to the product at a later point due to predetermined discrete work stages. This results in significant discrepancies between the expected and delivered output, necessitating rework. Employees also fail to respond to user complaints in a timely manner. While those managing software development projects are frequently bombarded with status reports, they typically have little to no insight into actual progress. This is due to the fact that it concerns working software features and functionality rather than business productivity.

2. Collaboration and Communication Gap:

Traditional software development initiatives necessitate lengthy approval processes. As a result, management time can be lost because several permissions are necessary. This means that project delays are compounded by management delays. The division of the software developers, test engineers, and maintenance staff into three separate groups is solved by adopting the philosophy of DevOps.

3. Frequent Errors and Infrequent Updates:

Failure to communicate, along with a siloed departmental structure, results in an unorganized and time-consuming methodology, as well as defective products. Fixing a bug and making new changes and implementing them can take weeks or even months when there is no direct interaction between the testing engineers and developers. If a company consistently fails to execute projects on schedule, it will not advance in the market.

4. Insufficient Testing:

The tests are carried out individually in unit forms during this phase. These tests are insufficient to produce standard quality output in terms of increased functionality and proper problem detection. Due to organizational bottlenecks, test experts struggle to maintain a continuous testing process at each level of development. Due to these flaws, the teams face post-release faults that could have been avoided by continuous testing at each stage.

Conclusion: DevOps prioritizes the satisfaction of the customers by providing quick delivery of features and updates, making it a more preferred method than the traditional development model. The future will drive modifications in tooling and administrative strategies. Focus on cloud optimization will sharpen and automation will play a major role. The time is now.

Data Visualization using MS Excel Sumeet Shah March 31, 2021


The ability to analyze data is a powerful skill that helps you make better decisions. When it comes to choosing a tool, there are several options; however, the first one you think of is Microsoft Excel. Why? It is a tool that is deep-rooted in almost every business and our day-to-day life. The extent of our dependence on it is such that “Export to Excel” is the most used option amongst BI communities. In terms of usage, there are over 750M Excel users worldwide, and literally, any function in any organization/industry can benefit through the use of Microsoft Excel. Additionally, the ability to learn and quickly put it into practice gives you another reason to adopt it.

“I never guess, it is a shocking habit—destructive to the logical faculty” – Sherlock Holmes

Today, we live in an information-rich and time-poor world. Businesses have transformed from being intuitive to data-driven engines. Leaders crave data because they are aware of its availability. However, what they are looking for is not data; it is information and knowledge extracted from it. The big problem, though, isn’t data or too much of it. It’s the lack of time; there just isn’t enough of it to analyze the tons of data you have at your disposal. So, if your organization isn’t using BI tools like Tableau or PowerBI and plan to use Microsoft Excel as a tool for data analyses, this might be of your interest. 

Above is a quick piece of analysis done on the recently concluded India vs England Cricket Test Series. A simple export of scorecards for all the test matches and the use of basic excel functions like IF, SUM, RANK, VLOOKUP, and OFFSET have been used to transform the data into a structured format. There are no VBA macros used anywhere in the analysis, and the visualizations have been created by basic chart options available as a part of the default excel package. 


If you are an organization looking to analyze your data and are struggling with it, feel free to reach out to us at Incentius.

Free Tableau visualization dashboard for your Shopify store! Sumeet Shah January 28, 2021


Do you know that the majority of e-commerce businesses use 0.5% of all the data available at their disposal? Are you sure you are amongst those and not the rest?

“Are you serious?” This is exactly how one of my clients responded when they were asked this question.

“It is a capital mistake to theorize before one has data. Invariably you end up twisting facts to suit theories”.

This quote from Sherlock could not have made more sense ever.

That’s where we are today, especially in the e-commerce realm of the world. Shopping has changed, and online purchase is no more a linear process. Every click, every product your customer views and all of their historical purchases hold critical importance. It tells you about their online journey. What one buys, when one buys, what are the top items bought together, and why would someone cancel an order has an abundance of insights. We do not even have to go too far to prove this. Amazon drives 35% of its revenue by capturing these insights. They are working towards opening up a whole new prospect of a personalized digital shopping assistant.

Are you doing your bit to leave the legacy approach and adopt an innovative data-driven one? By not going with the latter, you are losing business to the ones who are going with it.

Our client could not believe the insights we churned out from their data. Here’s a free sample Tableau dashboard built over Shopify for you. Just extract the Shopify orders data and plug it into this dashboard (user guide attached for convenience) and take a deep dive into the rich insights your data holds.

Claim your free tool hereYou can use the same dashboard with some tweaks if you are using any other e-commerce platform like BigCommerce, Magento, Woocommerce, Squarespace, Wix, Big Cartel, or Salesforce Commerce Cloud. Even better, if you reach out to us at Incentius, we can help you with custom insights based on your requirements and recommendations to increase your footfall-to-conversion ratio.