Tranzita Engineering Blog

GraphQL VS REST API

2023-03-02T01:46:00.002-08:00

GraphQL is well-known as an API Query Language that enables declarative data fetching, giving the client the option of specifying the data required from the API. GraphQL facilitates the evolution of APIs over time. GraphQL was created by Facebook in 2012 and was open-sourced in 2015.

REST API is defined as a Software Architectural Style created to guide the design and development of World Wide Web architecture. REST APIs define a collection of restrictions on how a distributed system's architecture should behave.

This blog focuses on the major differences between GraphQL and REST API to help you make an informed decision about which one best meets your business needs.

What exactly is GraphQL?

GraphQL is a Query Language for interacting with APIs. It allows the client to send HTTP requests and receive responses.

In GraphQL, any set of information is seen in the context of a graph, as the name implies. Objects are represented by nodes defined by the GraphQL Schema System. Edges between such nodes are employed to portray a graph's connection among nodes.

This helps to ensure consistent links between queries and enhances object connectivity. Users can also use GraphQL to request data from multiple resources with a single request.

GraphQL allows you to make ad-hoc queries rather than making multiple requests to retrieve data. GraphQL also allows the user to define the type of information that must be received from the server. Because of its predictable data model, it is both efficient as well as readable. GraphQL is very simple to grasp. Its declarative essence lets you make the most of it.

What exactly is REST?

REST APIs offer a flexible and lightweight method of integrating applications. REST APIs enable you to search for things, and the results are brought back from the service you requested. A REST API contains the following elements:

Headers: The Authentication Mode and Content Types are referred to as headers.

The Method: There are several strategies for calling a REST API, the most common of which are.

POST and GET: The GET method is used to gather information, whereas the POST method is used to send data to web resources.

The Endpoint: The URL of the resource to which you're going is defined as the Endpoint.

The Data or Body: This corresponds to the text data in JSON format which must be transmitted to the resource.

Listed below are a few REST basic principles that really are vital for a REST API's smooth operation:

Client-Server Decoupling: When designing a REST API, the server and client applications have to be entirely independent of one another. The URI of the requested data should be the sole information the client has access to. Similarly, an application server must not change the client application other than to send the information requested via HTTP.

Statelessness: REST APIs have no state. This implies that each request must contain all of the information required to process it. This may imply that REST APIs do not require server-side sessions. Server applications are not allowed to store any client-related data.

Cacheability: Resources should be cacheable on the server or client-side whenever possible. Server responses must also indicate as to if caching is authorized for the deliverable resource. You should try to boost client-side performance whilst also trying to increase server-side scalability.

Uniform Interface: Regardless of whom the request comes from, all API requests for the exact same resource should take a glance at the same. The REST API should make sure that a single piece of data, like an email or a user's name, is delegated to only one Uniform Resource Identifier (URI). The resources should not be excessively large, but they should encompass all of the data that the client can perhaps require.

Layered System Architecture: REST API responses as well as calls go through various layers. You must not assume that the Client and Server applications actually communicate. The communication loop may include many intermediaries. REST APIs therefore must be created in such a way so that neither the server nor even the client can tell whether they're communicating with just an intermediary or even an end application.

Why was GraphQL created when REST was already available?

The following are the primary reasons why GraphQL was created as a feasible alternative to REST APIs:

The early 2010s have seen a rise in mobile usage, which caused some problems with untidy networks and low-powered gadgets. REST APIs also wasn't incredibly adept at resolving these problems.

The percentage of front-end platforms and frameworks which run client applications had also grown in tandem with the increase in mobile usage. Cos of REST's lack of flexibility, it was difficult to develop a single API that could quickly meet the requirements of every client.

The main reason for creating GraphQL as an option was because the majority of data is in use in mobile apps and the modern web has a graph shape.

The main distinctions between GraphQL and REST

GraphQL and REST are two design methods that both concentrate on the same feature: data transmission through the use of internet protocols like HTTP. However, it is in the execution that they deviate. Here are a few things to think about when going to decide between GraphQL and REST:

Usability of GraphQL vs. REST
Compared The performance of GraphQL and REST
GraphQL vs. REST: Which Is More Secure?
GraphQL vs. REST: Which is more popular?
GraphQL vs. REST: Data Access
Usability of GraphQL vs. REST

Whenever it comes to GraphQL vs REST functionality, the two are markedly distinct in terms of both version control and predictability. The high predictability of GraphQL is a massive benefit.

It enables you to make a request to your API and obtain only the results you need, with no undesired inclusions. GraphQL queries generate predictable outcomes, which enhances their usability substantially.

The behavior of REST, in contrast, hand, varies based on the HTTP and URI methods utilized. When an API consumer calls an endpoint, this could make it unclear what and how to expect.

REST does not have standardized and clear guidance for versioning. This means that each provider is free to choose their own approach. GraphQL is on the other end of the spectrum. It did take a straightforward approach to version control: don't version APIs.

When trying to compare GraphQL vs REST API in aspects of versioning and predictability, GraphQL's simplicity provides it with an advantage over REST.

Performance Comparison of GraphQL and REST

In terms of GraphQL vs REST performance, RESTful services' proclivity for Over-fetching and Under-fetching gives GraphQL a significant advantage. Because REST APIs have inherently rigid data structures designed to return the specified data when they are called, you may end up with unnecessary information or be required to make numerous calls before receiving the relevant information. These deficiencies can lengthen the time it takes the server to return the information requested.

GraphQL, on the other hand, employs a flexible style that allows you to retrieve what you require in a single API request. After defining the structure of the information you require, the server will return the same structure to you, avoiding over-fetching and under-fetching.

In general, the GraphQL vs REST performance discussion favors GraphQL, but there are some use situations where REST APIs are superior. For example, caching may indeed be preferred to speed up API calls.

GraphQL vs. REST: Which Is More Secure?

When comparing GraphQL and REST in terms of security, it appears that REST has the upper hand. REST provides several methods for enforcing API security.

You can ensure REST API security, for example, by integrating different API authentication methods, such as HTTP authentication. Delicate data is transmitted in HTTP headers, via JSON Web Tokens, where delicate data is transmitted as JSON data structures, or via OAuth 2.0 processes.

GraphQL also includes some security protocols, but they aren't as advanced as those in REST. For example, while GraphQL aids in data validation, users must start figuring out how to apply authentication and authentication metrics on top.

GraphQL vs. REST: Which is more popular?

In terms of popularity, REST APIs win the GraphQL vs REST debate. According to the State of API 2020 Report, approximately 82% of API consumers and practitioners are using REST-based OpenAPI authentication, whereas only 18% are using GraphQL.

GraphQL's popularity, on the contrary hand, has been increasing steadily. Based on the State of JavaScript 2019 Report, whereas only 5% of those polls conducted used it in 2016, the number increased to 38.7% in 2019.

GraphQL vs. REST: Data Access

When it comes to data fetching in GraphQL vs REST, the REST API gathers information by accessing multiple endpoints. Take alternate paths or endpoints that use the hierarchy.

For example, /user/id will be one endpoint to retrieve user data, /user/id/posts will be a second endpoint to retrieve all of the user's posts, and /user/id/followers will retrieve a list of the user's followers.

In GraphQL, on the other hand, you can simply submit a query to the GraphQL server with the data requirements, and the server will respond with a JSON object.

What is the Purpose of REST?

Here are a few examples of when using REST APIs is a good idea:

Monitoring and error reporting: REST enables you to build a monitor and control system based on API responses. GraphQL doesn't really support this feature since it always returns a 200 OK status response.

HTTP Caching Mechanism: Nowadays, all web pages include an HTTP cache implementation to help users avoid getting to reinstall resources. It is also employed to determine if two resources are exactly equivalent. Because you employ the same URL for all requests, GraphQL does not allow you to get a globally unique id for a specific item. For this, you will have to configure your GraphQL cache.

What is the Purpose of GraphQL?

Here are a few scenarios in which GraphQL can be useful:

GraphQL enables users to easily retrieve the appropriate amount of data to render a specific view.
GraphQL is best suited for businesses looking to create mobile-responsive platforms.
Mobile-responsive platforms anticipate applications to be able to respond and also have low latency, which GraphQL could provide due to its simple architecture.
With its syntax SDL, GraphQL defines an API's scheme to use its type system (Schema Definition Language).
It serves as a contract between both the server and the client to define how a client can access the information.
Because the front end can be easily tested with mocks, the front-end and back-end teams can collaborate independently.

The Benefits of GraphQL

Some of the benefits of GraphQL are as follows:

It facilitates query batching and caching.
GraphQL automatically helps to keep documentation and APIs in sync.
It's hierarchical and product-focused.
A single API call can be used to retrieve information.
Because GraphQL is a strongly typed language, queries are implemented in the context of the system.
It has a declarative query language.

GraphQL disadvantages

The following are some of GraphQL's drawbacks:

Design patterns for complex apps are largely missing.
For small applications, this is overkill.
Complex queries have performance problems.
Rather than employing HTTP caching, it uses a single endpoint.

REST's Benefits

Some of the key benefits of REST are as follows:

The protocol has become simple to implement across multiple projects.
Process instances can be explicitly created.
Developers can create an API to fulfill a specific user need.
REST APIs are simple to create and modify.
It makes it possible to easily scale software.

REST's disadvantages

The following are some of the major drawbacks of REST:

There is no method for obtaining restricted fields.
Inadequate data search capabilities.
There is no query validation available.
It is not possible to manipulate nested resources.
It does not keep track of previous interactions between the client and server.

Comparison of GraphQL and REST APIs

To give you a better idea, here is informative data on a few main differences between GraphQL and REST API:

Sr.No GraphQL REST

GraphQL is built on a client-driven architecture. REST is based on a server-driven architecture.
GraphQL only claims to support JSON as a data representation format. REST supports a wide range of data formats to make it easier to use.
GraphQL does not have a fully automated caching mechanism. Unlike GraphQL, REST automatically wants to implement caching during its processes.
GraphQL does not support API versioning. REST is compatible with various API versions.
GraphQL is defined as a query language that provides flexibility and effectiveness in solving common issues associated with API integration. REST is defined as a style of architecture that is generally regarded as a standard for developing APIs.
Error identification in GraphQL is complicated because it involves dealing with HTTP Status Codes.
REST, on the other hand, can easily recognize errors by using HTTP Status Codes.

GraphiQl is only used for documentation in GraphQL.

REST provides a variety of options for automated documentation, including API and OpenAPI blueprints.

GraphQL is deployed over HTTP via a single endpoint that exposes all of the exposed service's capabilities. REST is implemented as a set of URLs, each of which can expose a single resource.

REST is implemented as a set of URLs, each of which may expose a single resource.

Which is better, GraphQL or REST API?

To summarise the previous discussion on GraphQL vs REST, the answer is subjective and heavily dependent on your particular project requirements.

For example, GraphQL may be your best bet if you want to inject something new into your API development while sustaining a modern design aesthetic and attempting to avoid multiple round trips to fetch data.

However, if you want to use a tried-and-true technique that provides robust native verification or caching, REST is the way to go.

Knowing the tradeoffs and restrictions that lie beneath each API design style is crucial for choosing the one that meets your requirements best. You might even try deploying a hybrid approach that incorporates REST and GraphQL.

Regardless of your choice, you should concentrate on creating an API product that meets the requirements of all API value chain participants: the API Consumer (Developer), the API Provider, and the End-user (Customer).

Conclusion

This blog discusses the key factors in the GraphQL vs REST discussion to assist you in comprehending them better. It begins with an overview of REST and GraphQL before delving into GraphQL vs REST.

Extraction of complex information from a diverse set of free data sources, such as REST APIs, can be difficult.

Greener, Diverse, and Cloud-Native Analytics Trends and Predictions for 2023

2022-12-30T06:47:00.002-08:00

Artificial intelligence, Digital transformation, machine learning, Automation, Omnichannel, and Predictive analytics are the buzzwords that controlled 2021 and will most likely remain on the radar of every business for quite some time. Lingaro's experts weigh in on the dynamics that corporate leaders and decision-makers are probable to see in the supply chain, procurement, as well as consumer sector, in addition to how analytics will form their pathways in 2022 and beyond if they have not already.

Disruptions remained in 2021, upending the ever-volatile sector. Meanwhile, clients, customers, as well as employees expect more: a comprehensive, cross-channel experience, environmental commitment, and more options for acquiring resources or consuming customized services and products upfront, when and wherever they want. The list would then grow longer, and so will the number of obstacles that decision-makers should indeed overcome.

Making informed business choices while responding quickly as well as proactively was never more important. Organizations must figure out how to use data creatively as well as efficiently to fuel their own long- and short-term strategies. The previous two years provided the impetus for decision-makers to digital format transform, and 2022 will see them embrace analytics not because they desire to, but because they must.

Sustainability: From publicity to business outcome

Take, for example, sustainability: 84% of chief supply chain officers polled said they intend to invest in capabilities that will assist their companies in adapting to and mitigating climate change.

"Sustainability will be here to stay — not just for the sake of publicity, but because the business needs it," said Mateusz Panek, a supply chain specialist and Lingaro's enterprise as well as a business solutions architect. "What we're noticing between many of our clients as well as other groups with which we collaborate is a greater shift away towards doing something that appears or sounds good again for brand and toward a holistic approach motivated by business outcomes."

Companies rely heavily on linear supply chains to maintain their operations running, and yet circular as well as sustainable supply chains are getting momentum in consumer products, high-tech, healthcare, retail, and other industries. Industries involved in logistics. The added value as well as efficiency that reclaimed, reused, or recycled resources offer additional are significant, especially in complex, globally connected supply chains exposed by the pandemic for their fragility as well as bottlenecks.

Setting reduction targets for Scope 3 emission levels will soon be the new business norm. Industrial production, telecommunication services, transportation, as well as aviation, to name a few, are anticipated to lead the way in 2022, especially in terms of lowering their carbon footprint. This is not surprising given that the steel industry alone accounts for at least 7% of global CO2 emissions.

Panek cautioned that without the data and tools to back them up, these goals will be futile. "Businesses cannot reduce what they cannot quantify. Their goals should be in line to their actions. Many organisations struggle with sustainability because they do not concretely measure and track their own environmental effects over time. Advanced analytics can assist by evaluating data to pinpoint supply chain regions where resources can be utilised or emission levels should be lowered."

Panek added that achieving these goals will necessitate a strategic effort. Despite their best efforts, many organisations struggle to make their sustainability efforts meaningful. In fact, only 38% of the world's 8,000 largest corporations were committed to the UN's Sustainable Development Goals (SDGs). "It's wonderful that you've been recycling, but if the recycled materials you are using to package a new product would then end up will become trash once more, you're defeating your intent. "Analytics can shed light on these often overlooked as well as tricky details," he said.

Procurement follows the same rules.

Igor Vasquez, Lingaro’s head of practice for sourcing and procurement analytics, added that laws and regulations are also underscoring sustainability’s significance for years ahead. “Inaction poses legal risks for companies as more governments incorporate sustainability in their policies. These regulations are compelling companies to embed sustainable practices in their supply chains. The COP26 in Glasgow further fueled the conversation on the vital role of businesses in achieving sustainability. We expect that more of these conversations will be backed by legal and transnational frameworks that will directly affect global supply chains. We’re already seeing it in the procurement space,” Vasquez said.

Igor Vasquez, Lingaro's head of practice for sourcing as well as procurement analytics, got to add that regulations and laws are also trying to emphasize the significance of sustainability in the coming years. "As more governments integrate sustainability into their policies, companies are facing legal risks if they act nothing. These regulations compel businesses to integrate sustainability methods into their supply chains. The COP26 in Glasgow helped fuel the debate about just the significant contribution of businesses in addressing sustainability. We anticipate that increasing numbers of these discussions will indeed be backed by legal as well as transnational frameworks that are going to have a straightforward impact on international supply chains. "We're already noticing it in procurement," Vasquez says.

Assessing vendors and suppliers utilizing, and governance (ESG) criteria, for example, is one of the UN's SDGs.Meanwhile, the EU is going to consider levying taxes on nonrecycled plastic packaging as well as increasing the cost of Carbon dioxide emission rights. It also recommended thorough research and corporate reporting guideline that would demand companies to identify, notify, and mitigate ESG threats in their own supply chains.

These discussions are also being sparked by consumers. Shopping preferences are shifting toward socially and environmentally conscious products. Indeed, 72% of consumers globally are willing to pay more for environmentally friendly materials. They expect the business, particularly fast-moving consumer goods (FMCG) as well as consumer-packaged goods (CPG), to do their part.

Bridging gaps with supplier diversity/inclusive procurement Sustainability also enables decision-makers, particularly in procurement, to view supply chains through the lens of inclusion and diversity. "At the time, regulations and laws have been the single force pressing businesses to interact with underserved populations, at least in the United States. "They are now acknowledging the possibility of supplier diversity programs designed to help their companies thrive — whether that be through greater market share, improved brand recognition, or continued to improve change in the company," Vasquez explained.

Supplier diversity, also known as inclusive procurement, actively encourages interaction with businesses that are owned and operated by at least 51% of historically underserved and underrepresented groups or individuals. Supplier diversity initiatives really aren't new; Coca-Cola, Ford, Mastercard, and Target have all implemented them. UPS, for example, spends $2.6 billion annually on 6,000 different vendors.

Many factors contribute to its massive popularity. One thing, it's a growing reaction to social issues. Organizations are also looking into new procurement channels to improve the agility and resilience of their supply chains. In the EU, 40% of manufacturers cite a shortage of equipment and materials as a factor limiting their production.

Supplier diversity also allows companies to interact with new client bases as well as enter new markets. The combined purchasing power of culturally diverse, minoritized groups in the United States has been estimated to be approximately US$3.9 trillion. Women-owned companies in the United States produced at least $1.9 trillion in revenues in 2019. The changes are encouraging. Within the next four years, businesses in the United States are expected to boost their diversity expenditure goals by 50%. These businesses are currently assigning US$72 million per billion dollars of total expenditure.

"An inclusive procurement policy expands the pool of suppliers," Vasquez said. It inspires innovation and competition resulting in the knock-on result of increasing product quality and continuing to drive down costs. Having a such diversified group of suppliers indicates a lot in today’s dynamic times.” It also increases credibility among many of today's outspoken and socially conscious consumers, who are progressively relying on brands to fill gaps left by governments.

The major difficulty, according to Vasquez, is to keep moving beyond tokenism. Only 43% of US businesses surveyed use information and real-proof strategies to validate their own diverse expenditures. "Leaders must implement rigor to supplier diversity efforts in order to reap the great benefits. They shouldn't be concealed in the procurement cycle. They should indeed take the stage. This is where analytics can assist — it can simplify the process of reporting and measuring the firm's sourcing or procurement having spent with multiple providers. "This lays the groundwork for diverse characters in the supply chain," Vasquez said.

Personalization that cuts out the middleman

Sustainable development, as well as supplier diversity both, have an impact on the bottom line, yet there is one crucial factor that has an increasing influence on value chain decision-making — today's consumers. Their expectations require, and behavior patterns force businesses to reconsider and redefine their own operations.

"These days, expert knowledge and ease of access appear to be should indeed for this generation," said Michal Jablonski, Lingaro's information strategist as well as head of consumer data analysis practice."Under such challenges, an increasing number of businesses are rethinking how they can get closer to their own customers beyond customisation options. One of the continuous trends we see in many FMCG as well as other product-based market participants is their attempt to just get closer to the end customer in terms of both understanding and production and marketing."

Personalization, Jablonski clarified, will always be present, but a D2C approach adds a new dimension. Even though brands own their entire customer journeys, they can conduct business with customers in ways that significantly improve the product lines, business operations, and communication systems for their target market. Buyer relationships may be more individualised, providing the most value and enhancing the overall experience. Consumers in Europe, for example, rank product truthiness as the second most significant reason for buying beauty or household goods directly from of the brand, with luxury products ranking perhaps higher.

A direct-to-consumer (D2C) business model allows companies to reduce the manufacturer and consumer value chains by creating or activating with there own digital as well as e-commerce streams even while attempting to sell to numerous marketplaces. D2C e-commerce sales in the United States rose by 46% in 2020, bringing a total of US$111.5 billion. It is expected to reach 152 billion next year. It will achieve $175 billion by 2023. So many international firms and retail behemoths would then jump just on the D2C bandwagon, making investments in supply chain management, payment, as well as stock control capabilities as a way to divert customers' focus away from marketplaces or towards their own. This has been the case for companies like Nike, Walmart, even Colgate-Palmolive Nestle, and Unilever.

Adopting a D2C model, on the other hand, entails more so than simply putting your products online. "Brands, especially brick-and-mortar business owners, will confront strong competition in establishing their presence online to a wider audience," Jablonski advised. Businesses will take on even more roles and responsibilities as intermediaries are removed, such as marketing, inventory control, logistic support, as well as cybersecurity. The allure, as well as boon of D2C, may encompass more preconditions than some consumer products companies' resources could indeed handle."

Data is one of these prerequisites. But not simply any data: brands must have the right data to comprehend who their consumers are — their purchasing habits as well as demographic trends, for example — and what message will gently push them to purchase and return. Brands also request knowledge of what their own consumers say regarding their products in order to enhance them. Getting robust e-commerce capabilities is one thing, but data analysis can facilitate brands in developing an effective D2C strategy — from choosing the right audience to constructing marketing plans, defining KPIs, and leveraging advanced methods such as AI as well as machine learning to get closer to their customers.

A cloud-native, digital-first approach

The ability of a company to invest in and use technology proactively will determine its success in terms of long-term sustainability, supplier diversity, as well as the D2C model. Many companies will continue to prioritise digitalization, but in order to achieve it, they have to be capable of navigating today's as well as tomorrow's innovation landscapes.

This echoes a sentiment expressed by 80% of consumers in 2022: an all-digital globe within which the majority of exchanges, interrelations, as well as experiences will actually occur. Cloud-native apps are expected to account for 35% of production apps as well as 90% of new digital services by 2022.

People, procedures, and process flows are reinvented as well as future-proofed with a cloud-native as well as a digital-first attitude. It is no longer required to migrate application forms, information, as well as business functions to the cloud. It now involves orchestrating strategic plans.

The cloud-assisted international markets in trying to mitigate and respond to interruptions, particularly throughout a pandemic when resources have been dispersed as well as the workforce must be remote. Organizations will utilize the cloud, even more, to develop solutions rapidly, scale their execution, and enable automation. More than 90% of apps would be cloud-native by 2025, with 95% of digital workloads deployed on cloud applications. These technical frameworks will open up new possibilities for groups that are willing to just be early adopters:

Composability will be the watchword: Modularity will not simply be a principle for trying to deal with interruptions; it will also encompass corporate culture, predictive analysis, and IT infrastructure, which will all be cloud-based. 75% of enterprises would be running containerized applications by 2022. Information democratization in data analysis will enable employees to create solutions that package separate business capabilities into composable applications that utilize low- or no-code platforms.

Data warehouses, data lakes, and data fabrics: A new approach to information planning will be intertwined into the data ecosystems of enterprises. This isn't to say that data warehouses and data lakes won't exist in the future; they're separate but necessary and complementary solutions for storing, moving, processing, and reporting data. In contrast, data fabrics are deliberately developed to just provide fully integrated, consistent, and immersive access to multiple sources of information. Data fabrics have the potential to cut data management efforts in order and costs by up to 70%.

If necessity is the mother of invention, the disruptions of the past two years have forced companies to re-imagine how they conduct business and also have speeded up their strategy and necessitated that they digitally transform. This momentum would be sustained in the years ahead. Businesses are going to accelerate, and analytics will serve as the fuel that enables them to keep up.

In an uncertain world, however, every decision matters. Emerging trends and technological advances will create more opportunities, but the hype as well as the risk associated with them could outweigh their real-world value and utility. Knowing what's coming will not be enough. Organizations must examine their information in order to address their own priority areas and use analytics to eliminate the discrepancy between their vision as well as the impact.

Why Do we use cloud service

2022-12-06T22:50:00.000-08:00

These days, the terms "cloud," "cloud services," and "cloud computing" appear to be everywhere. Everybody talks about it, uses it, fascinated by it, but who truly understands what it is and, more importantly, why anyone should use it?

I. The cloud and its benefits

In general, the term cloud comes from the cloud drawing that was used to represent the Internet itself. The relationship between the cloud (as we now refer to it) and the Internet is self-evident: cloud computing entails managing your data through a third-party network hosted over the Internet. In a summary, cloud services provide you with an online hard disc.

How can you profit from this? That's simple - you stand to gain significantly! When compared to local data storage, cloud services provide significant advantages:

Accessibility

You can access your data from any device by using a cloud service. In practice, this means that you can have your personal and professional documents, family photos, videos, music, and so on with you at all times, no matter where you are. So, if you run into a friend at a coffee house, you can show him your vacation photos on your tablet. If you're on a business trip and need to access an important document, you can use an external computer. Isn't that amazing?

Convenience

The provider manages all associated services, so you don't have to worry about anything. You do not require any extra physical devices (such as an external hard drive), just some a kind of end device (which shouldn't be an issue in a world where the number of mobile phones well almost outnumbers the world's population) and internet access. Many businesses have discovered this benefit as well. It is undoubtedly one of the primary reasons why the percentage of businesses using cloud computing is expected to rise from 36% to 52% this year.

Affordability

The low prices of cloud services are especially useful now that hard disc prices are skyrocketing and the economic situation calls for cost savings. Several companies believe they can cut costs by up to 20% per year. There's also a rumor going around that there are certain crazy cloud providers out there who offer their services for free ;)

Plenty of storage space

Your cloud storage space is scalable. That is, you can change the size of the storage space you require at any time. Unlike a hard disc, which becomes full at some point, you can increase your "online storage space" at any time.

II. Market availability of cloud services

There are numerous cloud services available in the marketplace. Some are provided by Google, Apple, and Amazon, while others are created by innovative start-ups. However, several practical solutions are a number of practical solutions available to both consumers and businesses. Here are a couple of examples:

Traditional cloud services include Dropbox, Google Play, iCloud, Gmail, Picasa, and Calendar, to name a few. Amazon's cloud computing

Netflix and YouTube are two video cloud services.

Pandora, Grooveshark, and Spotify are examples of music streaming services.

Twitter, Facebook, and other social cloud services

Wunderlist and Evernote are two cloud-based note-taking and task-management services.

Salesforce CRM cloud services

Cloud text messaging services:???

III. Cloud-based text messaging

Text messaging was once the sole network operator's business. None of these operators have ever been interested in a text messaging cloud. They ignored the advantages that such a service would provide their users. These days, thankfully, are over! Mysms saw an opportunity and seized it. The mysms Cloud is a text messaging cloud that provides its users with features that greatly simplify message management.

Accessibility

All text messages are synced across all devices. This means that a user can access his or her text messages, images, and files from any end device, no matter where he or she is.

Convenience

Aside from the convenience of not having to worry about saving or losing messages, pictures, and files, mysms is useful for users who are not friends with their smartphone's keyboard. Users can type messages on their computers using the mysms cloud.

Affordability

Speaking of insane, the mysms Cloud is completely free.

Storage area

All text messages, images, and files sent and received are saved in the mysms Cloud. There will be no more lost text messages. Furthermore, if you switch devices your old messages follow you - automatically.

Overall, I believe that cloud computing is one of the most significant benefits that modern technology can provide. It appears to be an easy-to-manage tool for the end user, which is great because that is exactly what it was intended to be. Some people haven't realised they're already using a cloud service (for example, Gmail or Facebook ), but they'll when you help us spread the word. So, please share as well as inform your friends about the benefits of using cloud services.

This Is Why Python Is So Popular

2022-08-07T22:57:00.084-07:00

What exactly is Python, and why is it so widely used?

Even as more people pursue careers in software programming and development, this is a frequently Googled question. Today, there are numerous coding languages available. However, Python is the leader of the pack. What is the driving force behind the increased demand for Python programmers? In our most recent blog post, we will discuss this and other topics.

To begin, consider the programming language Python.

Python: A Beginner's Guide

Python is an interpreted high-level general-purpose language that emphasises code readability via the use of considerable indentation. It supports various programming paradigms, which include structured (particularly procedural), object-oriented, as well as functional programming, and is dynamically typed and garbage-collected.

Guido van Rossum, a Dutch programmer, began working on the Python programming language in the late 1980s, and the language was first released in 1991 as Python 0.9.0. Many versions of it have been released since then. Python 2.0 was released in 2000, and it included new features such as list comprehensions and a garbage collection system that used reference counting. Python 3.0, a major revision of the language, was released in 2008. Python 2 was retired in 2020 with version 2.7.18. Python has done remarkably well in its three decades of existence and is one of the fastest-growing coding languages. It is now used in web development, AI, web application development, operating systems, machine learning, mobile app development, data analytics, video games, and data visualisation. What factors have contributed to Python's dominance of the list of most popular programming languages? A variety of factors have contributed to this. In the section below, we go over them in depth.

Why is Python so well-liked?

Ease of use and learning
Python has been designed to be an easily readable language, with visually uncluttered formatting and the use of English keywords where other languages use punctuation. Curly brackets aren't used to delimit blocks here, and semicolons after statements are almost never used. Furthermore, when compared to C or Pascal, it has far fewer exceptions in its syntax and very far fewer special cases. All of this definitely Makes a very simple language to use, and it is also simple to learn. Indeed, Python code is easier to write and executes much faster than other coding languages, making it an excellent choice for coding beginners and newcomers. Furthermore, because it is an interpreted language, It also helps that one can quickly change its code base, which adds to Python's popularity among developers.

Frameworks and rich libraries
Python now boasts a variety of rich (and often open source) libraries that developers can use, especially in the early stages of development, with corporates favoring it and a large supportive community to boot. Matplotlib, for example, is used for plotting graphs and charts; SciPy for engineering applications, math, and science; Beautiful Soup for HTML parsing and XML; Django for server-side web development, NumPy for scientific computing, NLTK (Natural Language Tool Kit) for Natural Language Processing (NLP), as well as Scikit-learn for machine learning applications. Furthermore, there is no shortage of cloud media services that offer cross-platform support via library-like tools.

A Strong and Helpful Community
A strong community's support is important, and Python ranks highly in this category. In fact, those working with the Python programming language can easily access a wealth of documentation, guides, and video tutorials. Python developers of varying skill levels can benefit from the assistance required to improve their skills in this language, making it a popular starting point for new coders. Python, which is also used for in-depth research projects, is commonly used to introduce students to computer science. Because of the advanced and active Python community, all issues are typically resolved quickly, ensuring that overall project development is not hampered.

Versatility, reliability, efficiency, and speed are all critical considerations.
Python has a reputation for being more efficient, dependable, and faster than most modern languages. It can now be used in almost any type of environment, which is a significant advantage. So the developer could be working on a mobile app, a desktop app, a website, or hardware programming, and Python would be a proper programming language for such a project.

Excellent adaptability
Python lends itself well to experimenting, owing primarily to its flexibility. Anyone who is familiar with the Python programming language can try to build something new with it. Other programming languages do not provide such flexibility and freedom.

Large Tech Support

A programming language is bound to grow faster when it is supported by a large tech company. Today, you'll see these in a variety of places, including PHP, which would be supported by Facebook, C# by Microsoft, Sun, Visual Basic &, and Java by Oracle. Similarly, Python has the support of big tech: many big companies. Facebook, Google, and Amazon Web Services are the global players that support and use the coding language widely in their projects. Google, for example, has been using Python since 2006 as well as shall continue to do so for its multiple platforms and applications. In addition, Google has invested a considerable amount of effort, and money in the training and success of Python, to the point where it has a portal dedicated solely to Python. As a result, the list of support tools plus supporting documents is rapidly expanding, making Python more accessible to developers.

Popularity and uses in Machine Learning, Big Data, and Computing in the Cloud

Python is now the most popular coding language in data sciences (including data analysis) after R; the language is also widely used in research and development. Python is commonly used by organizations that work in emerging and exciting areas such as Big Data, Artificial Intelligence (AI)/Machine Learning (ML), deep learning, and cloud services to transform and improve their workflows and processes.

Automation

Python, with its tools, modules, and libraries, also provides greater convenience to automation developers. They typically end up typing only a few lines of code (and in a short period of time) in order to automate tasks, including software testing.

Python is the preferred programming language.

As the reasons stated above show, Python is in high demand in a variety of industries today, and it is frequently the ideal programming language for many programmers as well as students. In fact, Python is now an important part of data science courses. Knowing Python is a huge asset to a programmer's resume and opens up many doors for such candidates.

Python Implementation in schools and colleges

Given that Python provides a wide range of job opportunities, it is not surprising that universities, schools, and afterschool programmes are choosing to focus on teaching students this coding language. All who learn Python in school or evening classes stand to benefit more often when they attend university and pursue a career in computer science.

ONDC the game changer in the eCommerce industry

2022-07-22T04:43:00.003-07:00

ONDC is a term that stands for Open Network for Digital Commerce. Consider it a network that will bring together a diverse range of buyers and sellers in a single digital space. But doesn't this sound like Amazon or Flipkart? No, not at all, and here's why.

ONDC is not a platform. It is a decentralized, interoperable network. Okay, too many big words. Essentially, it is a connection of multiple buyers and sellers who can connect with one another and offer a greater variety of options, likely more than Amazon and Flipkart or similar sites. On the pricing front, while ONDC will not prescribe any particular fees to be charged by participants in the network, competition would then ensure that commissions are significantly less than current e-commerce monopolies.

So, how exactly does ONDC work?

As I previously stated, ONDC is a network of buyers and sellers. Buyers (people like you and me) are current users of payment apps and tech platforms. For example, if you use PhonePe, and PayTM, you automatically become a buyer app user.

Sellers (merchants) are onboarded onto seller applications by companies that either digitize in the business of digitizing commercial activities (e.g. eSamudaay) or even have end-to-end order fulfillment technical know-how. A seller app's goal is to onboard retailers, consumer-packaged direct-to-consumer (D2C) brands, and goods (CPG) brands to ONDC1.

According to reports, ONDC currently charges sellers a 3% referral commission to find buyers. This really is 7 to 10 times less than the current 23% - 28%2 online seller commission rates. Furthermore, the merchants do not have to spend money on customer acquisition or marketing because the buyer apps already have regular customers who use them.

What is the current situation with ONDC?

ONDC is now available in five cities: Delhi NCR, Bhopal, Bengaluru, Coimbatore, and Shillong. Currently, the operations are focused on retail and restaurants, as well as facilitating real-time transactions. The open network will eventually expand to other categories such as travel and mobility.

ONDC will be expanded to 100 cities and towns across India by October 2022, based on the pilot exercise and after the network stabilizes. The goal is to accommodate 30 million online sellers and 10 million online merchants.

Who are the main players in ONDC?

Twenty government and private organizations have agreed to invest INR 2.55 billion (US$ 33.34 million). Several both public and private banks, including Kotak Mahindra HDFC, Axis Bank, and the State Bank of India, are involved (SBI).

Around 80 companies are working to integrate market participants with the ONDC platform. ONDC is being integrated by startups such as hyperlocal delivery startup Dunzo, Flipkart's logistics arm Ekart Logistics, and payment service provider PhonePe.

"Anyone with a monopoly will pretend to be in fierce competition."

When a platform grows in size, buyers and sellers experience concentration risk, which can lead to a single point of failure. Power concentration results in deliberate exclusions, discretionary behavior, and predatory pricing. They no longer function as a platform and instead become market monopolists. This means that smaller and medium-sized sellers will lose the ability to participate on their own terms. In some cases, a seller must pay a high price to migrate or sell on various platforms, such as their own. This includes maintaining separate infrastructure, increasing costs, and restricting participation. Solving these issues for a country with a large population necessitates a multifaceted approach.

In technical terms, a shift from an operator/platform-centric model to a platform-driven model to an interoperable decentralized network was required.

What this means for small and medium-sized businesses is:

Boost will join the ONDC network.
Improved discoverability6 and trust, regardless of platforms
Buyer and seller autonomy

The concept of open network commerce is not limited to the retail sector; it can also be applied to other types of digital commerce such as mobility, logistics, travel food delivery, and other products and services7.

So that's the motivation behind establishing ONDC!

What awaits us?

First, the ONDC aims to increase e-commerce penetration in India to 25% of consumer purchases in the next two years, up from nearly 8% now in a country of 1.35 billion people.

Second, it hopes to enroll 900 million buyers as well as 1.2 million sellers on the network share over the next five years, with a gross merchandise value of $48 billion.

Third, it hopes to entice logistics companies and others who are able to work with sellers to deliver customers their goods. Small merchants as well as rural consumers should be prioritized, with apps available in Indian languages.

Fourth, ONDC's primary goal is to reach millions of small businesses, many of which lack technical knowledge, so the government will need to conduct extensive public awareness campaigns.

Fifth, the Competition Commission of India's role would be critical in ONDC's success because smaller businesses with low volumes lack the funds to compete with heavyweights such as Amazon and Flipkart. Only when big players are effectively stopped from engaging in anti-competitive practices can they be integrated and sustained.

Sixth, the government must ensure that ONDC complies with the Information Technology Act of 2000 as well as the pending Personal Data Protection Bill.

Conclusion

ONDC offers a technological platform to help India achieve its goal of becoming a digitally savvy and wired middle-income economy. It provides a critical global template for leveraging technology for and as a public good at the population scale in an equitable and inclusive manner.

Advantages of remote access

2022-07-08T00:53:00.000-07:00

One of the most important lessons of 2020?

Work can take place anywhere. "Going to work" used to mean sitting in traffic, going to an office, and then returning home after a long and exhausting day. Following COVID-19, we now know that "office-based offices" are far less important than previously thought. Employees can now access workplace applications as well as remote desktops from home offices, offsite locations, and business travel thanks to remote access technology.

There has long been discussion about flexible work and distributed workforces. But, after COVID-19 reared its obnoxious and unwelcome head, it wasn't high on anyone's priority list. In fact, many businesses regarded it with skepticism. And rightly so, because it needs a high level of trust that employees will be as hardworking at home as they are at work.

But life has a way of taking decisions away from us. And, once there was no choice but to work from home, working remotely enabled businesses to continue operations uninterrupted (or at least relatively uninterrupted) during the crisis. And, as it turns out, working remotely has significant long-term benefits that should be investigated and utilized much further than crisis mode.

For years, business experts have extolled the virtues of remote work. To name a few advantages, remote working increases employee satisfaction, productivity, and overall profitability. There are also intangible benefits, such as the increased peace of mind which comes with less time spent in traffic, and also the environmental benefits. In this post, we'll look at some of the advantages of remote working from a non-panic perspective.

The Benefits of Remote Access

Flexibility. Allowing your employees to perform work activities outside of the office via remote access allows for more flexible work schedules and helps the employees achieve a greater work/life balance. People can devote a larger proportion of their day to just being involved with their family members while remaining committed to their jobs thanks to remote access, and travelers can go abroad without having missed a beat thanks to their own remote desktops.

Continuity of operations. Remote access greatly improves businesses' ability to function in times of crisis, as demonstrated by the COVID-19 crisis. Even under less dramatic situations, remote access enables employees who are not able to travel to the office due to obstacles such as adverse weather, transportation strikes, or mild diseases to access their business data and applications from home, making sure they meet their goals on time. Similarly, if an urgent client or operational situation emerges after hours, managers can handle it from the household (or wherever they are) instead of returning to the office, which could lead to delays.

The Global Talent Pool. Are you looking for new employees? You don't have to limit your search to local talent when you have remote access. Instead of being limited to a single geographical location, remote access organisations tap into a larger global talent pool. The advantages are twofold: first, talented people living in remote areas can now find well-paying as well as satisfying employment options, and second, firms no longer need to restrict their searches to a 50-mile radial distance to locate the right talent.

Overhead has been reduced. Hiring remote talent and allowing employees to operate from home-office remote desktop computers allows businesses to save money on office space, utilities, software, computing equipment, furniture, and other expenses related.

Employee efficiency and retention have increased. Many professionals report that remote working, on their preferred devices, in a comfortable environment, increases their job satisfaction and productivity. Indeed, many people have reported being much more productive without the distractions of office environments. This, in turn, improves work quality as well as business profitability while also increasing retention of the employees.

Software licensing has been optimized. Organizations can optimize software licensing agreements by hosting applications centrally and providing employees with remote access. Applications that are only used infrequently by a large number of users, for example, can be installed once on the server and distributed under concurrent usage licenses, instead of purchasing completely separate licenses and installing them locally for every employee and device.

Maintenance costs have been reduced. Although many remote access alternatives necessitate the installation of remote client software on client devices, some web-based remote access systems allow remote access even without installation or setup of any end-points. Clientless, centrally controlled solutions completely remove the requirement to install, manage, and manage the system on personal devices, lowering IT support costs significantly.

User Preferences Compatibility. Selecting remote access software that is compatible with just about any browser and users' preferred devices, such as Macs, Chromebooks, and iPads tends to make remote access a suitable BYOD enabler. Users are content to use whatever device they prefer.

Remote Access: A Long-Term Good Enabler

Even when organizations are not in crisis mode, remote access provides numerous benefits to both employees and employers. Obviously, it must be carefully implemented with a robust and secure solution, which needs to be done with great caution and advance planning outside of a crisis. However, the advantages it provides, such as increased job satisfaction and productivity, can help any business increase its long-term profitability and revenue.

Pandas on steroids

2022-06-27T06:00:00.003-07:00

Introduction:

When you use Python's Pandas to open a sizable Dataset and attempt to get a few metrics, everything just abruptly stops. If you frequently work with Big Data, you're undoubtedly already aware that using Pandas, a straightforward series-loading operation for a few million rows can take up to a minute! Parallel computing is the term/technique used in the industry for this. In this post, we will talk about parallel computing and the Dask library, which is recommended for such jobs. We will also cover all the different features that Dask brings to the table as well as its limitations. But let’s first start by understanding Parallel Computing.

Parallel Computing

The process of running numerous processors an application or computation simultaneously is referred to as parallel computing. In general, it refers to a type of computing architecture where big issues are divided into separate, smaller, typically related sections that can be processed all at once. Multiple CPUs work together to complete it by exchanging information across shared memory, which then combines the findings. It facilitates the execution of complex computations by distributing the enormous problem among multiple processors.

By boosting the systems' available computational capability, parallel computing also facilitates quicker application processing and task resolution. The majority of supercomputers run on parallel computing concepts. In general, parallel processing is employed in operational scenarios that require a lot of computing or processing capacity.

Why Dask?

Data manipulation and machine learning tasks are made simple with the help of Python libraries like Numpy, Pandas, Sklearn, Seaborn, and others. For the majority of data analysis tasks, the Python [pandas] module is enough. Data can be manipulated in many different ways, and machine learning models can be created using that data.

Pandas will, however, become insufficient if your data grows greater than the RAM that is available. This is a rather common problem. You can employ Spark or Hadoop to get around this. However, these aren't Python environments. You are unable to use NumPy, sklearn, pandas, TensorFlow, and other well-known Python machine-learning packages as a result. Exists a way to get around this? Yes! This is where Dask comes into play.

What is Dask?

Dataframes, Bags, and Arrays are the three parallel collections available in Dask. because of which it can store data that is bigger than RAM. Each of them can make use of data that has been divided between RAM and a hard disc or that has been dispersed among several cluster nodes. For efficiency, a Dask DataFrame has partitioned row-by-row, grouping rows by the index value. These Pandas objects could reside on a system or disc. Numerous Pandas DataFrames or Series are organized along with the index, and Dask DataFrames coordinate them.

In other words, Dask utilizes all of the cores of the connected workstations, allowing it to process data effectively on a cluster of computers. It's amazing that not every system needs to have the same amount of cores. Dask can accept the discrepancy in core count if one machine has two cores and the other has four.

Dask has two families of task schedulers:

Basic thread pool or local process functionality is offered by a single-machine scheduler. The default scheduler was the first to be developed. It is easy to use and reasonably priced. It does not scale and is only compatible with one machine.
A more sophisticated scheduler is the distributed scheduler. Although it requires a little more setup work, it has additional features. It can be deployed across a cluster of machines or run on a single system.

Dask highlights the following qualities:

Familiar: Parallelized NumPy array and Pandas DataFrame objects are familiar
Flexible: Provides a task scheduling interface to support the integration of projects and more specialized workloads.
Native: Allows for distributed Python computation and access to the PyData stack.
Fast: Has low overhead, low latency, and low serialization, all of which are necessary for quick numerical computations.
Scalable: Reliable performance on clusters with tens of thousands of cores.
Scales down: Easy to set up and run on a laptop in a single process.
Responsive: It is responsive because it was made with interactive computing in mind and provides prompt feedback and diagnostics to benefit people.

Installation:

Type the following command in the terminal to install this module:

python -m pip install "dask[complete]"

Demonstration:

Importing the libraries:

Code to figure out the amount of time and file size:

The first snippet determines the amount of time that has passed since we last performed an operation, such as reading a file. The file size that we'll be using for this demo is shown in the second snippet. The file is about 4GB in size.

Dask over Pandas:

Reading a file — Pandas & Dask:

A 4GB file took Pandas about 5 minutes to read. Wait, size isn't everything; a data set's number of columns and rows also has a significant impact on how long it takes to process. Let's check how long Dask needs to process the identical file.

Holy smokes, Pandas took about 5 minutes to read the identical file, whereas it only took a few milliseconds. Isn't it wonderful? Let's carry out a few additional operations on the dask and pandas data frames, respectively.

Appending two files — Pandas & Dask:

The foregoing operations took about 9 minutes to complete. Let's now see how Dask can be used to improve it.

Well, you just saved yourself another nine minutes or so. Now let's look at some additional regularly done things involving pandas.

Merging the datasets — Pandas & Dask:

It failed:

It attempted for around 30 minutes, but pandas was unable to merge those two files. See if we can use dask to accomplish it.

Well, utilizing dask, it took me hardly any time at all. Dask.dataframe.read csv just reads in a sample from the beginning of the file, as opposed to pandas.read csv, which reads in the full file before inferring data types (or first file if using a glob). When reading all partitions, these assumed data types are then enforced.

Saving a Dataframe to a file — Pandas & Dask:

The file is successfully saved by Pandas. I needed around three minutes to save the filtered file.

Dask Output :

The file is not correctly saved by Dask. It divides the file into many pieces and stores these files in the aforementioned folder. Another issue is that you will never again be able to read this stored file. It is only a time-waster.

A dask data frame can be saved to a file by first being converted into a pandas data frame using this method, and then the pandas data frame is saved to a file.

Limitations

Dask has many drawbacks. You can reliably create efficient distributed computations by understanding them

The central scheduler spends a few hundred microseconds on every task. For optimal performance, task durations should be greater than 10-100ms.
Dask is unable to parallelize within specific jobs. The amount of each assignment should be appropriate to avoid overwhelming any one worker in particular.
Dask uses heuristics to distribute assignments to employees. Though it normally chooses wisely, occasionally unfortunate circumstances do arise.
The workers are merely Python processes, thus they share all of Python's strengths and weaknesses. They are not constrained or limited in any way. You might want to use containers to run dask-workers in production.
Dask allows for the remote execution of any code as a distributed computing platform. Only host dask-workers in networks that you are confident in. Although distributed computing frameworks all agree on this, it is nonetheless important to mention.

Conclusion:

Using Pandas and Dask together is always the ideal choice because one can effectively address the constraints of the other. When utilised separately, I guess you can encounter various problems. Thus, we draw the conclusion that using Pandas with Dask can help you save a lot of time and money.

Complete Guide For Regular Expression

2022-05-26T05:47:00.002-07:00

Photo by Markus Spiske on Unsplash

egular expressions or regex puts a lot of people off, just because of its look at first glance. But once you master this it will open a whole new different level of doing string manipulation and the best part of it is that it can be used with mostly all of the programming language as well as with Linux commands. It can be used to find any kind of pattern that you can think of within the text and once you find the text you can do pretty much whatever you want to do with that text. By this example, you can get an idea of how powerful and useful regex is.

What is Regex?

If you are reading this post then most probably you already know what a regex is, if you don’t know here is a quick and easy definition

Regex stands for Regular Expression and is essentially an easy way to define a pattern of characters. The most common use of regex is in pattern identification, text mining, or input validation.

Let’s Get Regex...

Photo by Kevin Ku on Unsplash

As we have seen regex can be used to find a pattern in the given sentence lets just start this up by finding just character. We are going to have a look at regex with python as this is the programming language that I love to work with.

Python has a built-in package called re, which can be used to work with Regular Expressions.

Just to search simple word

import re

word=” There is something that we are looking for “ re.search(“are”, word)
print(x) #['are']

Like every programming language, there are some special characters in regex and so we need to escape them in order to serve them. Let’s see what happens when we directly used them without using Escape Sequence

import re
word=”www.creatorghost.com 
x = re.findall(“.”, word)
print(x) #[‘w’, ‘w’, ‘w’, ‘.’, ‘c’, ‘r’, ‘e’, ‘a’, ‘t’, ‘o’, ‘r’, ‘g’, ‘h’, ‘o’, ‘s’, ‘t’, ‘.’, ‘c’, ‘o’, ‘m’]

Now let’s see the output using Escape Sequence ( \ )

import re
word=”www.creatorghost.com 
x = re.findall(“\.”, word) #Here only . will be searched
print(x) #[‘.’, ‘.’]

Let's have a look at all the Metacharacters

Python Regular Expression Quick Guide
^        Matches the beginning of a line
$        Matches the end of the line
.        Matches any character
\s       Matches whitespace 
?        0 or more time
\S       Matches any non-whitespace character
\r       Carriage return character
*        Repeats a character zero or more times
*?       Repeats a character zero or more times 
         (non-greedy)
+        Repeats a character one or more times
+?       Repeats a character one or more times 
         (non-greedy)
[aeiou]  Matches a single character in the listed set
[^XYZ]   Matches a single character not in the listed set
[a-z0-9] The set of characters can include a range
(        Indicates where string extraction is to start
)        Indicates where string extraction is to end

Photo by Marvin Meyer on Unsplash

Here we have seen all Regular Expressions now let’s see how we can combine all of them to get a wonderful result. Let’s see them with a real-world example.

Case 1 — Remove All Url From Text

Sample text

text1
text2
http://url.com/bla1/blah1/
text3
text4
http://url.com/bla2/blah2/
text5
text6
http://url.com/bla3/blah3/

Now let’s remove all of the URL

import re
text = re.sub(r'^https?:\/\/.*[\r\n]*', '', text, flags=re.MULTILINE)

Output:

text1
text2
text3
text4
text5
text6

Let's see how it worked. First of all, we used ^https that says starting with https and ? says it can either come one or it can’t come and then we used Escape Sequence to escape all our // and then the .* says any character can come any number of time and then we use \r\n to find all characters till the new line. And that how we were able to select all the URL and then we just python inbuilt re.sub to replace all URL with “ “ or just empty space.

Case 2 — Remove All Number From the text

text 123
text 234
text 3
text4
text5
text6

Now let's remove all number

import re
text =  re.sub(r'[0-9]+', '', text,flags=re.MULTILINE)

Output:

text
text
text
text
text
text

Case 3 — To Remove All Special Character

text @& for you 123

We want to remove all special character like @ &

import re
text =  re.sub(r'[^a-z0-9\s]', '', text,flags=re.MULTILINE)

Output:

text for you 123

Do we need to remember all the regular expressions?

Photo by Mathew Schwartz on Unsplash

The simple answer is no, you don’t have to remember all the regular expressions if you want to find any type of regular expression for most of the time you can google it and find it on StackOverflow or any similar website .So you might've thinking then why should we study this, to be simply put you the latest need to know what code are you coping from the internet as not always it may suits your need sometimes when you need to customize it the knowledge of regex will surely help.

I hope you like this post and you can also see my previous post to know why python is mostly used nowadays. Thanks for reading.

Using Firebase Functions as your Micro Web Service

2022-02-02T03:08:00.000-08:00

Cloud Functions for Firebase is a serverless framework that lets you automatically run backend code in response to events triggered by Firebase features and HTTPS requests. Your JavaScript or TypeScript code is stored in Google's cloud and runs in a managed environment. There's no need to manage and scale your own servers.

Tranzita Engineering Blog

GraphQL VS REST API

Greener, Diverse, and Cloud-Native Analytics Trends and Predictions for 2023

Why Do we use cloud service

This Is Why Python Is So Popular

ONDC the game changer in the eCommerce industry

Advantages of remote access

Pandas on steroids

Introduction:

Parallel Computing

Why Dask?

Installation:

Demonstration:

Importing the libraries:Code to figure out the amount of time and file size:

Dask over Pandas:

Complete Guide For Regular Expression

What is Regex?

Let’s Get Regex...

Case 1 — Remove All Url From Text

Case 2 — Remove All Number From the text

Case 3 — To Remove All Special Character

Do we need to remember all the regular expressions?

Using Firebase Functions as your Micro Web Service

Importing the libraries:

Code to figure out the amount of time and file size: