Articles

Simplify Java Persistence Implementation With Kotlin on Quarkus

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For decades, developers have struggled with optimizing persistence layer implementation in terms of storing business data, retrieving relevant data quickly, and — most importantly — simplifying data transaction logic regardless of programming languages.

Fortunately, this challenge triggered the invention of Java ecosystems in which developers can implement the Java Persistence API (JPA). For instance, Hibernate Object-Relational Mapper (ORM) with Panache is the standard framework for JPA implementation in the Java ecosystem.

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Old Habits Die Hard, But Getting New Ones is Essential. Tips on Getting the Most Out of TypeScript

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In web development, no one is surprised when a new framework or library enters the stage. Implementing complex functionalities and creating UI elements from scratch only using the powers of a specific programming language is not always the most optimal way. Instead, developers either rely on the existing frameworks or create their own for internal use. In the case of programming languages, the situation is a little more complicated.

A fairly large number of coders are unhappy with the limitations of the programming language that they use every day. Therefore, from time to time, we can hear about the new programming language release. The question is, will the development community adopt it as a replacement for well-established technologies? As an example, we can take a look at Dart. It was initially released in 2011 but remained quite unpopular until the Flutter framework was launched in 2017.

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A Beginner’s Guide to High-Performance Computing in Python

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Ever since the Python programming language was born, its core philosophy has always been to maximize the readability and simplicity of code. In fact, the reach for readability and simplicity is so deep within Python’s root that, if you type import this in a Python console, it will recite a little poem:

    Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. The complex is better than complicated. The flat is better than nested. Sparse is better than dense. Readability counts…

Simple is better than complex. Readability counts. No doubt, Python has indeed been quite successful at achieving these goals: it is by far the most friendly language to learn, and an average Python program is often 5 to 10 times shorter than equivalent C++ code. Unfortunately, there is a catch: Python’s simplicity comes at the cost of reduced performance. In fact, it is almost never surprising for a Python program to be 10 to 100 times slower than its C++ counterpart. It thus appears that there is a perpetual trade-off between speed and simplicity, and no programming language shall ever possess both.
But, don’t you worry, all hope is not lost.

Taichi: Best of Both Worlds

The Taichi Programming Language is an attempt to extend the Python programming language with constructs that enable general-purpose, high-performance computing. It is seamlessly embedded in Python, yet can summon every ounce of computing power in a machine — the multi-core CPU, and more importantly, the GPU.
We’ll show an example program written using taichi. The program uses the GPU to run a real-time physical simulation of a piece of cloth falling onto a sphere and simultaneously renders the result.
Writing a real-time GPU physics simulator is rarely an easy task, but the Taichi source code behind this program is surprisingly simple. The remainder of this article will walk you through the entire implementation, so you can get a taste of the functionalities that taichi provides, and just how powerful and friendly they are.
Before we begin, take a guess of how many lines of code this program consists of. You will find the answer at the end of the article.

Algorithmic Overview

Our program will model the piece of cloth as a mass-spring system. More specifically, we will represent the piece of cloth as an N by N grid of point-masses, where adjacent points are linked by springs. The following image, provided by Matthew Fisher, illustrates this structure:
The motion of this mass-spring system is affected by 4 factors:
  • Gravity
  • Internal forces of the springs
  • Damping
  • Collision with the red ball in the middle
For the simplicity of this blog, we ignore the self-collisions of the cloth. Our program begins at the time t = 0. Then, at each step of the simulation, it advances time by a small constant dt. The program estimates what happens to the system in this small period of time by evaluating the effect of each of the 4 factors above, and updates the position and velocity of each mass point at the end of the timestep. The updated positions of mass points are then used to update the image rendered on the screen.

Getting Started

Although Taichi is a programming language in its own right, it exists in the form of a Python package and can be installed by simply running pip install taichi.
To start using Taichi in a python program, import it under the alias ti:
import taichi as ti
The performance of a Taichi program is maximized if your machine has a CUDA-enabled Nvidia GPU. If this is the case, add the following line of code after the import: ti.init(arch=ti.cuda)

If you don’t have a CUDA GPU, Taichi can still interact with your GPU via other graphics APIs, such as ti.metal, ti.vulkan, and ti.opengl. However, Taichi’s support for these APIs is not as complete as its CUDA support, so, for now, use the CPU backend: ti.init(arch=ti.cpu)And don’t worry, Taichi is blazing fast even if it only runs on the CPU. Having initialized Taichi, we can start declaring the data structures used to describe the mass-spring cloth. We add the following lines of code:

Python

  

 N = 128 x = ti.Vector.field(3, float, (N, N)) v = ti.Vector.field(3, float, (N, N))

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How to Prepare for the Immersive Web

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Websites as we know them are going to change very soon. The days of text, images, and basic interactions in a 2D browser window have served us well, but virtual, augmented, and mixed reality experiences are getting better all the time. Developers and designers need to think beyond the browser window and prepare for an immersive future.

Many have been very skeptical about VR and AR in the past because despite grand promises about what they would achieve, they’ve mostly failed to deliver on the scale that the industry hoped for.

But it’s different this time: industry leaders like Meta, Apple, and Microsoft are pursuing a range of different mixed reality projects; they see the opportunity and are dropping hints about what’s next.

In a survey from Perkins Coie LLP and the XR Association, nearly 9 in 10 respondents said that by the year 2025, immersive technologies—including augmented reality, virtual reality, and mixed reality — will be as ubiquitous as mobile devices.

That’s a bold prediction, but it could be our new reality.

Use Cases

VR and AR aren’t a logical fit for every website, and that’s fine. There’s no need to force an immersive experience on something better suited to a standard viewing experience.

But when they’re done right, 3D experiences can add a lot to your website. Check out the demo experience from Mozilla, the 3D tours from Matterport, and the immersive storytelling from Within.

Here are a few areas where these technologies shine:

  • Retail – VR can be used to provide a virtual showroom where customers browse through products. AR can even bring the products into your home by showing you how a piece of furniture will fit in your room, what a painting will look like on your wall, or in Apple’s case, how a product will look on your desk.
  • News – Coverage of events can be enriched by providing a 360-degree view and placing viewers in the center of the story.
  • Training – AR can generate virtual overlays over physical equipment so employees can have hands-on training that’s more effective.

Define Your Platform

Adding immersive experiences to your website will require various skills based on what you’re trying to create. Whether you’re new to web development or are a seasoned developer with many years of experience, the main difference from classic web development is that you’re switching from a 2D to a 3D experience. Development in VR/AR is much closer to developing 3D video games than creating web applications.

First of all, you need to decide on the hardware that you’re building for. Are your viewers mainly using computers, smartphones, or a headset like the Oculus Quest? Each hardware category offers a different set of capabilities for what’s possible.

Next, when we look at 3D engines and frameworks on the market, some big names like Unity, Unreal Engine, and CRYENGINE stand out. Most of these engines were spun out of game development and are based on programming languages like C, C++, or C#. While very powerful, they’re overkill for anyone trying to create a basic immersive web experience.

The good news for web developers is that the WebXR Device API is an open standard specified by the W3C with a JavaScript API that makes immersive experiences possible in the browser. So if you already have a background in web development, you can use your knowledge of JavaScript to get started.

There are some useful frameworks and platforms that make working with WebXR more convenient:

  • A-Frame – A web framework for building 3D experiences.
  • React 360 – A framework for the creation of interactive 360-degree experiences that run in the web browser. As the name already suggests, it builds on React and reuses the concepts you already know.
  • Amazon Sumerian – A managed service that lets you create and run 3D, AR, and VR applications. Since it’s integrated into the AWS ecosystem, it’s also possible to add AI-enabled elements into your generated world.

Create Your Content

No one wants to read long blocks of text in 3D. Since we’re talking about visual experiences, it’s logical that the emphasis should be on creating content that is pleasing to the eye and interesting to look at. What works on a normal website probably isn’t going to feel natural in a 3D environment, so you need to decide what visuals you should create to suit the format.

What high-resolution images and assets do you need? Can you add videos? How about 360-degree videos? Will viewers just be looking at something, or will they be able to interact with it?

You also can’t forget about sound because it’s a critical part of immersive experiences. What music and sounds should you create to make the content come alive?

Not everyone is going to have the latest and greatest device or 5G coverage. The requirements for bandwidth and transmission quality are much higher with 3D content. A few milliseconds of latency can go unnoticed on a typical website, but in a VR/AR setting, it can make the experience laggy or unusable.

Try to optimize your content to be the highest quality it can be within a reasonable file size. If the experience starts to suffer from too many assets downloading at the same time, it’s better to create a more streamlined experience that maintains a high performance rate.

It’s important to consider your hosting infrastructure, as well. This shouldn’t be a big problem, but it is worth mentioning that you need to add new content types to your configurations, and your CDN needs to support these new types, too.

Make Your Content Flexible

When we’re talking about getting your website ready for immersive experiences, we’re not just talking about having people scroll through your regular website in VR. That isn’t compelling for your audience.

The idea is to take some content that’s already on your website and separate it from the presentation layer so you can use it in a 3D environment or any other platform that you want. Classic content management takes place in silos, which means you cannot easily reuse the content from your website.

This separation can be achieved by using a classic database, but if you want developers and content teams to collaborate, a headless CMS is front-end agnostic and more user friendly.

Start Experimenting Today

Building 3D content experiences may seem intimidating, but as we’ve seen, you likely already have the web development skills necessary to get started and try out some different ideas.

What you build today will prepare you for the 3D future of tomorrow.

 

Featured image via Pexels.

Source

The post How to Prepare for the Immersive Web first appeared on Webdesigner Depot.

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Low Code and No-Code Considerations

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Low-code and no-code development platforms have been the trending IT topic for some time now. Experts in this field speculate as to how they could change the future of the industry. Some publications predict the gradual decline of traditional IT, as low-code and no-code products continue to reshape the very approach to solutions development. But, before we get into a discussion on the future of low-code and no-code, let’s define what these terms actually mean.

What Is Low-Code/No-Code?

Low-code and no-code tools help scale and maximize software delivery. The grid below explains, defines, and differentiates low-code vs. no-code tools.

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Choosing a PaaS Provider To Empower, Not To Limit You

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As cloud computing continues exponential growth and adoption, numerous deployment strategies and models have emerged to meet the increasing demand and diverse needs of cloud users. PaaS (platform as a service) is a cloud computing model that allows you to develop, run, deploy, and launch applications and services over the internet without the constraints, complexity, and cost of building and managing the underlying cloud infrastructure—including storage, operating systems, servers, networks, and more.

PaaS provides the fundamental benefits of cloud computing—from turnkey provisioning and disaster recovery to on-demand scalability and transparent pricing—all consistently managed. And, PaaS vendors provide developers with the tools and infrastructure to run needed programming languages and technology stacks.

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Top Automation Programming Languages of 2021

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Introduction

In today’s highly competitive world, software development and automation play a significant role in creating robust software applications for businesses. Additionally, emerging technologies like artificial intelligence and blockchain have given a competitive edge to enterprises. For gaining maximum benefits out of automation testing, testers require hands-on experience in a minimum of one automation programming language. There are numerous programming languages available today, with new ones continuously emerging. No matter which phase you are in, whether starting with automation testing or being an experienced tester planning to learn a new programming language, deciding which language to choose is very critical.

Which Automation Programming Language Is the Best for Testing?

The following list is prepared after considering metrics like recent trends, language popularity, career prospects, open-source projects, and more. As per TIOBE Index 2021 and IEEE Spectrum Magazine, Java, C, and Python are the top three automation programming languages on the list. Following are some of the most preferred languages on the whole entire list.

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Should Perl Die Gracefully?

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Cover: Should Perl Die Gracefully?

A comment on the Medium version of last week’s article got to me:

I wish they’d just leave Perl static. Then we wouldn’t have to waste money retesting legacy Perl apps on the newest version every couple of years, in case new features we don’t want breaks code that’s been unchanged for years. […] Sometimes things should just be left to die gracefully.

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No-Code:  »It’s a Trap! »

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Gartner predicts that by 2023, over 50% of medium to large enterprises will have adopted a Low-code/No-code application as part of their platform development.
The proliferation of Low-code/No-code tooling can be partially attributed to the COVID-19 pandemic, which has put pressure on businesses around the world to rapidly implement digital solutions. However, adoption of these tools — while indeed accelerated by the pandemic — would have occurred either way.
Even before the pandemic, the largest, richest companies had already formed an oligopsony around the best tech talent and most advanced development tools. Low-Code/No-code, therefore, is an attractive solution for small and mid-sized organizations to level the playing field, and it does so by giving these smaller players the power to do more with their existing resources.
While these benefits are often realized in the short term, the long-term effect of these tools is often shockingly different. The promise of faster and cheaper delivery is the catch — or lure — inside this organizational mousetrap, whereas backlogs, vendor contracts, technical debts, and constant updates are the hammer.
So, what exactly is the No-Code trap, and how can we avoid it?

What is a No-Code Tool?

First, let’s make sure we clear up any confusion regarding naming. So far I have referred Low-Code and No-Code as if they were one term. It’s certainly easy to confuse them — even large analyst firms seem to have a hard time differentiating between the two — and in the broader context of this article, both can lead to the same set of development pitfalls.
Under the magnifying glass, however, there are lots of small details and capabilities that differentiate Low-code and No-code solutions. Most of them aren’t apparent at the UI level, leading to much of the confusion between where the two come from.
In this section, I will spend a little bit of time exploring the important differences between those two, but only to show that when it comes to the central premise of this article they are virtually equivalent.

Low-Code vs. No-Code Tools

The goal behind Low-Code is to minimize the amount of coding necessary for complex tasks through a visual interface (such as Drag ‘N’ Drop) that integrates existing blocks of code into a workflow.
Skilled professionals have the potential to work smarter and faster with Low-Code tools because repetitive coding or duplicating work is streamlined. Through this, they can spend less time on the 80% of work that builds the foundation and focuses more on optimizing the 20% that makes it different. It, therefore, takes on the role of an entry-level employee doing the grunt work for more senior developers/engineers.
No-Code has a very similar look and feel to Low-Code, but is different in one very important dimension. Where Low-Code is meant to optimize the productivity of developers or engineers that already know how to code (even if just a little), No-Code is built for business and product managers that may not know any actual programming languages. It is meant to equip non-technical workers with the tools they need to create applications without formal development training.
No-Code applications need to be self-contained and everything the No-Code vendor thinks the user may need is already built into the tool.
As a result, No-Code applications create a lot of restrictions for the long-term in exchange for quick results in the short-term. This is a great example of a ‘deliberate-prudent’ scenario in the context of the Technical Debt Quadrant, but more on this later.

Advantages of No-Code Solutions

The appeal of both Low-Code and No-Code is pretty obvious. By removing code organizations can remove those that write it — developers — because they are expensive, in short supply, and fundamentally don’t produce things quickly.
The benefits of these two forms of applications in their best forms can be pretty substantial:
  • Resources: Human Capital is becoming increasingly scarce — and therefore expensive. This can stop a lot of ambitious projects dead in their tracks. Low-Code and No-Code tools minimize the amount of specialized technical skills needed to get an application of the ground, which means things can get done more quickly and at a lower cost.
  • Low Risk/High ROI: Security processes, data integrations, and cross-platform support are all built into Low-Code and No-Code tools, meaning less risk and more time to focus on your business goals.
  • Moving to Production: Similarly, for both types of tools a single click is all it takes to send or deploy a model or application you built to production.
Looking at these advantages, it is no wonder that both Low-Code and No-Code have been taking industries by storm recently. While being distinctly different in terms of users, they serve the same goal — that is to say, faster, safer and cheaper deployment. Given these similarities, both terms will be grouped together under the ‘No-Code’ term for the rest of this article unless otherwise specified.

List of No-Code Data Tools

So far, we have covered the applications of No-Code in a very general way, but for the rest of this article, I would like to focus on data modeling. No-Code tools are prevalent in software development, but have also, in particular, started to take hold in this space, and some applications even claim to be an alternative to SQL and other querying languages (crazy, right?!). My reasons for focusing on this are two-fold: 
Firstly, there is a lot of existing analysis around this problem for software development and very little for data modeling. Secondly, this is also the area in which I have the most expertise.
Now let’s take a look at some of the vendors that provide No-Code solutions in this space. These in no way constitute a complete list and are, for the most part, not exclusively built for data modeling. 

1. No-Code Data Modeling in Power BI

Power BI was created by Microsoft and aims to provide interactive visualizations and business intelligence capabilities to all types of business users. Their simple interface is meant to allow end-users to create their own reports and dashboards through a number of features, including data mapping, transformation, and visualization through dashboards. Power BI does support some R coding capabilities for visualization, but when it comes to data modeling, it is a true No-Code tool.

2. Alteryx as a Low-Code Alternative

Alteryx is meant to make advanced analytics accessible to any data worker. To achieve this, it offers several data analytics solutions. Alteryx specializes in self-service analytics with an intuitive UI. Their offerings can be used as Extract, Transform, Load (ETL) Tools within their own framework. Alteryx allows data workers to organize their data pipelines through their custom features and SQL code blocks. As such, they are easily identified as a Low-Code solution.

3. Is Tableau a No-Code Data Modeling Solution?

Tableau is a visual analytics platform and a direct competitor to Power BI. They were recently acquired by Salesforce which is now hoping to ‘transform the way we use data to solve problems—empowering people and organizations to make the most of their data.’ It is also a pretty obvious No-Code platform that is supposed to appeal to all types of end-users. As of now, it offers fewer tools for data modeling than Power BI, but that is likely to change in the future.

4. Looker is a No-Code Alternative to SQL

Looker is a business intelligence software and big data analytics platform that promises to help you explore, analyze, and share real-time business analytics easily. Very much in line with Tableau and Power BI, it aims to make non-technical end-users proficient in a variety of data tasks such as transformation, modeling, and visualization.

You might be wondering why I am including so many BI/Visualization platforms when talking about potential alternatives to SQL. After all, these tools are only set up to address an organization’s reporting needs, which constitute only one of the use cases for data queries and SQL. This is certainly a valid point, so allow me to clarify my reasoning a bit more.

While it is true that reporting is only one of many potential uses for SQL, it is nevertheless an extremely important one. There is a good reason why there are so many No-Code BI tools in the market—to address heightening demand from enterprises around the world — and therefore, it is worth taking a closer look at their almost inevitable shortcomings.

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Why Python Is Best for Machine Learning

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Today, most companies are using Python for AI and Machine Learning. With predictive analytics and pattern recognition becoming more popular than ever, Python development services are a priority for high-scale enterprises and startups. Python developers are in high-demand — mostly because of what can be achieved with the language. AI programming languages need to be powerful, scalable, and readable. Python code delivers on all three.

While there are other technology stacks available for AI-based projects, Python has turned out to be the best programming language for this purpose. It offers great libraries and frameworks for AI and Machine Learning (ML), as well as computational capabilities, statistical calculations, scientific computing, and much more. 

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