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With the introduction of the internet, consumers are more vocal about their thoughts and experiences related to any particular brand or product. Online reviews provide them with the opportunity to express themselves and provide their feedback. As reviews help consumers to make informed decisions, they can similarly be leveraged to help brands display their credibility. […]
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While load testing may sound like an esoteric domain exclusive to software engineers or network administrators, it is, in fact, a silent superhero in our increasingly digital world. Acting behind the scenes, load testing ensures the apps and websites we use daily are capable of withstanding the demands of their users without stumbling.
It's the silent force keeping the digital infrastructure wheel rotating smoothly, even during peak usage times. Intrigued? Let's dive into the world of load testing, examining its significance in our digital lives and how it impacts the software and systems we interact with daily.
In this article, I will show you how to quickly create an entirely free app using a JavaScript program that runs within the free Oracle database and calls Hugging Face AI, the results of which are stored in the database and can then be accessed using SQL, JSON, REST, or any other language. All of the source code is available here.
A common flow of applications in general and AI applications specifically involves an app calling an AI service and then storing the information in the database where it may be retrieved and processed (e.g., using OML) further and/or analyzed and queried later.
As a developer, one of your greatest fears is likely to develop software with bugs. This fear results from the knowledge that even a minor error or oversight in the code could have serious repercussions, causing system failures, data loss, or security breaches. Hence, we imagine that you work tirelessly to reduce the likelihood of such errors by writing clear and effective code and doing exhaustive testing.
However, do you know that you can minimize the risk of such errors to a great extent and ensure your software adheres to high standards giving users a dependable, secure, and convenient experience? Well, Yes, you can! Especially if you follow the ten best practices for developing high-quality software, which we will discuss in this article.
There are many ways to evaluate poor data, but the following approach has proved to be the most effective and universal in practice.
To weed out poor data, you need to:
In the world of software development, the ability to wear different hats is a valuable skill. This is especially true when developing full-stack applications.
So, what is required when building a full-stack application? Firstly, one needs to design and build the application (frontend and backend) itself. Then one has to create and configure the backend infrastructure, and finally, host the application and connect the frontend and backend so users can access it.
You should be familiar with APIs, especially if you have worked with a developer or designer. But in case you haven't, then let's start by saying that APIs are powerful secret agents that empower mobile apps with useful features and seamless functioning.
Application Programming Interfaces (APIs) play a vital role in shaping the modern digital landscape. APIs help software systems communicate and interact with each other. They also help businesses improve their existing systems and build cutting-edge apps through third-party integrations.
In today's Generative AI world, Vector database has become one of the integral parts while designing LLM-based applications. Whether you are planning to build an application using OpenAI or Google's Generative AI or you are thinking to solve use cases like designing a recommendation engine or building a computer vision (CV) or Vector database, would be an important component to consider.
In the machine learning world, Vector or Embeddings represent the numerical or mathematical representation of data, which can be text, images, or media contents (Audio or Video). LLM from OpenAI or others can transform the regular data into Vector Embeddings with high-level multi-dimensions and store them in the vector space. These numerical forms help determine the semantic meaning among data or identify patterns or clustering, or draw relationships. Regular columnar-based RDBMS or NoSQL databases are not equipped to store Vector Embeddings data with multi-dimensions and efficiently scaling if needed. This is where we need a Vector database, which is a special kind of database that is designed to handle and store this kind of Embeddings data and, at the same time, offers high performance and scalability.
In today's rapidly evolving software development landscape, architects and IT leaders face the critical challenge of designing systems that can adapt, scale, and evolve effectively. As modern architectural practices emphasize the decoupling of domains to achieve a decentralized architecture, it becomes increasingly clear that the topology of development teams and their interactions plays a vital role in the success of architectural design.
The interplay between team topologies and software architecture is an essential factor that architects must consider. A well-structured team topology sets the stage for efficient collaboration, effective communication, and streamlined delivery of software solutions. Without careful consideration of team dynamics and organizational structure, even the most well-conceived architectural designs may encounter obstacles and fall short of their full potential.
Caching plays a vital role in enhancing the performance and scalability of APIs, allowing for quicker retrieval of data and reducing the load on backend systems. By intelligently storing and serving frequently accessed data, caching can significantly improve response times and overall user experience. In this article, we will build upon the insights shared in our earlier blog post and explore different caching strategies that can be employed to optimize API performance further.
Before we dive in, we'd like to acknowledge the inspiration for this article. We draw upon the valuable insights and expertise shared in the blog post titled "Using Caching Strategies to Improve API Performance" by Toro Cloud, a leading provider of API management solutions. If you haven't read their insightful post yet, we highly recommend checking it out.
As technology continues to evolve, businesses are increasingly relying on APIs to connect their applications and services to databases. APIs provide a way to access and manipulate data stored in databases, making it easier to build powerful and flexible applications.
But connecting APIs to databases is not always straightforward, especially when dealing with different types of databases such as SQL, NoSQL, and in-memory databases. Each type of database has its own strengths and weaknesses and requires different approaches to connect with APIs.
Data visualization is a powerful tool for understanding and communicating patterns and insights from data. In Python development services, there are several libraries available for creating compelling visualizations, such as Matplotlib, Seaborn, Plotly, and Bokeh. In this comprehensive guide, we will explore the basics of data visualization in Python and learn how to create various types of visualizations.
1. Installation: Before getting started, make sure you have Python installed on your system. You can install the necessary libraries using pip, the Python package manager. Open a terminal or command prompt and run the following commands:
Over the past few years, while working on the MirAIe IoT platform, we realized the need for an extensible and scalable rule engine framework. A rule engine enables you to group, manage and automate various actions and is used in a variety of applications such as home automation, fraud detection, risk management, and workflow automation. At Panasonic, we are engaged in several initiatives in the fields of mobility, industry 4.0, building management, and home automation. The framework must therefore be adaptable to be used with a variety of applications. In this article, I'll describe a high-level design of our rule engine framework.
A rule essentially allows users to group and automate numerous tasks. Examples of user-generated rules include turning on your bedroom air conditioner whenever the room temperature rises above 27 degrees, turning on a cluster of lights in an office lobby every evening at 6, notifying you when the efficiency of a machine in assembly lines drops below 80 percent, or sending out a push notification to your electric car when it’s battery level drops below 30%, and a charging station with an open slot is found nearby.
REST has become the dominant architecture for API development due to its many benefits. First and foremost, REST is a highly scalable and efficient architecture, making it well-suited for web applications with high traffic and user demand. Additionally, the statelessness of RESTful services allows for greater flexibility and ease of implementation, making it easier for developers to create, update, and maintain APIs. Moreover, RESTful services allow for easy integration with various platforms and technologies, making them highly compatible and versatile. Finally, RESTful services are designed with a focus on simplicity and ease of use, making it easier for developers to build and maintain APIs without the need for complex coding or infrastructure. All of these factors combined have contributed to the popularity of RESTful services as the architecture of choice for API development.
REST stands for Representational State Transfer, and it's an architectural style for designing web services. RESTful endpoints are the individual URLs or entry points for accessing the resources of a RESTful API.
In an ever-evolving field like software development, it's crucial to understand current practices. This post is based on a comprehensive study where we analyzed over a million Git commits.
Rather than making predictions, we've dived into the hard data. We've uncovered actual trends based on real-world Git use.
The Flutter engine represents the core component of the Flutter framework responsible for rendering the user interface (UI) and displaying it on the screen. It maintains a tree of rendered objects, such as widgets and layouts, which are responsible for defining their own layout, painting their visual representation, and handling input events. The rendering engine in Flutter performs two main processes: layout and paint. In the layout phase, the engine analyzes the render object tree to determine the size and position of each element/object based on the constraints provided by the parent. Once the layout phase is complete, the engine analyzes the tree again, instructing each render object to paint itself onto a canvas.
The rendering engine in Flutter takes advantage of hardware acceleration and optimizes performance by minimizing unnecessary repaints. This allows for high-performance graphics rendering and smooth animations. By leveraging the capabilities of the rendering engine, Flutter enables developers to create fast and fluid user interfaces across different platforms and devices.