Tutorials on Postgresql

This article will provide a mildly thorough comparison of TailwindCSS vs. Bootstrap to help you determine the more suitable framework for your web development needs. TailwindCSS: As a utility-first CSS framework, TailwindCSS enables developers to construct custom designs without writing bespoke CSS. It provides low-level utility classes that can be combined to create any design directly in the HTML. Bootstrap: As one of the most widely-used CSS frameworks, Bootstrap offers pre-designed components and a grid system. It is designed to aid developers in rapidly creating responsive and consistent web interfaces.

Type safety is a critical feature of TypeScript that aids in preventing runtime errors and boosting developer productivity. In this article, I will dive into how tRPC, a TypeScript RPC framework known for its type safety, builds upon this feature. I’ll be discussing the fundamental concepts of TypeScript type safety, the advantages of tRPC, and its role in augmenting type safety in projects as well. My goal is to give readers a comprehensive understanding of how tRPC can enhance development workflows and minimize technical debt.

This article will dive into a development stack that is gaining traction due to its robustness and adaptability. We'll explore what sets this stack apart from well-established stacks like MEAN and MERN, and why developers should consider its adoption. The cutting-edge stack we're exploring comprises several technologies that, although not entirely new, are combined uniquely to boost development efficiency and code quality. This modern stack includes: This stack facilitates enhanced type safety, seamless management of monorepo structures, shared configurations across packages, and a streamlined frontend setup with Vite, React, and Tailwind. Moreover, this stack enables database migration with raw SQL and access via knex.js , using tRPC as the API layer and Koa as the backend framework.

Welcome to this introductory guide on leveraging tRPC for full-stack development. With TypeScript's increasing popularity, tRPC has surfaced as a modern standard that empowers developers to achieve automatic type safety, an optimized developer experience, and exceptional compatibility with existing JavaScript frameworks. This guide aims to introduce you on integrating tRPC into your web stack and its efficient usage with PostgreSQL's modern features and TailwindCSS. This educational article is specifically designed for individuals who:

Are you ready to dive into the world of powerful database management with PostgreSQL and NodeJS? This guide is designed for beginners who want to understand how to use PostgreSQL in their NodeJS projects. Whether you're new to databases or looking to expand your skills, this tutorial will help you get started with confidence. In this guide, we'll cover: By the end of this tutorial, you'll have a solid foundation for building applications with PostgreSQL and NodeJS. For a more visual representation and in-depth details, you may refer to this video tutorial by me, Kristian Dupont, on \newline’s YouTube channel.

If you are using PostgreSQL for storing data of multiple users, you might want to apply row-level security, or RLS. It’s good practice even if you are manually writing all the queries you send to your database but it’s especially important if you have any type of LLM or similar generating queries for you! Let’s create a trivial data model. Users and items, whatever that might be. Each item belongs to a user. Now, per default, if you ask the database about any users items, it will just tell you. By introducing RLS, you can limit what the responses will be to add a layer of protection. Even if you should create a buggy query, you will not accidentally get the items belonging to someone else, just like you cannot accidentally change or delete items belonging to someone else. We do that like this:

Most modern web applications rely on a relational database management system (RDBMS) to store and retrieve troves of related data. When you bring together two modern, open-source technologies, Deno and PostgreSQL , you can build production-grade web applications that... Since Deno was built as an improved, alternative V8 runtime to Node.js, Deno projects can use both the third-party modules developed specifically to work with Deno and the many third-party packages that exist in the npm ecosystem. A popular PostgreSQL client for Node.js projects is node-postgres . If you visit the deno.land/x website and search postgres , then you will come across deno-postgres , a lightweight PostgreSQL driver that was developed specifically for Deno. The API and underlying implementation of deno-postgres is based on two popular PostgreSQL drivers: node-postgres (coincidentally) and pq , a Go PostgreSQL driver. deno-postgres provides abstractions for features and operations that allow you to unleash the full potential of PostgreSQL: connection pools, prepared statements, transactions and typed queries.

Analytics dashboards display different data visualizations to represent and convey data in ways that allow users to quickly digest and analyze information. Most multivariate datasets consumed by dashboards include a spatial field/s, such as an observation's set of coordinates (latitude and longitude). Plotting this data on a map visualization contextualizes the data within a real-world setting and sheds light on spatial patterns that would otherwise be hidden in the data. Particularly, seeing the distribution of your data across an area connects it to geographical features and area-specific data (i.e., neighborhood/community demographics) available from open data portals. The earliest example of this is the 1854 cholera visualization by John Snow , who marked cholera cases on a map of London's Soho and uncovered the source of the cholera outbreak by noticing a cluster of cases around a water pump. This discovery helped to correctly identify cholera as a waterborne disease and not as an airbourne disease. Ultimately, it changed how we think about disease transmission and the impact our surroundings and environment have on our health. If your data consists of spatial field/s, then you too can apply the simple technique of plotting markers on a map to extrapolate valuable insight from your own data. Map visualizations are eye-catching and take on many forms: heatmaps, choropleth maps, flow maps, spider maps, etc. Although colorful and aesthetically pleasing, these visualizations provide intuitive controls for users to navigate through their data with little effort. To create a map visualization, many popular libraries (e.g., Google Maps API and deck.gl ) support drawing shapes, adding markers and overlaying geospatial visualization layers on top of a set of base map tiles. Each layer generates a pre-defined visualization based on a collection of data. It associates each data point with certain attributes (color, size, etc.) and renders them on to a map.

Serving a web application to a global audience requires deploying, hosting and scaling it on reliable cloud infrastructure. Heroku is a cloud platform as a service (PaaS) that supports many server-side languages (e.g., Node.js, Go, Ruby and Python), monitors application status in a beautiful, customizable dashboard and maintaining an add-ons ecosystem for integrating tools/services such as databases, schedulers, search engines, document/image/video processors, etc. Although it is built on AWS, Heroku is simpler to use compared to AWS. Heroku automatically provisions resources and configures low-level infrastructure so developers can focus exclusively on their application without the additional headache of manually setting up each piece of hardware and installing an operating system, runtime environment, etc. When deploying to Heroku, Heroku's build system packages the application's source code and dependencies together with a language runtime using a buildpack and slug compiler to generate a slug , which is a highly optimized and compressed version of your application. Heroku loads the slug onto a lightweight container called a dyno . Depending on your application's resource demands, it can be scaled horizontally across multiple concurrent dynos. These dynos run on a shared host, but the dynos responsible for running your application are isolated from dynos running other applications. Initially, your application will run on a single web dyno, which serves your application to the world. If a single web dyno cannot sufficiently handle incoming traffic, then you can always add more web dynos. For requests exceeding 500ms to complete, such as uploading media content, consider delegating this expensive work as a background job to a worker dyno. Worker dynos process these jobs from a job queue and run asynchronously to web dynos to free up the resources of those web dynos.

From businesses looking to optimize their operations, data influences the decisions being made. For scientists looking to validate their hypotheses, data influences the conclusions being arrived at. Regardless, the sheer amount of data collected and harnessed from various sources presents the challenge of identifying rising trends and interesting patterns hidden within this data. If the data is stored within an SQL database, such as PostgreSQL , querying data with the expressive power of the SQL language unlocks the data's underlying value. Creating interfaces to fully leverage the constructs of SQL in analytics dashboards can be difficult if done from scratch. With a library like React Query Builder , which contains a query builder component for fetching and exploring rows of data with the exact same query and filter rules provided by the SQL language, we can develop flexible, customizable interfaces for users to easily access data from their databases. Although there are open source, administrative tools like pgAdmin , these tools cannot be integrated directly into a custom analytics dashboard (unless embedded within an iframe). Additionally, you would need to manage more user credentials and permissions, and these tools may be considered too overwhelming or technical for users who aren't concerned with advanced features, such as a procedural language debugger, and intricate back-end and database configurations. By default, the <QueryBuilder /> component from the React Query Builder library contains a minimal set of controls only for querying data with pre-defined rules. Once the requested data is queried, this data can then be summarized by rendering it within a data visualization, such as a table or a line graph.