Tutorials on Express Js

Accessing an e-mail account anywhere in the world on any device requires authenticating yourself to prove the data associated with the account (e.g., e-mail address and inbox messages) actually belongs to you. Often, you must fill out a login form with credentials, such as an e-mail address and password, that uniquely identify your account. When you first create an account, you provide this information in a sign-up form. In some cases, the service sends either a confirmation e-mail or an SMS text message to ensure that you own the supplied e-mail address or phone number. Because it is highly likely that only you know the credentials to your account, authentication prevents unwanted actors from accessing your account and its data. Each time you log into your e-mail account and read your most recent unread messages, you, and like many other end users, don't think about how the service implements authentication to protect/secure your data and hide your activity history. You're busy, and you only want to spend a few minutes in your e-mail inbox before closing it out and resuming your day. For developers, the difficulty in implementing authentication comes from striking a balance between the user experience and the strength of the authentication. For example, a sign up form may prompt the user to enter a password that contains not only alphanumeric characters, but also must meet other requirements such as a minimum password length and containing punctuation marks. Asking for a stronger password decreases the likelihood of a malicious user correctly guessing it, but simultaneously, this password is increasingly more difficult for the user to remember. Keep in mind that poorly designed authentication can easily be bypassed and introduce more vulnerabilities into your application. In most cases, applications implement either session-based or token-based authentication to reliably verify a user's identity and persist authentication for subsequent page visits. Since Go is a popular choice for building server-side applications, Go's ecosystem offers many third-party packages for implementing these solutions into your applications.

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 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.