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Unfortunately, the API we created is not suitable for anything but the most basic prototyping. For a real API, we will likely want to use our own domain. This appears to be quite complicated in GCP. We will need a Load Balancer, a serverless NEG and an API Gateway among some other components. See Getting started with HTTP(S) Load Balancing for API Gateway and HTTP(S) Load Balancing for API Gateway. - Source: dev.to / over 1 year ago
Set up a Load Balancer and Cloud Armor in front of your function, or. Source: over 1 year ago
In this article, I’ll show you how to configure a global cloud load balancer that serves as both a proxy and a load balancer. This type of load balancer comes with a single IP address that can be accessed from any location on earth and can route a request to the nearest (active!) application instance. - Source: dev.to / over 1 year ago
Cloud Load Balancing for distribution. - Source: dev.to / almost 2 years ago
While the precise features of the application are immaterial, the architecture is of primary importance. A lot of tools (and buzzwords) come to mind when trying to architect a modern web application. Assets can be served from a CDN to improve page load speed. A global load balancer can front all traffic, sending requests to the nearest server. Serverless functions and edge functions can be used to handle requests,... - Source: dev.to / about 2 years ago
How to Accomplish: Utilize visualization libraries like Matplotlib, Seaborn, or Plotly in Python to create histograms, scatter plots, and bar charts. For image data, use tools that visualize images alongside their labels to check for labeling accuracy. For structured data, correlation matrices and pair plots can be highly informative. - Source: dev.to / 24 days ago
For dashboards: - https://plotly.com/ is probably my favourite, but there are others like streamlit, voila and others... Source: 7 months ago
If your CEO wants you to solo build an alternative to Tableau, PowerBi, or even Plotly then consider him/her delusional. Source: about 1 year ago
Python's pandas, NumPy, and SciPy libraries offer powerful functionality for data manipulation, while matplotlib, seaborn, and plotly provide versatile tools for creating visualizations. Similarly, in R, you can use dplyr, tidyverse, and data.table for data manipulation, and ggplot2, lattice, and shiny for visualization. These packages enable you to create insightful visualizations and perform statistical analyses... Source: about 1 year ago
I use plotly and like it a lot. It is slower though. Noticeable if you want to batch-generate a bunch of images and dump them into a folder. But that probably isn't the case most times. Source: over 1 year ago
nginx - A high performance free open source web server powering busiest sites on the Internet.
D3.js - D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.
AWS Elastic Load Balancing - Amazon ELB automatically distributes incoming application traffic across multiple Amazon EC2 instances in the cloud.
Chart.js - Easy, object oriented client side graphs for designers and developers.
Azure Traffic Manager - Microsoft Azure Traffic Manager allows you to control the distribution of user traffic for service endpoints in different datacenters.
Highcharts - A charting library written in pure JavaScript, offering an easy way of adding interactive charts to your web site or web application