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https://www.youtube.com/watch?v=lKLC14puqCo
#angular #googlechart #javascript
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Data Science, Data Analytics, Big Data, these are the buzz words of today’s world. A huge amount of data is being generated and analyzed every day. So communicating the insights from that data becomes crucial. Charts help visualize the data and communicate the result of the analysis with charts, it becomes easy to understand the data.
There are a lot of libraries for angular that can be used to build charts. In this blog, we will look at one such library, NGX-Charts. We will see how to use it in angular and how to build data visualizations.
What we will cover:
Installing ngx-chart.
Building a vertical bar graph.
Building a pie chart.
Building an advanced pie chart.
NGX-Chart charting framework for angular2+. It’s open-source and maintained by Swimlane.
NGX-Charts does not merely wrap d3, nor any other chart engine for that matter. It is using Angular to render and animate the SVG elements with all of its binding and speed goodness and uses d3 for the excellent math functions, scales, axis and shape generators, etc. By having Angular do all of the renderings it opens us up to endless possibilities the Angular platform provides such as AoT, Universal, etc.
NGX-Charts supports various chart types like bar charts, line charts, area charts, pie charts, bubble charts, doughnut charts, gauge charts, heatmap, treemap, and number cards.
1. Install the ngx-chart package in your angular app.
npm install @swimlane/ngx-charts --save
2. At the time of installing or when you serve your application is you get an error:
ERROR in The target entry-point "@swimlane/ngx-charts" has missing dependencies: - @angular/cdk/portal
You also need to install angular/cdk
npm install @angular/cdk --save
3. Import NgxChartsModule from ‘ngx-charts’ in AppModule
4. NgxChartModule also requires BrowserAnimationModule. Import is inAppModule.
app.module.ts
import { BrowserModule } from '@angular/platform-browser';
import { NgModule } from '@angular/core';
import { AppComponent } from './app.component';
import { NgxChartsModule }from '@swimlane/ngx-charts';
import { BrowserAnimationsModule } from '@angular/platform-browser/animations';
@NgModule({
declarations: [
AppComponent
],
imports: [
BrowserModule,
BrowserAnimationsModule,
NgxChartsModule
],
providers: [],
bootstrap: [AppComponent]
})
export class AppModule { }
Amazing! Now we can start using ngx-chart component and build the graph we want.
In the AppComponent we will provide data that the chart will represent. It’s a sample data for vehicles on the road survey.
#angular #angular 6 #scala #angular #angular 9 #bar chart #charting #charts #d3 charts #data visualisation #ngx #ngx charts #pie
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Angular 9/10/11 social login with google using angularx-social-login library example. In this tutorial, i will show you step by step on how to implement google social login in angular 11 app.
And also, this tutorial will show you How to login into Angular 10/11 application with google using angularx-social-login library in angular 11 app.
https://www.tutsmake.com/angular-11-google-social-login-example/
#angular 11 google login #angular 11 social-login example #login with google button angular 8/9/10/11 #angular 10/11 login with google #angular 10 social google login #angular social login google
1610191977
Angular 9/10/11 social login with google using angularx-social-login library example. In this tutorial, i will show you step by step on how to implement google social login in angular 11 app.
And also, this tutorial will show you How to login into Angular 10/11 application with google using angularx-social-login library in angular 11 app.
https://www.tutsmake.com/angular-11-google-social-login-example/
#angular 11 google login #angular 11 social-login example #login with google button angular 8/9/10/11 #angular 10/11 login with google #angular 10 social google login #angular social login google
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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
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The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management