How to Integrate Data Table with the Angular 8 Application With JSON backend

DataTables is a plug-in for the JQuery JavaScript library. It is a highly flexible tool, build upon the foundations of progressive enhancement, that adds all of these advanced features to any HTML table.

Create Project:

Create a new project using angular CLI generator. using the following command to create new project.

ng new angularDatatable

Install Dependencies:

Install the following dependencies using npm.

npm install jquery --save
npm install datatables.net --save
npm install datatables.net-dt --save
npm install @types/jquery --save-dev
npm install @types/datatables.net --save-dev

project Configuration:

you need to import the jquery and datatable scripts files into angular.json file. Into the styles section you need to import CSS files. Into the script section you need to import JS files. Example code is given below.

angular.json

"styles": [
"node_modules/datatables.net-dt/css/jquery.dataTables.css"
],
"scripts": [
"node_modules/jquery/dist/jquery.js",
"node_modules/datatables.net/js/jquery.dataTables.js"
]

create a new component using the following command

ng generate component Jsondata

Your component template should be like the following

<table #dataTable class="display" style="width:100%">
</table>

Create a modal using the following command

ng g m resDataModal

Create a new service using the following command

ng g s Datasource

After creating this your folder structure like this

Your Modal file be like this

resDataModal.ts

export interface ResDataModal {
  page: number;
  per_page: number;
  total: number;
  total_pages: number;
  data: [];
}

Your service file should like this
datasource.service.ts

import { Injectable } from '@angular/core';
import {HttpClient} from '@angular/common/http';
import {Observable} from 'rxjs';
import {ResDataModal} from '../modals/resDataModal';

@Injectable({
  providedIn: 'root'
})
export class DatasourceService {
  apiUrl = 'https://reqres.in/api/users?page=2';

  constructor(private http: HttpClient) { }

  getData(): Observable<ResDataModal> {
    return this.http.get<ResDataModal>(this.apiUrl);
  }
}

Your component file be like this

import {Component, OnInit, ViewChild} from '@angular/core';
import {DatasourceService} from '../services/datasource.service';
declare var $;
@Component({
  selector: 'app-json-data',
  templateUrl: './json-data.component.html',
  styleUrls: ['./json-data.component.scss']
})
export class JsonDataComponent implements OnInit {

  dataTable: any;
  dtOptions: any;
  tableData = [];
  @ViewChild('dataTable', {static: true}) table;

  constructor(private dataService: DatasourceService) { }

  ngOnInit() {
    this.getDataFromSource();
  }

  getDataFromSource() {
    this.dataService.getData().subscribe(data => {
      this.tableData = data.data;
      this.dtOptions = {
        data: this.tableData,
        columns: [
          {title: 'ID', data: 'id'},
          {title: 'Email', data: 'email'},
          {title: 'First Name', data: 'first_name'},
          {title: 'Last Name', data: 'last_name'},
          {title: 'Avatar', data: 'avatar'},
        ]
      };
    }, err => {}, () => {
      this.dataTable = $(this.table.nativeElement);
      this.dataTable.DataTable(this.dtOptions);
    });
  }

}

Thanks for reading !

Originally published by Sathish kumar Ramalingam at medium

#JavaScript #Angular8 #Datatables #json #angular

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Your Data Architecture: Simple Best Practices for Your Data Strategy

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.

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Data Quality Testing Skills Needed For Data Integration Projects

The impulse to cut project costs is often strong, especially in the final delivery phase of data integration and data migration projects. At this late phase of the project, a common mistake is to delegate testing responsibilities to resources with limited business and data testing skills.

Data integrations are at the core of data warehousing, data migration, data synchronization, and data consolidation projects.

In the past, most data integration projects involved data stored in databases. Today, it’s essential for organizations to also integrate their database or structured data with data from documents, e-mails, log files, websites, social media, audio, and video files.

Using data warehousing as an example, Figure 1 illustrates the primary checkpoints (testing points) in an end-to-end data quality testing process. Shown are points at which data (as it’s extracted, transformed, aggregated, consolidated, etc.) should be verified – that is, extracting source data, transforming source data for loads into target databases, aggregating data for loads into data marts, and more.

Only after data owners and all other stakeholders confirm that data integration was successful can the whole process be considered complete and ready for production.

#big data #data integration #data governance #data validation #data accuracy #data warehouse testing #etl testing #data integrations

What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

• ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
• ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

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Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

• Industrial metrology for quality assurance.
• 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

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Wondering how to upgrade your skills in the pandemic? Here's a simple way you can do it.

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