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Column 1 | Column 2 | Column 3 | origin_pattern | origin_url | createdAt | updatedAt | pingedAt |
---|---|---|---|---|---|---|---|
Data Engineering Foundations | Data Engineering Foundations | https://www.coursera.org/search?query=data%20engineering& | https://www.coursera.org/search?query=data%20engineering& | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | |
Data Engineering, Big Data, and Machine Learning on GCP | Data Engineering, Big Data, and Machine Learning on GCP | https://www.coursera.org/search?query=data%20engineering& | https://www.coursera.org/search?query=data%20engineering& | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | |
Data Warehousing for Business Intelligence | Data Warehousing for Business Intelligence | https://www.coursera.org/search?query=data%20engineering& | https://www.coursera.org/search?query=data%20engineering& | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | |
IBM Data Engineering | IBM Data Engineering | https://www.coursera.org/search?query=data%20engineering& | https://www.coursera.org/search?query=data%20engineering& | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | |
IBM Data Warehouse Engineer | IBM Data Warehouse Engineer | https://www.coursera.org/search?query=data%20engineering& | https://www.coursera.org/search?query=data%20engineering& | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | |
IBM DevOps and Software Engineering | IBM DevOps and Software Engineering | https://www.coursera.org/search?query=data%20engineering& | https://www.coursera.org/search?query=data%20engineering& | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | |
Introduction to Data Engineering | Introduction to Data Engineering | https://www.coursera.org/search?query=data%20engineering& | https://www.coursera.org/search?query=data%20engineering& | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | |
Machine Learning Engineering for Production (MLOps) | Machine Learning Engineering for Production (MLOps) | https://www.coursera.org/search?query=data%20engineering& | https://www.coursera.org/search?query=data%20engineering& | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | |
Microsoft Azure Data Engineering Associate DP-203 Exam Prep | Microsoft Azure Data Engineering Associate DP-203 Exam Prep | https://www.coursera.org/search?query=data%20engineering& | https://www.coursera.org/search?query=data%20engineering& | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | |
Preparing for Google Cloud Certification: Cloud Data Engineer | Preparing for Google Cloud Certification: Cloud Data Engineer | https://www.coursera.org/search?query=data%20engineering& | https://www.coursera.org/search?query=data%20engineering& | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC | 2022-02-04 06:25:14 UTC |
Sample code snippets to quickly import data set into your application
For more information on how to automatically trigger an import please reference our WebHook API guide
Integrating with Java
import java.io.BufferedReader; import java.io.InputStreamReader; import java.net.URL; import java.net.URLConnection; import java.util.Arrays; public class HelloWorld { public static void main(String[] args) { try { URL urlCSV = new URL( "https://cache.getdata.io/n101979_29727843682b8b460480b07bb76c7118eses/latest_all.csv" ); URLConnection urlConn = urlCSV.openConnection(); InputStreamReader inputCSV = new InputStreamReader( ((URLConnection) urlConn).getInputStream() ); BufferedReader br = new BufferedReader(inputCSV); String line; String[] fields; while ((line = br.readLine()) != null) { // Each row fields = line.split(","); System.out.println(Arrays.toString(fields)); } // clean up buffered reader br.close(); } catch (Exception e) { System.out.println(e.getMessage()); } } }
Integrating with NodeJs
const csv = require('csv-parser'); const https = require('https'); const fs = require('fs'); const file = fs.createWriteStream("temp_download.csv"); const request = https.get( "https://cache.getdata.io/n101979_29727843682b8b460480b07bb76c7118eses/latest_all.csv", function(response) { response.pipe(file); } ); file.on('finish', function() { file.close(); fs.createReadStream('temp_download.csv').pipe(csv()).on('data', (row) => { // Each row console.log(row); }).on('end', () => { console.log('CSV file successfully processed'); }); });
Integrating with PHP
$data = file_get_contents("https://cache.getdata.io/n101979_29727843682b8b460480b07bb76c7118eses/latest_all.csv"); $rows = explode("\n",$data); $s = array(); foreach($rows as $row) { # Each row var_dump( $row); }
Integrating with Python
import csv import urllib2 url = 'https://cache.getdata.io/n101979_29727843682b8b460480b07bb76c7118eses/latest_all.csv' response = urllib2.urlopen(url) cr = csv.reader(response) for row in cr: # Each row print row
Integrating with Ruby
require 'open-uri' require 'tempfile' require 'csv' temp_file = Tempfile.new( "getdata", :encoding => 'ascii-8bit') temp_file << open("https://cache.getdata.io/n101979_29727843682b8b460480b07bb76c7118eses/latest_all.csv").read temp_file.rewind CSV.foreach( open(uri), :headers => :first_row ).each do |row| # Each row puts row end
created on 2024-11-16
created on 2024-11-16
created on 2024-11-15