parj

package io.github.parj.sql;

import org.apache.calcite.config.Lex;
import org.apache.calcite.sql.*;
import org.apache.calcite.sql.fun.SqlStdOperatorTable;
import org.apache.calcite.sql.parser.SqlParseException;
import org.apache.calcite.sql.parser.SqlParser;

import java.util.HashMap;
import java.util.List;

parj

# (optional) if using minikube switch docker context
eval $(minikube docker-env)

# Build Spark
bin/docker-image-tool.sh -r docker.io/myrepo -t v3.5 -p kubernetes/dockerfiles/spark/bindings/python/Dockerfile build

# Expose K8S API server (minikube)
kubectl proxy --port=8080

#Create spark service account and give permissions

parj

Have you ever wondered how computer programs talk to databases? Well, that's where JDBC comes to the rescue! JDBC, which stands for "Java Database Connectivity," is like a magical translator between Java programs and databases. It helps them understand each other's language!

Imagine your program wants to store or get some data from a database. JDBC is the friendly bridge that helps them shake hands and exchange information. It makes life easier for developers by handling all the complicated stuff, so they can focus on building awesome apps. Thanks to JDBC, your favorite apps can store and fetch data, making them super cool and smart!

parj

import com.google.gson.Gson;
import com.google.gson.GsonBuilder;
import java.io.BufferedWriter;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.OutputStreamWriter;
import java.sql.*;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

parj

import java.io.BufferedWriter;
import java.io.FileWriter;
import java.io.IOException;
import java.sql.*;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

public class DatabaseToJson {
    private static final int PAGE_SIZE = 10000;
    private static final String DB_URL = "jdbc:mysql://localhost:3306/mydatabase";

parj

import java.io.BufferedWriter;
import java.io.FileWriter;
import java.sql.*;
import org.json.*;

public class DatabaseToJson {
    private static final String DB_URL = "jdbc:mysql://localhost/mydatabase";
    private static final String DB_USER = "myusername";
    private static final String DB_PASSWORD = "mypassword";
    private static final int FETCH_SIZE = 1000;

parj

import java.io.BufferedWriter;
import java.io.FileWriter;
import java.io.IOException;
import java.io.Writer;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;
import java.util.concurrent.ExecutorService;

parj

import java.io.FileWriter;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import org.json.simple.JSONArray;
import org.json.simple.JSONObject;

parj

import com.fasterxml.jackson.databind.ObjectMapper;
import java.io.File;
import java.sql.*;
import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

public class DatabaseToJson {
    private static final String DB_URL = "jdbc:mysql://localhost:3306/mydatabase";

parj

#First install beautiful soup. Example
# pip3 install beautifulsoup4
import requests
from bs4 import BeautifulSoup

url = 'https://azure.microsoft.com/en-gb/services/'
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")

for service in soup.find_all("div", class_="column medium-6 end"):