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Tuva Shirley
02/24/2025

Collecting E-Commerce Data from Hotline.ua via Go & SQLite: Extracting Popular Products, Flash Sales, and Market Trends for Business Research

In the rapidly evolving world of e-commerce, data is king. Businesses that can effectively collect and analyze data from online platforms like Hotline.ua gain a competitive edge by understanding market trends, consumer preferences, and sales patterns. This article explores how to collect e-commerce data from Hotline.ua using the Go programming language and SQLite database, focusing on extracting popular products, flash sales, and market trends for business research.

Understanding the Importance of E-Commerce Data

E-commerce data provides invaluable insights into consumer behavior, product popularity, and market dynamics. By analyzing this data, businesses can make informed decisions about product offerings, pricing strategies, and marketing campaigns. Hotline.ua, a popular Ukrainian e-commerce platform, offers a wealth of data that can be leveraged for business research.

Collecting data from Hotline.ua allows businesses to track popular products, identify flash sales, and monitor market trends. This information can be used to optimize inventory management, enhance customer experiences, and increase sales. By understanding what products are trending and when flash sales occur, businesses can tailor their strategies to meet consumer demand.

Setting Up the Environment: Go and SQLite

To collect data from Hotline.ua, we will use the Go programming language for its efficiency and concurrency capabilities. Go is well-suited for web scraping tasks due to its robust libraries and ease of use. Additionally, we will use SQLite, a lightweight and self-contained database, to store and manage the collected data.

Before we begin, ensure that Go and SQLite are installed on your system. You can download Go from the official website and SQLite from the SQLite website. Once installed, set up a new Go project and create a SQLite database to store the e-commerce data.

Web Scraping with Go: Extracting Data from Hotline.ua

Web scraping involves extracting data from websites by sending HTTP requests and parsing the HTML content. In Go, we can use the “net/http” package to send requests and the “goquery” package to parse HTML. Let’s start by writing a simple Go program to scrape product data from Hotline.ua.

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package main
import (
"fmt"
"log"
"net/http"
"github.com/PuerkitoBio/goquery"
)
func main() {
// Send HTTP request to Hotline.ua
res, err := http.Get("https://hotline.ua")
if err != nil {
log.Fatal(err)
}
defer res.Body.Close()
// Parse the HTML document
doc, err := goquery.NewDocumentFromReader(res.Body)
if err != nil {
log.Fatal(err)
}
// Extract product data
doc.Find(".product-item").Each(func(index int, item *goquery.Selection) {
productName := item.Find(".product-title").Text()
productPrice := item.Find(".product-price").Text()
fmt.Printf("Product: %s, Price: %sn", productName, productPrice)
})
}
package main import ( "fmt" "log" "net/http" "github.com/PuerkitoBio/goquery" ) func main() { // Send HTTP request to Hotline.ua res, err := http.Get("https://hotline.ua") if err != nil { log.Fatal(err) } defer res.Body.Close() // Parse the HTML document doc, err := goquery.NewDocumentFromReader(res.Body) if err != nil { log.Fatal(err) } // Extract product data doc.Find(".product-item").Each(func(index int, item *goquery.Selection) { productName := item.Find(".product-title").Text() productPrice := item.Find(".product-price").Text() fmt.Printf("Product: %s, Price: %sn", productName, productPrice) }) }
package main

import (
    "fmt"
    "log"
    "net/http"

    "github.com/PuerkitoBio/goquery"
)

func main() {
    // Send HTTP request to Hotline.ua
    res, err := http.Get("https://hotline.ua")
    if err != nil {
        log.Fatal(err)
    }
    defer res.Body.Close()

    // Parse the HTML document
    doc, err := goquery.NewDocumentFromReader(res.Body)
    if err != nil {
        log.Fatal(err)
    }

    // Extract product data
    doc.Find(".product-item").Each(func(index int, item *goquery.Selection) {
        productName := item.Find(".product-title").Text()
        productPrice := item.Find(".product-price").Text()
        fmt.Printf("Product: %s, Price: %sn", productName, productPrice)
    })
}

This code sends a request to Hotline.ua, parses the HTML content, and extracts product names and prices. You can modify the selectors to target specific elements on the page, such as flash sales or trending products.

Storing Data in SQLite: Creating a Database Schema

Once we have extracted the data, we need to store it in a database for further analysis. SQLite is an excellent choice for this task due to its simplicity and ease of integration with Go. Let’s create a database schema to store product data.

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CREATE TABLE products (
id INTEGER PRIMARY KEY AUTOINCREMENT,
name TEXT NOT NULL,
price TEXT NOT NULL,
timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
);
CREATE TABLE products ( id INTEGER PRIMARY KEY AUTOINCREMENT, name TEXT NOT NULL, price TEXT NOT NULL, timestamp DATETIME DEFAULT CURRENT_TIMESTAMP );
CREATE TABLE products (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    name TEXT NOT NULL,
    price TEXT NOT NULL,
    timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
);

This SQL script creates a “products” table with columns for product ID, name, price, and a timestamp. The timestamp column records when the data was collected, allowing us to track changes over time.

Integrating Go and SQLite: Inserting Data into the Database

Now that we have a database schema, let’s integrate SQLite into our Go program to insert the extracted data into the database. We will use the “github.com/mattn/go-sqlite3” package to interact with SQLite from Go.

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package main
import (
"database/sql"
"fmt"
"log"
"net/http"
"github.com/PuerkitoBio/goquery"
_ "github.com/mattn/go-sqlite3"
)
func main() {
// Open SQLite database
db, err := sql.Open("sqlite3", "./hotline.db")
if err != nil {
log.Fatal(err)
}
defer db.Close()
// Send HTTP request to Hotline.ua
res, err := http.Get("https://hotline.ua")
if err != nil {
log.Fatal(err)
}
defer res.Body.Close()
// Parse the HTML document
doc, err := goquery.NewDocumentFromReader(res.Body)
if err != nil {
log.Fatal(err)
}
// Extract and insert product data into the database
doc.Find(".product-item").Each(func(index int, item *goquery.Selection) {
productName := item.Find(".product-title").Text()
productPrice := item.Find(".product-price").Text()
_, err := db.Exec("INSERT INTO products (name, price) VALUES (?, ?)", productName, productPrice)
if err != nil {
log.Fatal(err)
}
fmt.Printf("Inserted Product: %s, Price: %sn", productName, productPrice)
})
}
package main import ( "database/sql" "fmt" "log" "net/http" "github.com/PuerkitoBio/goquery" _ "github.com/mattn/go-sqlite3" ) func main() { // Open SQLite database db, err := sql.Open("sqlite3", "./hotline.db") if err != nil { log.Fatal(err) } defer db.Close() // Send HTTP request to Hotline.ua res, err := http.Get("https://hotline.ua") if err != nil { log.Fatal(err) } defer res.Body.Close() // Parse the HTML document doc, err := goquery.NewDocumentFromReader(res.Body) if err != nil { log.Fatal(err) } // Extract and insert product data into the database doc.Find(".product-item").Each(func(index int, item *goquery.Selection) { productName := item.Find(".product-title").Text() productPrice := item.Find(".product-price").Text() _, err := db.Exec("INSERT INTO products (name, price) VALUES (?, ?)", productName, productPrice) if err != nil { log.Fatal(err) } fmt.Printf("Inserted Product: %s, Price: %sn", productName, productPrice) }) }
package main

import (
    "database/sql"
    "fmt"
    "log"
    "net/http"

    "github.com/PuerkitoBio/goquery"
    _ "github.com/mattn/go-sqlite3"
)

func main() {
    // Open SQLite database
    db, err := sql.Open("sqlite3", "./hotline.db")
    if err != nil {
        log.Fatal(err)
    }
    defer db.Close()

    // Send HTTP request to Hotline.ua
    res, err := http.Get("https://hotline.ua")
    if err != nil {
        log.Fatal(err)
    }
    defer res.Body.Close()

    // Parse the HTML document
    doc, err := goquery.NewDocumentFromReader(res.Body)
    if err != nil {
        log.Fatal(err)
    }

    // Extract and insert product data into the database
    doc.Find(".product-item").Each(func(index int, item *goquery.Selection) {
        productName := item.Find(".product-title").Text()
        productPrice := item.Find(".product-price").Text()

        _, err := db.Exec("INSERT INTO products (name, price) VALUES (?, ?)", productName, productPrice)
        if err != nil {
            log.Fatal(err)
        }

        fmt.Printf("Inserted Product: %s, Price: %sn", productName, productPrice)
    })
}

This code connects to the SQLite database, extracts product data from Hotline.ua, and inserts it into the “products” table. By running this program periodically, you can build a comprehensive dataset for analysis.

With the data stored in SQLite, you can perform various analyses to identify trends and insights. For example, you can query the database to find the most popular products, track price changes over time, or identify peak sales periods. These insights can inform business strategies and decision-making.

Consider using SQL queries to extract specific insights from the data. For instance, you can find the top 10 most popular products by running a query that counts the occurrences of each product name. Similarly, you can analyze price trends by comparing prices over different timestamps.

Conclusion: Leveraging E-Commerce Data for Business Success

Collecting e-commerce data from platforms like Hotline.ua using Go and SQLite provides businesses with valuable insights into market trends, consumer preferences, and sales patterns. By extracting and analyzing

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