Scraping Blibli Using Kotlin & Cassandra: Tracking Electronics Pricing, Coupon Offers, and Verified Seller Ratings

In the fast-paced world of e-commerce, staying ahead of the competition requires real-time data analysis and strategic decision-making. One of the most effective ways to achieve this is through web scraping, a technique that allows businesses to extract valuable information from websites. In this article, we will explore how to scrape Blibli, a popular Indonesian e-commerce platform, using Kotlin and Cassandra. Our focus will be on tracking electronics pricing, coupon offers, and verified seller ratings.

Understanding the Importance of Web Scraping in E-commerce

Web scraping has become an essential tool for businesses looking to gain a competitive edge in the e-commerce industry. By extracting data from online platforms, companies can monitor pricing trends, identify lucrative coupon offers, and assess seller credibility. This information is crucial for making informed decisions that can enhance customer satisfaction and boost sales.

For instance, tracking electronics pricing allows businesses to adjust their pricing strategies in real-time, ensuring they remain competitive. Similarly, identifying coupon offers can help companies attract price-sensitive customers, while analyzing seller ratings ensures that customers receive high-quality products and services.

Why Choose Kotlin for Web Scraping?

Kotlin, a modern programming language developed by JetBrains, has gained popularity for its simplicity, conciseness, and interoperability with Java. These features make it an excellent choice for web scraping projects. Kotlin’s expressive syntax and powerful libraries enable developers to write clean and efficient code, reducing development time and effort.

Moreover, Kotlin’s compatibility with Java allows developers to leverage existing Java libraries and frameworks, making it easier to integrate with other technologies. This flexibility is particularly beneficial when working with complex web scraping tasks that require seamless integration with databases and data processing tools.

Setting Up the Environment for Scraping Blibli

Before diving into the code, it’s essential to set up the development environment. First, ensure that you have the latest version of Kotlin installed on your system. You can download it from the official Kotlin website. Additionally, you’ll need an Integrated Development Environment (IDE) like IntelliJ IDEA, which provides excellent support for Kotlin development.

Next, set up a Cassandra database to store the scraped data. Apache Cassandra is a highly scalable and distributed NoSQL database that excels in handling large volumes of data. Its ability to provide high availability and fault tolerance makes it an ideal choice for storing e-commerce data.

Scraping Electronics Pricing from Blibli

To scrape electronics pricing from Blibli, we will use the Jsoup library, a popular Java library for working with real-world HTML. Jsoup provides a convenient API for extracting and manipulating data from web pages, making it an excellent choice for web scraping tasks.

import org.jsoup.Jsoup

fun scrapeElectronicsPricing(url: String): List<Pair> {
    val document = Jsoup.connect(url).get()
    val products = document.select(".product-item")
    val pricingData = mutableListOf<Pair>()

    for (product in products) {
        val name = product.select(".product-name").text()
        val price = product.select(".product-price").text().replace("Rp", "").replace(",", "").toDouble()
        pricingData.add(Pair(name, price))
    }

    return pricingData
}

This Kotlin function connects to the specified Blibli URL, retrieves the HTML content, and extracts the product names and prices. The data is then stored in a list of pairs, where each pair contains the product name and its corresponding price.

Tracking Coupon Offers on Blibli

Coupon offers are a significant factor in attracting customers to e-commerce platforms. By tracking these offers, businesses can identify trends and tailor their marketing strategies accordingly. To scrape coupon offers from Blibli, we can modify our previous code to extract coupon-related information.

fun scrapeCouponOffers(url: String): List {
    val document = Jsoup.connect(url).get()
    val coupons = document.select(".coupon-offer")
    val couponData = mutableListOf()

    for (coupon in coupons) {
        val offer = coupon.text()
        couponData.add(offer)
    }

    return couponData
}

This function extracts coupon offers from the specified Blibli URL by selecting elements with the class “coupon-offer.” The extracted offers are stored in a list of strings for further analysis.

Analyzing Verified Seller Ratings

Seller ratings play a crucial role in building customer trust and ensuring a positive shopping experience. By analyzing verified seller ratings, businesses can identify reliable sellers and avoid potential issues. To scrape seller ratings from Blibli, we can extend our scraping logic to include rating information.

fun scrapeSellerRatings(url: String): List<Pair> {
    val document = Jsoup.connect(url).get()
    val sellers = document.select(".seller-info")
    val sellerRatings = mutableListOf<Pair>()

    for (seller in sellers) {
        val name = seller.select(".seller-name").text()
        val rating = seller.select(".seller-rating").text().toDouble()
        sellerRatings.add(Pair(name, rating))
    }

    return sellerRatings
}

This function extracts seller names and ratings from the specified Blibli URL by selecting elements with the class “seller-info.” The data is stored in a list of pairs, where each pair contains the seller name and its corresponding rating.

Storing Scraped Data in Cassandra

Once we have extracted the necessary data, the next step is to store it in a Cassandra database for further analysis. Cassandra’s distributed architecture and high availability make it an ideal choice for handling large volumes of e-commerce data.

CREATE KEYSPACE blibli_data WITH replication = {'class': 'SimpleStrategy', 'replication_factor': 1};

CREATE TABLE blibli_data.electronics_pricing (
    product_name text PRIMARY KEY,
    price double
);

CREATE TABLE blibli_data.coupon_offers (
    offer_id uuid PRIMARY KEY,
    offer_text text
);

CREATE TABLE blibli_data.seller_ratings (
    seller_name text PRIMARY KEY,
    rating double
);

These CQL scripts create a keyspace named “blibli_data” and three tables: “electronics_pricing,” “coupon_offers,” and “seller_ratings.” Each table is designed to store the corresponding scraped data, with appropriate data types for each field.

Conclusion

In conclusion, web scraping is a powerful tool for businesses looking to gain insights into the e-commerce landscape. By using Kotlin and Cassandra, companies can efficiently

Scraping Blibli Using Kotlin & Cassandra: Tracking Electronics Pricing, Coupon Offers, and Verified Seller Ratings