#Monitoring services using JSON

[govindhtech]

What is Argo CD? And When Was Argo CD Established?

What Is Argo CD?

Argo CD is declarative Kubernetes GitOps continuous delivery.

In DevOps, ArgoCD is a Continuous Delivery (CD) technology that has become well-liked for delivering applications to Kubernetes. It is based on the GitOps deployment methodology.

When was Argo CD Established?

Argo CD was created at Intuit and made publicly available following Applatix’s 2018 acquisition by Intuit. The founding developers of Applatix, Hong Wang, Jesse Suen, and Alexander Matyushentsev, made the Argo project open-source in 2017.

Why Argo CD?

Declarative and version-controlled application definitions, configurations, and environments are ideal. Automated, auditable, and easily comprehensible application deployment and lifecycle management are essential.

Getting Started

Quick Start

kubectl create namespace argocd kubectl apply -n argocd -f https://raw.githubusercontent.com/argoproj/argo-cd/stable/manifests/install.yaml

For some features, more user-friendly documentation is offered. Refer to the upgrade guide if you want to upgrade your Argo CD. Those interested in creating third-party connectors can access developer-oriented resources.

How it works

Argo CD defines the intended application state by employing Git repositories as the source of truth, in accordance with the GitOps pattern. There are various approaches to specify Kubernetes manifests:

Applications for Customization

Helm charts

JSONNET files

Simple YAML/JSON manifest directory

Any custom configuration management tool that is set up as a plugin

The deployment of the intended application states in the designated target settings is automated by Argo CD. Deployments of applications can monitor changes to branches, tags, or pinned to a particular manifest version at a Git commit.

Architecture

The implementation of Argo CD is a Kubernetes controller that continually observes active apps and contrasts their present, live state with the target state (as defined in the Git repository). Out Of Sync is the term used to describe a deployed application whose live state differs from the target state. In addition to reporting and visualizing the differences, Argo CD offers the ability to manually or automatically sync the current state back to the intended goal state. The designated target environments can automatically apply and reflect any changes made to the intended target state in the Git repository.

Components

API Server

The Web UI, CLI, and CI/CD systems use the API, which is exposed by the gRPC/REST server. Its duties include the following:

Status reporting and application management

Launching application functions (such as rollback, sync, and user-defined actions)

Cluster credential management and repository (k8s secrets)

RBAC enforcement

Authentication, and auth delegation to outside identity providers

Git webhook event listener/forwarder

Repository Server

An internal service called the repository server keeps a local cache of the Git repository containing the application manifests. When given the following inputs, it is in charge of creating and returning the Kubernetes manifests:

URL of the repository

Revision (tag, branch, commit)

Path of the application

Template-specific configurations: helm values.yaml, parameters

A Kubernetes controller known as the application controller keeps an eye on all active apps and contrasts their actual, live state with the intended target state as defined in the repository. When it identifies an Out Of Sync application state, it may take remedial action. It is in charge of calling any user-specified hooks for lifecycle events (Sync, PostSync, and PreSync).

Features

Applications are automatically deployed to designated target environments.

Multiple configuration management/templating tools (Kustomize, Helm, Jsonnet, and plain-YAML) are supported.

Capacity to oversee and implement across several clusters

Integration of SSO (OIDC, OAuth2, LDAP, SAML 2.0, Microsoft, LinkedIn, GitHub, GitLab)

RBAC and multi-tenancy authorization policies

Rollback/Roll-anywhere to any Git repository-committed application configuration

Analysis of the application resources’ health state

Automated visualization and detection of configuration drift

Applications can be synced manually or automatically to their desired state.

Web user interface that shows program activity in real time

CLI for CI integration and automation

Integration of webhooks (GitHub, BitBucket, GitLab)

Tokens of access for automation

Hooks for PreSync, Sync, and PostSync to facilitate intricate application rollouts (such as canary and blue/green upgrades)

Application event and API call audit trails

Prometheus measurements

To override helm parameters in Git, use parameter overrides.

Read more on Govindhtech.com

[reviewgatorsusa]

How Web Scraping TripAdvisor Reviews Data Boosts Your Business Growth

Are you one of the 94% of buyers who rely on online reviews to make the final decision? This means that most people today explore reviews before taking action, whether booking hotels, visiting a place, buying a book, or something else.

We understand the stress of booking the right place, especially when visiting somewhere new. Finding the balance between a perfect spot, services, and budget is challenging. Many of you consider TripAdvisor reviews a go-to solution for closely getting to know the place.

Here comes the accurate game-changing method—scrape TripAdvisor reviews data. But wait, is it legal and ethical? Yes, as long as you respect the website's terms of service, don't overload its servers, and use the data for personal or non-commercial purposes. What? How? Why?

Do not stress. We will help you understand why many hotel, restaurant, and attraction place owners invest in web scraping TripAdvisor reviews or other platform information. This powerful tool empowers you to understand your performance and competitors' strategies, enabling you to make informed business changes. What next?

Let's dive in and give you a complete tour of the process of web scraping TripAdvisor review data!

What Is Scraping TripAdvisor Reviews Data?

Extracting customer reviews and other relevant information from the TripAdvisor platform through different web scraping methods. This process works by accessing publicly available website data and storing it in a structured format to analyze or monitor.

Various methods and tools available in the market have unique features that allow you to extract TripAdvisor hotel review data hassle-free. Here are the different types of data you can scrape from a TripAdvisor review scraper:

Hotels

Ratings

Awards

Location

Pricing

Number of reviews

Review date

Reviewer's Name

Restaurants

Images

You may want other information per your business plan, which can be easily added to your requirements.

What Are The Ways To Scrape TripAdvisor Reviews Data?

TripAdvisor uses different web scraping methods to review data, depending on available resources and expertise. Let us look at them:

Scrape TripAdvisor Reviews Data Using Web Scraping API

An API helps to connect various programs to gather data without revealing the code used to execute the process. The scrape TripAdvisor Reviews is a standard JSON format that does not require technical knowledge, CAPTCHAs, or maintenance.

Now let us look at the complete process:

First, check if you need to install the software on your device or if it's browser-based and does not need anything. Then, download and install the desired software you will be using for restaurant, location, or hotel review scraping. The process is straightforward and user-friendly, ensuring your confidence in using these tools.

Now redirect to the web page you want to scrape data from and copy the URL to paste it into the program.

Make updates in the HTML output per your requirements and the information you want to scrape from TripAdvisor reviews.

Most tools start by extracting different HTML elements, especially the text. You can then select the categories that need to be extracted, such as Inner HTML, href attribute, class attribute, and more.

Export the data in SPSS, Graphpad, or XLSTAT format per your requirements for further analysis.

Scrape TripAdvisor Reviews Using Python

TripAdvisor review information is analyzed to understand the experience of hotels, locations, or restaurants. Now let us help you to scrape TripAdvisor reviews using Python:

Continue reading https://www.reviewgators.com/how-web-scraping-tripadvisor-reviews-data-boosts-your-business-growth.php

[hindintech]

You can learn NodeJS easily, Here's all you need:

1.Introduction to Node.js

• JavaScript Runtime for Server-Side Development

• Non-Blocking I/0

2.Setting Up Node.js

• Installing Node.js and NPM

• Package.json Configuration

• Node Version Manager (NVM)

3.Node.js Modules

• CommonJS Modules (require, module.exports)

• ES6 Modules (import, export)

• Built-in Modules (e.g., fs, http, events)

4.Core Concepts

• Event Loop

• Callbacks and Asynchronous Programming

• Streams and Buffers

5.Core Modules

• fs (File Svstem)

• http and https (HTTP Modules)

• events (Event Emitter)

• util (Utilities)

• os (Operating System)

• path (Path Module)

6.NPM (Node Package Manager)

• Installing Packages

• Creating and Managing package.json

• Semantic Versioning

• NPM Scripts

7.Asynchronous Programming in Node.js

• Callbacks

• Promises

• Async/Await

• Error-First Callbacks

8.Express.js Framework

• Routing

• Middleware

• Templating Engines (Pug, EJS)

• RESTful APIs

• Error Handling Middleware

9.Working with Databases

• Connecting to Databases (MongoDB, MySQL)

• Mongoose (for MongoDB)

• Sequelize (for MySQL)

• Database Migrations and Seeders

10.Authentication and Authorization

• JSON Web Tokens (JWT)

• Passport.js Middleware

• OAuth and OAuth2

11.Security

• Helmet.js (Security Middleware)

• Input Validation and Sanitization

• Secure Headers

• Cross-Origin Resource Sharing (CORS)

12.Testing and Debugging

• Unit Testing (Mocha, Chai)

• Debugging Tools (Node Inspector)

• Load Testing (Artillery, Apache Bench)

13.API Documentation

• Swagger

• API Blueprint

• Postman Documentation

14.Real-Time Applications

• WebSockets (Socket.io)

• Server-Sent Events (SSE)

• WebRTC for Video Calls

15.Performance Optimization

• Caching Strategies (in-memory, Redis)

• Load Balancing (Nginx, HAProxy)

• Profiling and Optimization Tools (Node Clinic, New Relic)

16.Deployment and Hosting

• Deploying Node.js Apps (PM2, Forever)

• Hosting Platforms (AWS, Heroku, DigitalOcean)

• Continuous Integration and Deployment-(Jenkins, Travis CI)

17.RESTful API Design

• Best Practices

• API Versioning

• HATEOAS (Hypermedia as the Engine-of Application State)

18.Middleware and Custom Modules

• Creating Custom Middleware

• Organizing Code into Modules

• Publish and Use Private NPM Packages

19.Logging

• Winston Logger

• Morgan Middleware

• Log Rotation Strategies

20.Streaming and Buffers

• Readable and Writable Streams

• Buffers

• Transform Streams

21.Error Handling and Monitoring

• Sentry and Error Tracking

• Health Checks and Monitoring Endpoints

22.Microservices Architecture

• Principles of Microservices

• Communication Patterns (REST, gRPC)

• Service Discovery and Load Balancing in Microservices

[easylaunchpad]

Built-in Logging with Serilog: How EasyLaunchpad Keeps Debugging Clean and Insightful

Debugging shouldn’t be a scavenger hunt.

When things break in production or behave unexpectedly in development, you don’t have time to dig through vague error messages or guess what went wrong. That’s why logging is one of the most critical — but often neglected — parts of building robust applications.

With EasyLaunchpad, logging is not an afterthought.

We’ve integrated Serilog, a powerful and structured logging framework for .NET, directly into the boilerplate so developers can monitor, debug, and optimize their apps from day one.

In this post, we’ll explain how Serilog is implemented inside EasyLaunchpad, why it’s a developer favorite, and how it helps you launch smarter and maintain easier.

🧠 Why Logging Matters (Especially in Startups)

Whether you’re launching a SaaS MVP or maintaining a production application, logs are your eyes and ears:

Track user behavior

Monitor background job status

Catch and analyze errors

Identify bottlenecks or API failures

Verify security rules and access patterns

With traditional boilerplates, you often need to configure and wire this up yourself. But EasyLaunchpad comes preloaded with structured, scalable logging using Serilog, so you’re ready to go from the first line of code.

🔧 What Is Serilog?

Serilog is one of the most popular logging libraries for .NET Core. Unlike basic logging tools that write unstructured plain-text logs, Serilog generates structured logs — which are easier to search, filter, and analyze in any environment.

It supports:

JSON log output

File, Console, or external sinks (like Seq, Elasticsearch, Datadog)

Custom formats and enrichers

Log levels: Information, Warning, Error, Fatal, and more

Serilog is lightweight, flexible, and production-proven — ideal for modern web apps like those built with EasyLaunchpad.

���� How Serilog Is Integrated in EasyLaunchpad

When you start your EasyLaunchpad-based project, Serilog is already:

Installed via NuGet

Configured via appsettings.json

Injected into the middleware pipeline

Wired into all key services (auth, jobs, payments, etc.)

🔁 Configuration Example (appsettings.json):

“Serilog”: {

“MinimumLevel”: {

“Default”: “Information”,

“Override”: {

“Microsoft”: “Warning”,

“System”: “Warning”

}

},

“WriteTo”: [

{ “Name”: “Console” },

{

“Name”: “File”,

“Args”: {

“path”: “Logs/log-.txt”,

“rollingInterval”: “Day”

}

}

}

}

This setup gives you daily rotating log files, plus real-time console logs for development mode.

🛠 How It Helps Developers

✅ 1. Real-Time Debugging

During development, logs are streamed to the console. You’ll see:

Request details

Controller actions triggered

Background job execution

Custom messages from your services

This means you can debug without hitting breakpoints or printing Console.WriteLine().

✅ 2. Structured Production Logs

In production, logs are saved to disk in a structured format. You can:

Tail them from the server

Upload them to a logging platform (Seq, Datadog, ELK stack)

Automatically parse fields like timestamp, level, message, exception, etc.

This gives predictable, machine-readable logging — critical for scalable monitoring.

✅ 3. Easy Integration with Background Jobs

EasyLaunchpad uses Hangfire for background job scheduling. Serilog is integrated into:

Job execution logging

Retry and failure logs

Email queue status

Error capturing

No more “silent fails” in background processes — every action is traceable.

✅ 4. Enhanced API Logging (Optional Extension)

You can easily extend the logging to:

Log request/response for APIs

Add correlation IDs

Track user activity (e.g., login attempts, failed validations)

The modular architecture allows you to inject loggers into any service or controller via constructor injection.

🔍 Sample Log Output

Here’s a typical log entry generated by Serilog in EasyLaunchpad:

{

“Timestamp”: “2024–07–10T08:33:21.123Z”,

“Level”: “Information”,

“Message”: “User {UserId} logged in successfully.”,

“UserId”: “5dc95f1f-2cc2–4f8a-ae1b-1d29f2aa387a”

}

This is not just human-readable — it’s machine-queryable.

You can filter logs by UserId, Level, or Timestamp using modern logging dashboards or scripts.

🧱 A Developer-Friendly Logging Foundation

Unlike minimal templates, where you have to integrate logging yourself, EasyLaunchpad is:

Ready-to-use from first launch

Customizable for your own needs

Extendable with any Serilog sink (e.g., database, cloud services, Elasticsearch)

This means you spend less time configuring and more time building and scaling.

🧩 Built-In + Extendable

You can add additional log sinks in minutes:

Log.Logger = new LoggerConfiguration()

.WriteTo.Console()

.WriteTo.File(“Logs/log.txt”)

.WriteTo.Seq(“http://localhost:5341")

.CreateLogger();

Want to log in to:

Azure App Insights?

AWS CloudWatch?

A custom microservice?

Serilog makes it possible, and EasyLaunchpad makes it easy to start.

💼 Real-World Scenarios

Here are some real ways logging helps EasyLaunchpad-based apps:

Use Case and the Benefit

Login attempts — Audit user activity and failed attempts

Payment errors- Track Stripe/Paddle API errors

Email queue- Debug failed or delayed emails

Role assignment- Log admin actions for compliance

Cron jobs- Monitor background jobs in real-time

🧠 Final Thoughts

You can’t fix what you can’t see.

Whether you’re launching an MVP or running a growing SaaS platform, structured logging gives you visibility, traceability, and peace of mind.

EasyLaunchpad integrates Serilog from day one — so you’re never flying blind. You get a clean, scalable logging system with zero setup required.

No more guesswork. Just clarity.

👉 Start building with confidence. Check out EasyLaunchpad at https://easylaunchpad.com and see how production-ready logging fits into your stack.

[lakshmimonopoly1]

Integrating Third-Party APIs in .NET Applications

In today’s software landscape, building a great app often means connecting it with services that already exist—like payment gateways, email platforms, or cloud storage. Instead of building every feature from scratch, developers can use third-party APIs to save time and deliver more powerful applications. If you're aiming to become a skilled .NET developer, learning how to integrate these APIs is a must—and enrolling at the Best DotNet Training Institute in Hyderabad, Kukatpally, KPHB is a great place to start.

Why Third-Party APIs Matter

Third-party APIs let developers tap into services built by other companies. For example, if you're adding payments to your app, using a service like Razorpay or Stripe means you don’t have to handle all the complexity of secure transactions yourself. Similarly, APIs from Google, Microsoft, or Facebook can help with everything from login systems to maps and analytics.

These tools don’t just save time—they help teams build better, more feature-rich applications.

.NET Makes API Integration Easy

One of the reasons developers love working with .NET is how well it handles API integration. Using built-in tools like HttpClient, you can make API calls, handle responses, and even deal with errors in a clean and structured way. Plus, with async programming support, these interactions won’t slow down your application.

There are also helpful libraries like RestSharp and features for handling JSON that make working with APIs even smoother.

Smart Tips for Successful Integration

When you're working with third-party APIs, keeping a few best practices in mind can make a big difference:

Keep Secrets Safe: Don’t hard-code API keys—use config files or environment variables instead.

Handle Errors Gracefully: Always check for errors and timeouts. APIs aren't perfect, so plan for the unexpected.

Be Aware of Limits: Many APIs have rate limits. Know them and design your app accordingly.

Use Dependency Injection: For tools like HttpClient, DI helps manage resources and keeps your code clean.

Log Everything: Keep logs of API responses—this helps with debugging and monitoring performance.

Real-World Examples

Here are just a few ways .NET developers use third-party APIs in real applications:

Adding Google Maps to show store locations

Sending automatic emails using SendGrid

Processing online payments through PayPal or Razorpay

Uploading and managing files on AWS S3 or Azure Blob Storage

Conclusion

Third-party APIs are a powerful way to level up your .NET applications. They save time, reduce complexity, and help you deliver smarter features faster. If you're ready to build real-world skills and become job-ready, check out Monopoly IT Solutions—we provide hands-on training that prepares you for success in today’s tech-driven world.

[zakjasper]

MEV Bot Development: A Step-by-Step Guide

Introduction

As the DeFi ecosystem grows more complex, MEV (Maximal Extractable Value) bots have become one of the most powerful—and controversial—tools in crypto trading. These bots are designed to extract value from blockchain transactions by reordering, inserting, or censoring transactions in a block. While originally focused on miners, today’s MEV bot development company opportunities are accessible to smart developers who can build bots to interact with protocols like Uniswap, SushiSwap, Curve, and others.

In this step-by-step guide, we’ll walk through the process of developing your own MEV bot—from understanding its core components to writing, simulating, and deploying it.

What Is an MEV Bot?

An MEV bot is an automated trading agent that exploits inefficiencies in blockchain transactions. It aims to maximize profit through techniques such as:

Arbitrage: Buying an asset at a lower price on one DEX and selling it higher on another.

Sandwich Attacks: Placing a buy order before and a sell order after a large user transaction to manipulate price movements.

Liquidation Sniping: Monitoring DeFi lending protocols for vulnerable positions and profiting from liquidations.

These bots monitor the mempool or integrate with services like Flashbots to submit private bundles directly to miners or validators.

MEV Bot Development: A Step-by-Step Guide

Step 1: Understand MEV Fundamentals

Start by learning what Maximal Extractable Value (MEV) means, how it impacts Ethereum and other blockchains, and the types of MEV strategies such as arbitrage, sandwich attacks, and liquidations. Understanding blockchain mechanics, mempool structure, transaction ordering, and frontrunning concepts is critical before development.

Step 2: Choose Your MEV Strategy

Select the specific MEV technique you want to implement—DEX arbitrage, sandwiching, liquidation sniping, or time-bandit attacks. Your choice will determine the logic and external data your bot will need to operate effectively.

Step 3: Set Up Your Development Environment

Install essential tools like Node.js, Hardhat or Foundry, ethers.js or web3.js, and connect to Ethereum mainnet via Infura or Alchemy. Set up Flashbots for private transaction bundling. Use Git and VSCode for development and version control.

Step 4: Monitor Blockchain Data and Mempool

Develop or use an existing script to monitor pending transactions in the mempool using WebSocket or JSON-RPC. For strategies like sandwich or liquidation attacks, listen for large swaps or vulnerable loans, and simulate how your bot should respond.

Step 5: Write the Bot Logic

Create the logic for transaction construction, execution flow, and profit calculation. Include logic for gas estimation, token approvals, smart contract calls, and condition-based execution. Integrate error handling and fallback mechanisms.

Step 6: Simulate and Test on a Forked Network

Use Hardhat or Foundry to fork mainnet and simulate your bot’s transactions in a safe environment. Test for profitability, failed conditions, slippage, and gas efficiency. Refine strategy logic based on simulation results.

Step 7: Integrate Flashbots for Private Execution

To avoid frontrunning and reduce failed transactions, integrate with Flashbots by creating and submitting bundles directly to miners or validators. This helps ensure your transactions are mined in the intended order.

Step 8: Deploy the Bot on Mainnet

Once tested, deploy the bot on the Ethereum mainnet or another supported network. Run it on a secure server or cloud platform. Use cron jobs or real-time triggers to keep the bot active and responsive.

Step 9: Monitor and Optimize Performance

Track your bot’s trade history, gas usage, and success rate. Use dashboards or logging tools for performance monitoring. Continuously optimize strategies by adapting to network changes, gas spikes, and competition from other bots.

Step 10: Stay Updated and Compliant

The MEV landscape evolves quickly. Stay informed through forums, GitHub, and Flashbots research. Monitor ethical debates and legal implications around MEV activities. Consider evolving your bot for multi-chain or L2 MEV opportunities.

What Makes MEV Bots Different from Regular Crypto Bots?

MEV bots differ from regular crypto trading bots in that they extract profits by manipulating the order and timing of on-chain transactions, rather than relying on market trends or exchange arbitrage. While regular bots operate through APIs or smart contracts to execute predefined strategies like scalping or grid trading, MEV bots actively monitor the blockchain’s mempool to exploit inefficiencies such as frontrunning, sandwich attacks, and liquidations. They often use private relayers like Flashbots to submit transaction bundles directly to validators, enabling faster and more secure execution. This makes MEV bots more complex, time-sensitive, and ethically debated compared to traditional trading bots.

Conclusion

A MEV bot development can be highly profitable—but it’s also complex, competitive, and ethically gray. With the right technical knowledge, tools, and strategic insights, developers can enter the world of MEV extraction and participate in the most cutting-edge space in DeFi.

Whether you're building a simple arbitrage bot or a sophisticated front-runner, understanding the Ethereum transaction stack and using Flashbots responsibly is key to long-term success.

[crawlxpert01]

A Step-by-Step Guide to Web Scraping Walmart Grocery Delivery Data

Introduction

As those who are in the marketplace know, it is today's data model that calls for real-time grocery delivery data accessibility to drive pricing strategy and track changes in the market and activity by competitors. Walmart Grocery Delivery, one of the giants in e-commerce grocery reselling, provides this data, including product details, prices, availability, and operation time of the deliveries. Data scraping of Walmart Grocery Delivery could provide a business with fine intelligence knowledge about consumer behavior, pricing fluctuations, and changes in inventory.

This guide shall give you everything you need to know about web scraping Walmart Grocery Delivery data—from tools to techniques to challenges and best practices involved in it. We'll explore why CrawlXpert provides the most plausible way to collect reliable, large-scale data on Walmart.

1. What is Walmart Grocery Delivery Data Scraping?

Walmart Grocery Delivery scraping data is the collection of the product as well as delivery information from Walmart's electronic grocery delivery service. The online grocery delivery service thus involves accessing the site's HTML content programmatically and processing it for key data points.

Key Data Points You Can Extract:

Product Listings: Names, descriptions, categories, and specifications.

Pricing Data: Current price, original price, and promotional discounts.

Delivery Information: Availability, delivery slots, and estimated delivery times.

Stock Levels: In-stock, out-of-stock, or limited availability status.

Customer Reviews: Ratings, review counts, and customer feedback.

2. Why Scrape Walmart Grocery Delivery Data?

Scraping Walmart Grocery Delivery data provides valuable insights and enables data-driven decision-making for businesses. Here are the primary use cases:

a) Competitor Price Monitoring

Track Pricing Trends: Extracting Walmart’s pricing data enables you to track price changes over time.

Competitive Benchmarking: Compare Walmart’s pricing with other grocery delivery services.

Dynamic Pricing: Adjust your pricing strategies based on real-time competitor data.

b) Market Research and Consumer Insights

Product Popularity: Identify which products are frequently purchased or promoted.

Seasonal Trends: Track pricing and product availability during holiday seasons.

Consumer Sentiment: Analyze reviews to understand customer preferences.

c) Inventory and Supply Chain Optimization

Stock Monitoring: Identify frequently out-of-stock items to detect supply chain issues.

Demand Forecasting: Use historical data to predict future demand and optimize inventory.

d) Enhancing Marketing and Promotions

Targeted Advertising: Leverage scraped data to create personalized marketing campaigns.

SEO Optimization: Enrich your website with detailed product descriptions and pricing data.

3. Tools and Technologies for Scraping Walmart Grocery Delivery Data

To efficiently scrape Walmart Grocery Delivery data, you need the right combination of tools and technologies.

a) Python Libraries for Web Scraping

BeautifulSoup: Parses HTML and XML documents for easy data extraction.

Requests: Sends HTTP requests to retrieve web page content.

Selenium: Automates browser interactions, useful for dynamic pages.

Scrapy: A Python framework designed for large-scale web scraping.

Pandas: For data cleaning and storing scraped data into structured formats.

b) Proxy Services to Avoid Detection

Bright Data: Reliable IP rotation and CAPTCHA-solving capabilities.

ScraperAPI: Automatically handles proxies, IP rotation, and CAPTCHA solving.

Smartproxy: Provides residential proxies to reduce the chances of being blocked.

c) Browser Automation Tools

Playwright: Automates browser interactions for dynamic content rendering.

Puppeteer: A Node.js library that controls a headless Chrome browser.

d) Data Storage Options

CSV/JSON: Suitable for smaller-scale data storage.

MongoDB/MySQL: For large-scale structured data storage.

Cloud Storage: AWS S3, Google Cloud, or Azure for scalable storage.

4. Building a Walmart Grocery Delivery Scraper

a) Install the Required Libraries

First, install the necessary Python libraries:

pip install requests beautifulsoup4 selenium pandas

b) Inspect Walmart’s Website Structure

Open Walmart Grocery Delivery in your browser.

Right-click → Inspect → Select Elements.

Identify product containers, pricing, and delivery details.

c) Fetch the Walmart Delivery Page

import requests from bs4 import BeautifulSoup url = 'https://www.walmart.com/grocery' headers = {'User-Agent': 'Mozilla/5.0'} response = requests.get(url, headers=headers) soup = BeautifulSoup(response.content, 'html.parser')

d) Extract Product and Delivery Data

products = soup.find_all('div', class_='search-result-gridview-item') data = [] for product in products: try: title = product.find('a', class_='product-title-link').text price = product.find('span', class_='price-main').text availability = product.find('div', class_='fulfillment').text data.append({'Product': title, 'Price': price, 'Delivery': availability}) except AttributeError: continue

5. Bypassing Walmart’s Anti-Scraping Mechanisms

Walmart uses anti-bot measures like CAPTCHAs and IP blocking. Here are strategies to bypass them:

a) Use Proxies for IP Rotation

Rotating IP addresses reduces the risk of being blocked.proxies = {'http': 'http://user:pass@proxy-server:port'} response = requests.get(url, headers=headers, proxies=proxies)

b) Use User-Agent Rotation

import random user_agents = [ 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)', 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)' ] headers = {'User-Agent': random.choice(user_agents)}

c) Use Selenium for Dynamic Content

from selenium import webdriver options = webdriver.ChromeOptions() options.add_argument('--headless') driver = webdriver.Chrome(options=options) driver.get(url) data = driver.page_source driver.quit() soup = BeautifulSoup(data, 'html.parser')

6. Data Cleaning and Storage

Once you’ve scraped the data, clean and store it:import pandas as pd df = pd.DataFrame(data) df.to_csv('walmart_grocery_delivery.csv', index=False)

7. Why Choose CrawlXpert for Walmart Grocery Delivery Data Scraping?

While building your own Walmart scraper is possible, it comes with challenges, such as handling CAPTCHAs, IP blocking, and dynamic content rendering. This is where CrawlXpert excels.

Key Benefits of CrawlXpert:

Accurate Data Extraction: CrawlXpert provides reliable and comprehensive data extraction.

Scalable Solutions: Capable of handling large-scale data scraping projects.

Anti-Scraping Evasion: Uses advanced techniques to bypass CAPTCHAs and anti-bot systems.

Real-Time Data: Access fresh, real-time data with high accuracy.

Flexible Delivery: Data delivery in multiple formats (CSV, JSON, Excel).

Conclusion

Scrape Data from Walmart Grocery Delivery: Extracting and analyzing the prices, trends, and consumer preferences can show any business the strength behind Walmart Grocery Delivery. But all the tools and techniques won't matter if one finds themselves in deep trouble against Walmart's excellent anti-scraping measures. Thus, using a well-known service such as CrawlXpert guarantees consistent, correct, and compliant data extraction.

Know More : https://www.crawlxpert.com/blog/web-scraping-walmart-grocery-delivery-data

[vaguelymellowharmony]

Boost Your Retail Strategy with Quick Commerce Data Scraping in 2025

Introduction

The retail landscape is evolving rapidly, with Quick Commerce (Q-Commerce) driving instant deliveries across groceries, FMCG, and essential products. Platforms like Blinkit, Instacart, Getir, Gorillas, Swiggy Instamart, and Zapp dominate the space, offering ultra-fast deliveries. However, for retailers to stay competitive, optimize pricing, and track inventory, real-time data insights are crucial.

Quick Commerce Data Scraping has become a game-changer in 2025, enabling retailers to extract, analyze, and act on live market data. Retail Scrape, a leader in AI-powered data extraction, helps businesses track pricing trends, stock levels, promotions, and competitor strategies.

Why Quick Commerce Data Scraping is Essential for Retailers?

Optimize Pricing Strategies – Track real-time competitor prices & adjust dynamically.

Monitor Inventory Trends – Avoid overstocking or stockouts with demand forecasting.

Analyze Promotions & Discounts – Identify top deals & seasonal price drops.

Understand Consumer Behavior – Extract insights from customer reviews & preferences.

Improve Supply Chain Management – Align logistics with real-time demand analysis.

How Quick Commerce Data Scraping Enhances Retail Strategies?

1. Real-Time Competitor Price Monitoring

2. Inventory Optimization & Demand Forecasting

3. Tracking Promotions & Discounts

4. AI-Driven Consumer Behavior Analysis

Challenges in Quick Commerce Scraping & How to Overcome Them

Frequent Website Structure Changes Use AI-driven scrapers that automatically adapt to dynamic HTML structures and website updates.

Anti-Scraping Technologies (CAPTCHAs, Bot Detection, IP Bans) Deploy rotating proxies, headless browsers, and CAPTCHA-solving techniques to bypass restrictions.

Real-Time Price & Stock Changes Implement real-time web scraping APIs to fetch updated pricing, discounts, and inventory availability.

Geo-Restricted Content & Location-Based Offers Use geo-targeted proxies and VPNs to access region-specific data and ensure accuracy.

High Request Volume Leading to Bans Optimize request intervals, use distributed scraping, and implement smart throttling to prevent getting blocked.

Unstructured Data & Parsing Complexities Utilize AI-based data parsing tools to convert raw HTML into structured formats like JSON, CSV, or databases.

Multiple Platforms with Different Data Formats Standardize data collection from apps, websites, and APIs into a unified format for seamless analysis.

Industries Benefiting from Quick Commerce Data Scraping

1. eCommerce & Online Retailers

2. FMCG & Grocery Brands

3. Market Research & Analytics Firms

4. Logistics & Supply Chain Companies

How Retail Scrape Can Help Businesses in 2025

Retail Scrape provides customized Quick Commerce Data Scraping Services to help businesses gain actionable insights. Our solutions include:

Automated Web & Mobile App Scraping for Q-Commerce Data.

Competitor Price & Inventory Tracking with AI-Powered Analysis.

Real-Time Data Extraction with API Integration.

Custom Dashboards for Data Visualization & Predictive Insights.

Conclusion

In 2025, Quick Commerce Data Scraping is an essential tool for retailers looking to optimize pricing, track inventory, and gain competitive intelligence. With platforms like Blinkit, Getir, Instacart, and Swiggy Instamart shaping the future of instant commerce, data-driven strategies are the key to success.

Retail Scrape’s AI-powered solutions help businesses extract, analyze, and leverage real-time pricing, stock, and consumer insights for maximum profitability.

Want to enhance your retail strategy with real-time Q-Commerce insights? Contact Retail Scrape today!

Read more >>https://www.retailscrape.com/fnac-data-scraping-retail-market-intelligence.php

officially published by https://www.retailscrape.com/.

[appitsoftware]

InsightGen AI Services by Appit: Unlock Real-Time Business Intelligence

Redefining Data-Driven Decision Making in the AI Era

In today’s hyperconnected and competitive environment, businesses can no longer rely on static reports or delayed analytics. The need for real-time insights, predictive intelligence, and data democratization is more critical than ever. Enter InsightGen AI Services by Appit—a cutting-edge solution designed to empower organizations with instant, actionable business intelligence powered by artificial intelligence and machine learning.

With InsightGen, Appit is revolutionizing how businesses understand data, forecast outcomes, and make mission-critical decisions—in real time.

What Is InsightGen AI?

InsightGen AI is a next-gen platform developed by Appit that enables businesses to extract deeper, smarter, and faster insights from structured and unstructured data. Unlike traditional BI tools, InsightGen combines AI-driven analytics, real-time data processing, and intuitive visualization dashboards to give decision-makers an always-on, intelligent pulse of their organization.

🧠 Core Capabilities:

Real-time analytics and dashboards

Predictive modeling and forecasting

Natural language query interface (NLQ)

AI-powered anomaly detection

Automated data storytelling and alerts

Integration with ERPs, CRMs, data lakes & cloud platforms

Why InsightGen Matters in 2025 and Beyond

⏱️ Real-Time Decision Making

In a world where trends shift by the minute, InsightGen enables organizations to act on data as it happens, not after it’s too late.

🔮 Predict the Future with Confidence

With built-in ML models, users can accurately forecast sales, churn, demand, and risk, allowing leadership to prepare for future scenarios with data-backed confidence.

🌐 Unify Data Across Sources

From siloed systems to cloud-native environments, InsightGen ingests data from various sources—SAP, Oracle, Salesforce, AWS, Azure, and more—to present a single source of truth.

💬 Ask Questions in Plain English

With Natural Language Query capabilities, even non-technical users can ask questions like "What was our top-selling product last quarter?" and receive instant visual answers.

🔔 Instant Alerts and Automation

InsightGen detects outliers, anomalies, and trends in real-time and sends automated alerts—preventing costly delays and enabling proactive actions.

Use Cases: Driving Intelligence Across Industries

🛒 Retail & eCommerce

Track inventory and sales in real time

Analyze customer buying behavior and personalize offers

Forecast seasonal demand with AI models

🏭 Manufacturing

Monitor production KPIs in real-time

Predict equipment failure using predictive maintenance AI

Optimize supply chain operations and reduce downtime

💼 Financial Services

Real-time fraud detection and transaction monitoring

Investment performance analytics

Compliance tracking and risk forecasting

🧬 Healthcare

Patient data analysis and treatment outcome prediction

Hospital resource planning and optimization

Monitor patient flow and emergency response trends

🎓 Education

Analyze student performance and dropout risks

Real-time reporting on admissions and operations

Personalized learning analytics for better outcomes

Security, Scalability, and Compliance

Appit designed InsightGen AI with enterprise-grade architecture, offering:

🔐 Role-based access control and end-to-end encryption

☁️ Cloud, on-prem, and hybrid deployment options

📊 Support for GDPR, HIPAA, CCPA, and other data regulations

⚙️ Auto-scaling and high availability infrastructure

InsightGen ensures that your data is safe, compliant, and available—always.

The Technology Behind InsightGen AI

InsightGen is built using a powerful technology stack including:

AI/ML Engines: TensorFlow, PyTorch, Scikit-learn

Data Platforms: Apache Kafka, Snowflake, Google BigQuery, Redshift

Visualization Tools: Custom dashboards, embedded BI, Power BI integration

Integration APIs: RESTful services, JSON, XML, Webhooks

AI Assistants: Integrated chat support for querying reports and insights

Case Study: Fortune 500 Firm Unlocks $12M in Cost Savings

Client: Global logistics and warehousing company Challenge: Disconnected data systems, slow insights, reactive decision-making Solution: Appit deployed InsightGen AI with real-time inventory tracking, predictive maintenance alerts, and automated KPI reporting. Results:

📉 $12M saved in operational inefficiencies

📊 65% faster decision cycles

🔄 90% automation of manual reporting

📈 40% improvement in customer SLA compliance

Getting Started with InsightGen AI Services

Whether you're a mid-sized enterprise or a Fortune 1000 company, InsightGen is scalable to meet your analytics maturity level. Appit offers end-to-end support from:

Data strategy and planning

Deployment and integration

Custom dashboard design

AI model training and tuning

Ongoing analytics support and optimization

Why Choose Appit for AI-Powered Business Intelligence?

✅ Decade-long expertise in enterprise software and AI

✅ Tailored analytics solutions for multiple industries

✅ Fast deployment with low-code/no-code customization options

✅ 24/7 support and continuous model refinement

✅ Trusted by leading organizations worldwide

With InsightGen AI, you’re not just collecting data—you’re unlocking real-time, business-changing intelligence.

The Future Is Now: Make Smarter Decisions with InsightGen

In 2025, businesses that react fast, predict accurately, and personalize effectively will win. InsightGen AI by Appit delivers the intelligence layer your enterprise needs to stay ahead of the curve.

Don’t let your data gather dust. Activate it. Understand it. Act on it.

[govindhtech]

Increase AWS Security with MITRE D3FEND, Engage, ATT&CK

Engage, ATT&CK, D3FEND

Connecting MITRE threat detection and mitigation frameworks to AWS security services. Amazon Web Services may benefit from MITRE ATT&CK, MITRE Engage, and MITRE D3FEND controls and processes. These organised, publicly available models explain threat actor activities to assist threat detection and response.

Combining MITRE frameworks completes security operations lifecycle strategy. MITRE ATT&CK specifies threat actor tactics, strategies, and processes, essential for threat modelling and risk assessment. Additionally, MITRE D3FEND proposes proactive security controls like system settings protection and least privilege access to align defences with known attack patterns.

With MITRE Engage, security teams can expose threat actors, cost them money by directing resources to honeypot infrastructure, or mislead them into divulging their strategies by exploiting appealing fictional targets. D3FEND turns ATT&CK insights into defensive mechanisms, unlike Engage. Integrating these frameworks informs security operations lifecycle detection, monitoring, incident response, and post-event analysis.

Depending on the services, the client handles cloud security and AWS handles cloud infrastructure security. This is crucial for AWS-using businesses. AWS cloud-scale platforms have native security capabilities like these MITRE frameworks.

Amazon Web Services follows MITRE security lifecycle frameworks:

Amazon Inspector finds threat actor-related vulnerabilities, Amazon Macie finds sensitive data exposure, and Amazon Security Lake collects logs for ATT&CK-based threat modelling and risk assessment.

AWS Web Application Firewall (WAF) provides application-layer security, while AWS Identity and Access Management (IAM) and AWS Organisations provide least privilege when implementing preventative measures. Honey tokens are digital decoys that replicate real credentials to attract danger actors and trigger alerts. They may be in AWS Secrets Manager.

Amazon AWS Security Hub centralises security alerts, GuardDuty detects unusual activity patterns, and Amazon Detective investigates irregularities. GuardDuty monitors AWS accounts and workloads to detect attacks automatically.

AWS Step Functions and Lambda automate incident response, containment, and recovery. Real-time DDoS mitigation is provided with AWS Shield and WAF. AWS Security Incident Response was introduced in 2024 to prepare, respond, and recover from security incidents. Threat actors may be rerouted to honeypots or given fake Amazon Simple Storage Service (S3) files.

Security Lake and Detective conduct post-event forensic investigations, while Security Hub and IAM policies use historical trends to improve security. Observing honeypot interactions can change MITRE Engage strategies.

GuardDuty and other AWS security services provide threat intelligence and details on detected threats to MITRE ATT&CK. GuardDuty Extended Threat Detection intelligently detects, correlates, and aligns signals with the MITRE ATT&CK lifecycle to find an attack sequence. A discovery report includes IP addresses, TTPs, AWS API queries, and a description of occurrences. The MITRE strategy and method identification of an activity is highlighted by each discovery signal.

Malicious IP lists, dubious network behaviours, and the AWS API request and user agent can be included. You can automate answers by downloading this extensive JSON data. Interestingly, AWS and MITRE have updated and developed new MITRE ATT&CK cloud matrix methodologies based on real-world threat actor behaviours that target AWS customers, such as modifying S3 bucket lifespan restrictions for data destruction.

Companies may automate detection and response, build security operations using industry-standard procedures, maintain visibility throughout their AWS environment, and improve security controls by aligning AWS security services with MITRE frameworks. Companies can better identify, stop, and fool threat actors using this relationship, boosting their security.

[datascraping001]

Unlock Fashion Intelligence: Scraping Fashion Products from Namshi.com

Unlock Fashion Intelligence: Scraping Fashion Products from Namshi.com

In the highly competitive world of online fashion retail, data is power. Whether you're a trend tracker, competitive analyst, eCommerce business owner, or digital marketer, having access to accurate and real-time fashion product data gives you a serious edge. At DataScrapingServices.com, we offer tailored Namshi.com Fashion Product Scraping Services to help you extract structured and up-to-date product data from one of the Middle East’s leading online fashion retailers.

Namshi.com has emerged as a prominent eCommerce platform, particularly in the UAE and other GCC countries. With a wide range of categories such as men’s, women’s, and kids’ clothing, shoes, bags, beauty, accessories, and premium brands, it offers a treasure trove of data for fashion retailers and analysts. Scraping data from Namshi.com enables businesses to keep pace with shifting fashion trends, monitor competitor pricing, and optimize product listings.

✅ Key Data Fields Extracted from Namshi.com

When scraping Namshi.com, we extract highly relevant product information, including:

Product Name

Brand Name

Price (Original & Discounted)

Product Description

Category & Subcategory

Available Sizes & Colors

Customer Ratings and Reviews

Product Images

SKU/Item Code

Stock Availability

These data points can be customized to meet your specific needs and can be delivered in formats such as CSV, Excel, JSON, or through APIs for easy integration into your database or application.

💡 Benefits of Namshi.com Fashion Product Scraping

1. Competitor Price Monitoring

Gain real-time insights into how Namshi.com prices its fashion products across various categories and brands. This helps eCommerce businesses stay competitive and optimize their pricing strategies.

2. Trend Analysis

Scraping Namshi.com lets you track trending items, colors, sizes, and brands. You can identify which fashion products are popular by analyzing ratings, reviews, and availability.

3. Catalog Enrichment

If you operate an online fashion store, integrating scraped data from Namshi can help you expand your product database, improve product descriptions, and enhance visuals with high-quality images.

4. Market Research

Understanding the assortment, discounts, and promotional tactics used by Namshi helps businesses shape their marketing strategies and forecast seasonal trends.

5. Improved Ad Targeting

Knowing which products are popular in specific regions or categories allows fashion marketers to create targeted ad campaigns for better conversion.

6. Inventory Insights

Tracking stock availability lets you gauge demand patterns, optimize stock levels, and avoid overstock or stockouts.

🌍 Who Can Benefit?

Online Fashion Retailers

Fashion Aggregators

eCommerce Marketplaces

Brand Managers

Retail Analysts

Fashion Startups

Digital Marketing Agencies

At DataScrapingServices.com, we ensure all our scraping solutions are accurate, timely, and fully customizable, with options for daily, weekly, or on-demand extraction.

Best eCommerce Data Scraping Services Provider

Scraping Kohls.com Product Information

Scraping Fashion Products from Namshi.com

Ozon.ru Product Listing Extraction Services

Extracting Product Details from eBay.de

Fashion Products Scraping from Gap.com

Scraping Currys.co.uk Product Listings

Extracting Product Details from BigW.com.au

Macys.com Product Listings Scraping

Scraping Argos.co.uk Home and Furniture Product Listings

Target.com Product Prices Extraction

Amazon Price Data Extraction

Best Scraping Fashion Products from Namshi.com in UAE:

Fujairah, Umm Al Quwain, Dubai, Khor Fakkan, Abu Dhabi, Sharjah, Al Ain, Ajman, Ras Al Khaimah, Dibba Al-Fujairah, Hatta, Madinat Zayed, Ruwais, Al Quoz, Al Nahda, Al Barsha, Jebel Ali, Al Gharbia, Al Hamriya, Jumeirah and more.

📩 Need fashion data from Namshi.com? Contact us at: info@datascrapingservices.com

Visit: DataScrapingServices.com

Stay ahead of the fashion curve—scrape smarter, sell better.

[cloudtopiaa]

From Edge to Cloud: Building Resilient IoT Systems with DataStreamX

Introduction

In today’s hyperconnected digital environment, real-time decision-making is no longer a luxury — it’s a necessity.

Whether it’s managing power grids, monitoring equipment in a factory, or ensuring freshness in a smart retail fridge, organizations need infrastructure that responds instantly to changes in data.

IoT (Internet of Things) has fueled this revolution by enabling devices to sense, collect, and transmit data. However, the true challenge lies in managing and processing this flood of information effectively. That’s where DataStreamX, a real-time data processing engine hosted on Cloudtopiaa, steps in.

Why Traditional IoT Architectures Fall Short

Most traditional IoT solutions rely heavily on cloud-only setups. Data travels from sensors to the cloud, gets processed, and then decisions are made.

This structure introduces major problems:

High Latency: Sending data to the cloud and waiting for a response is too slow for time-sensitive operations.

Reliability Issues: Network outages or poor connectivity can completely halt decision-making.

Inefficiency: Not all data needs to be processed centrally. Much of it can be filtered or processed at the source.

This leads to delayed reactions, overburdened networks, and ineffective systems — especially in mission-critical scenarios like healthcare, defense, or manufacturing.

Enter DataStreamX: What It Is and How It Helps

DataStreamX is a distributed, event-driven platform for processing, analyzing, and routing data as it’s generated, directly on Cloudtopiaa’s scalable infrastructure.

Think of it as the central nervous system of your IoT ecosystem.

Key Capabilities:

Streaming Data Pipelines: Build dynamic pipelines that connect sensors, processing logic, and storage in real time.

Edge-Cloud Synchronization: Process data at the edge while syncing critical insights to the cloud.

Secure Adapters & Connectors: Connect to various hardware, APIs, and databases without compromising security.

Real-Time Monitoring Dashboards: Visualize temperature, motion, voltage, and more as they happen.

Practical Use Case: Smart Industrial Cooling

Imagine a facility with 50+ machines, each generating heat and requiring constant cooling. Traditional cooling is either always-on (inefficient) or reactive (too late).

With DataStreamX:

Sensors track each machine’s temperature.

Edge Node (Cloudtopiaa gateway) uses a threshold rule: if temperature > 75°C, activate cooling.

DataStreamX receives and routes this alert.

Cooling system is triggered instantly.

Cloud dashboard stores logs and creates trend analysis.

Result: No overheating, lower energy costs, and smarter maintenance predictions.

Architecture Breakdown: Edge + Cloud

LayerComponentFunctionEdgeSensors, microcontrollersCollect dataEdge NodeLightweight processing unitFirst level filtering, logicCloudtopiaaDataStreamX engineProcess, store, trigger, visualizeFrontendDashboards, alertsInterface for decision makers

This hybrid model ensures that important decisions happen instantly, even if the cloud isn’t available. And when the connection is restored, everything resyncs automatically.

Advantages for Developers and Engineers

Developer-Friendly

Pre-built connectors for MQTT, HTTP, serial devices

JSON or binary data support

Low-code UI for building data pipelines

Enterprise-Grade Security

Encrypted transport layers

Role-based access control

Audit logs and traceability

Scalable and Flexible

From 10 sensors to 10,000

Auto-balancing workloads

Integrates with your existing APIs and cloud services

Ideal Use Cases

Smart Factories: Predictive maintenance, asset tracking

Healthcare IoT: Patient monitoring, emergency response

Smart Cities: Traffic control, environmental sensors

Retail Tech: Smart fridges, in-store behavior analytics

Utilities & Energy: Grid balancing, consumption forecasting

How to Get Started with DataStreamX

Step 1: Visit https://cloudtopiaa.com Step 2: Log in and navigate to the DataStreamX dashboard Step 3: Add an edge node and configure input/output data streams Step 4: Define business logic (e.g., thresholds, alerts) Step 5: Visualize and manage data in real-time

No coding? No problem. The UI makes it easy to drag, drop, and deploy.

Future Outlook: Smart Systems that Learn

Cloudtopiaa is working on intelligent feedback loops powered by machine learning — where DataStreamX not only responds to events, but learns from patterns.

Imagine a system that can predict when your machinery is likely to fail and take proactive action. Or, a city that automatically balances electricity demand before overloads occur.

This is the future of smart, resilient infrastructure — and it’s happening now.

Conclusion: Real-Time Is the New Standard

From agriculture to aerospace, real-time responsiveness is the hallmark of innovation. DataStreamX on Cloudtopiaa empowers businesses to:

React instantly

Operate reliably

Scale easily

Analyze intelligently

If you’re building smart solutions — whether it’s a smart farm or a smart building — this is your launchpad.

👉 Start your journey: https://cloudtopiaa.com

[actowizsolutions0]

Extract Amazon Product Prices with Web Scraping | Actowiz Solutions

Introduction

In the ever-evolving world of e-commerce, pricing strategy can make or break a brand. Amazon, being the global e-commerce behemoth, is a key platform where pricing intelligence offers an unmatched advantage. To stay ahead in such a competitive environment, businesses need real-time insights into product prices, trends, and fluctuations. This is where Actowiz Solutions comes into play. Through advanced Amazon price scraping solutions, Actowiz empowers businesses with accurate, structured, and actionable data.

Why extract Amazon Product Prices?

Price is one of the most influential factors affecting a customer’s purchasing decision. Here are several reasons why extracting Amazon product prices is crucial:

Competitor Analysis: Stay informed about competitors’ pricing.

Dynamic Pricing: Adjust your prices in real time based on market trends.

Market Research: Understand consumer behavior through price trends.

Inventory & Repricing Strategy: Align stock and pricing decisions with demand.

With Actowiz Solutions’ Amazon scraping services, you get access to clean, structured, and timely data without violating Amazon’s terms.

How Actowiz Solutions Extracts Amazon Price Data

Actowiz Solutions uses advanced scraping technologies tailored for Amazon’s complex site structure. Here’s a breakdown:

1. Custom Scraping Infrastructure

Actowiz Solutions builds custom scrapers that can navigate Amazon’s dynamic content, pagination, and bot protection layers like CAPTCHA, IP throttling, and JavaScript rendering.

2. Proxy Rotation & User-Agent Spoofing

To avoid detection and bans, Actowiz employs rotating proxies and multiple user-agent headers that simulate real user behavior.

3. Scheduled Data Extraction

Actowiz enables regular scheduling of price scraping jobs — be it hourly, daily, or weekly — for ongoing price intelligence.

4. Data Points Captured

The scraping service extracts:

Product name & ASIN

Price (MRP, discounted, deal price)

Availability

Ratings & Reviews

Seller information

Real-World Use Cases for Amazon Price Scraping

A. Retailers & Brands

Monitor price changes for own products or competitors to adjust pricing in real-time.

B. Marketplaces

Aggregate seller data to ensure competitive offerings and improve platform relevance.

C. Price Comparison Sites

Fuel your platform with fresh, real-time Amazon price data.

D. E-commerce Analytics Firms

Get historical and real-time pricing trends to generate valuable reports for clients.

Dataset Snapshot: Amazon Product Prices

Below is a snapshot of average product prices on Amazon across popular categories:

Product CategoryAverage Price (USD)Electronics120.50Books15.75Home & Kitchen45.30Fashion35.90Toys & Games25.40Beauty20.60Sports50.10Automotive75.80

Benefits of Choosing Actowiz Solutions

1. Scalability: From thousands to millions of records.

2. Accuracy: Real-time validation and monitoring ensure data reliability.

3. Customization: Solutions are tailored to each business use case.

4. Compliance: Ethical scraping methods that respect platform policies.

5. Support: Dedicated support and data quality teams

Legal & Ethical Considerations

Amazon has strict policies regarding automated data collection. Actowiz Solutions follows legal frameworks and deploys ethical scraping practices including:

Scraping only public data

Abiding by robots.txt guidelines

Avoiding high-frequency access that may affect site performance

Integration Options for Amazon Price Data

Actowiz Solutions offers flexible delivery and integration methods:

APIs: RESTful APIs for on-demand price fetching.

CSV/JSON Feeds: Periodic data dumps in industry-standard formats.

Dashboard Integration: Plug data directly into internal BI tools like Tableau or Power BI.

Contact Actowiz Solutions today to learn how our Amazon scraping solutions can supercharge your e-commerce strategy.Contact Us Today!

Conclusion: Future-Proof Your Pricing Strategy

The world of online retail is fast-moving and highly competitive. With Amazon as a major marketplace, getting a pulse on product prices is vital. Actowiz Solutions provides a robust, scalable, and ethical way to extract product prices from Amazon.

Whether you’re a startup or a Fortune 500 company, pricing intelligence can be your competitive edge. Learn More

[crawlxpert01]

A Complete Guide to Web Scraping Blinkit for Market Research

Introduction

Having access to accurate data and timely information in the fast-paced e-commerce world is something very vital so that businesses can make the best decisions. Blinkit, one of the top quick commerce players on the Indian market, has gargantuan amounts of data, including product listings, prices, delivery details, and customer reviews. Data extraction through web scraping would give businesses a great insight into market trends, competitor monitoring, and optimization.

This blog will walk you through the complete process of web scraping Blinkit for market research: tools, techniques, challenges, and best practices. We're going to show how a legitimate service like CrawlXpert can assist you effectively in automating and scaling your Blinkit data extraction.

1. What is Blinkit Data Scraping?

The scraping Blinkit data is an automated process of extracting structured information from the Blinkit website or app. The app can extract useful data for market research by programmatically crawling through the HTML content of the website.

>Key Data Points You Can Extract:

Product Listings: Names, descriptions, categories, and specifications.

Pricing Information: Current prices, original prices, discounts, and price trends.

Delivery Details: Delivery time estimates, service availability, and delivery charges.

Stock Levels: In-stock, out-of-stock, and limited availability indicators.

Customer Reviews: Ratings, review counts, and customer feedback.

Categories and Tags: Labels, brands, and promotional tags.

2. Why Scrape Blinkit Data for Market Research?

Extracting data from Blinkit provides businesses with actionable insights for making smarter, data-driven decisions.

>(a) Competitor Pricing Analysis

Track Price Fluctuations: Monitor how prices change over time to identify trends.

Compare Competitors: Benchmark Blinkit prices against competitors like BigBasket, Swiggy Instamart, Zepto, etc.

Optimize Your Pricing: Use Blinkit’s pricing data to develop dynamic pricing strategies.

>(b) Consumer Behavior and Trends

Product Popularity: Identify which products are frequently bought or promoted.

Seasonal Demand: Analyze trends during festivals or seasonal sales.

Customer Preferences: Use review data to identify consumer sentiment and preferences.

>(c) Inventory and Supply Chain Insights

Monitor Stock Levels: Track frequently out-of-stock items to identify high-demand products.

Predict Supply Shortages: Identify potential inventory issues based on stock trends.

Optimize Procurement: Make data-backed purchasing decisions.

>(d) Marketing and Promotional Strategies

Targeted Advertising: Identify top-rated and frequently purchased products for marketing campaigns.

Content Optimization: Use product descriptions and categories for SEO optimization.

Identify Promotional Trends: Extract discount patterns and promotional offers.

3. Tools and Technologies for Scraping Blinkit

To scrape Blinkit effectively, you’ll need the right combination of tools, libraries, and services.

>(a) Python Libraries for Web Scraping

BeautifulSoup: Parses HTML and XML documents to extract data.

Requests: Sends HTTP requests to retrieve web page content.

Selenium: Automates browser interactions for dynamic content rendering.

Scrapy: A Python framework for large-scale web scraping projects.

Pandas: For data cleaning, structuring, and exporting in CSV or JSON formats.

>(b) Proxy Services for Anti-Bot Evasion

Bright Data: Provides residential IPs with CAPTCHA-solving capabilities.

ScraperAPI: Handles proxies, IP rotation, and bypasses CAPTCHAs automatically.

Smartproxy: Residential proxies to reduce the chances of being blocked.

>(c) Browser Automation Tools

Playwright: A modern web automation tool for handling JavaScript-heavy sites.

Puppeteer: A Node.js library for headless Chrome automation.

>(d) Data Storage Options

CSV/JSON: For small-scale data storage.

MongoDB/MySQL: For large-scale structured data storage.

Cloud Storage: AWS S3, Google Cloud, or Azure for scalable storage solutions.

4. Setting Up a Blinkit Scraper

>(a) Install the Required Libraries

First, install the necessary Python libraries:pip install requests beautifulsoup4 selenium pandas

>(b) Inspect Blinkit’s Website Structure

Open Blinkit in your browser.

Right-click → Inspect → Select Elements.

Identify product containers, pricing, and delivery details.

>(c) Fetch the Blinkit Page Content

import requests from bs4 import BeautifulSoup url = 'https://www.blinkit.com' headers = {'User-Agent': 'Mozilla/5.0'} response = requests.get(url, headers=headers) soup = BeautifulSoup(response.content, 'html.parser')

>(d) Extract Product and Pricing Data

products = soup.find_all('div', class_='product-card') data = [] for product in products: try: title = product.find('h2').text price = product.find('span', class_='price').text availability = product.find('div', class_='availability').text data.append({'Product': title, 'Price': price, 'Availability': availability}) except AttributeError: continue

5. Bypassing Blinkit’s Anti-Scraping Mechanisms

Blinkit uses several anti-bot mechanisms, including rate limiting, CAPTCHAs, and IP blocking. Here’s how to bypass them.

>(a) Use Proxies for IP Rotation

proxies = {'http': 'http://user:pass@proxy-server:port'} response = requests.get(url, headers=headers, proxies=proxies)

>(b) User-Agent Rotation

import random user_agents = [ 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)', 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)' ] headers = {'User-Agent': random.choice(user_agents)}

>(c) Use Selenium for Dynamic Content

from selenium import webdriver options = webdriver.ChromeOptions() options.add_argument('--headless') driver = webdriver.Chrome(options=options) driver.get(url) data = driver.page_source driver.quit() soup = BeautifulSoup(data, 'html.parser')

6. Data Cleaning and Storage

After scraping the data, clean and store it: import pandas as pd df = pd.DataFrame(data) df.to_csv('blinkit_data.csv', index=False)

7. Why Choose CrawlXpert for Blinkit Data Scraping?

While building your own Blinkit scraper is possible, it comes with challenges like CAPTCHAs, IP blocking, and dynamic content rendering. This is where CrawlXpert can help.

>Key Benefits of CrawlXpert:

Accurate Data Extraction: Reliable and consistent Blinkit data scraping.

Large-Scale Capabilities: Efficient handling of extensive data extraction projects.

Anti-Scraping Evasion: Advanced techniques to bypass CAPTCHAs and anti-bot systems.

Real-Time Data: Access fresh, real-time Blinkit data with high accuracy.

Flexible Delivery: Multiple data formats (CSV, JSON, Excel) and API integration.

Conclusion

This web scraping provides valuable information on price trends, product existence, and consumer preferences for businesses interested in Blinkit. You can effectively extract any data from Blinkit, analyze it well, using efficient tools and techniques. However, such data extraction would prove futile because of the high level of anti-scraping precautions instituted by Blinkit, thus ensuring reliable, accurate, and compliant extraction by partnering with a trusted provider, such as CrawlXpert.

CrawlXpert will further benefit you by providing powerful market insight, improved pricing strategies, and even better business decisions using higher quality Blinkit data.

Know More : https://www.crawlxpert.com/blog/web-scraping-blinkit-for-market-research

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Understanding API Gateways in Modern Application Architecture

Sure! Here's a brand new 700-word blog on the topic: "Understanding API Gateways in Modern Application Architecture" — written in simple language with no bold formatting, and includes mentions of Hexadecimal Software and Hexahome Blogs at the end.

Understanding API Gateways in Modern Application Architecture

In today's world of cloud-native applications and microservices, APIs play a very important role. They allow different parts of an application to communicate with each other and with external systems. As the number of APIs grows, managing and securing them becomes more challenging. This is where API gateways come in.

An API gateway acts as the single entry point for all client requests to a set of backend services. It simplifies client interactions, handles security, and provides useful features like rate limiting, caching, and monitoring. API gateways are now a key part of modern application architecture.

What is an API Gateway?

An API gateway is a server or software that receives requests from users or applications and routes them to the appropriate backend services. It sits between the client and the microservices and acts as a middle layer.

Instead of making direct calls to multiple services, a client sends one request to the gateway. The gateway then forwards it to the correct service, collects the response, and sends it back to the client. This reduces complexity on the client side and improves overall control and performance.

Why Use an API Gateway?

There are many reasons why modern applications use API gateways:

Centralized access: Clients only need to know one endpoint instead of many different service URLs.

Security: API gateways can enforce authentication, authorization, and encryption.

Rate limiting: They can prevent abuse by limiting the number of requests a client can make.

Caching: Responses can be stored temporarily to improve speed and reduce load.

Load balancing: Requests can be distributed across multiple servers to handle more traffic.

Logging and monitoring: API gateways help track request data and monitor service health.

Protocol translation: They can convert between protocols, like from HTTP to WebSockets or gRPC.

Common Features of API Gateways

Authentication and authorization Ensures only valid users can access certain APIs. It can integrate with identity providers like OAuth or JWT.

Routing Directs requests to the right service based on the URL path or other parameters.

Rate limiting and throttling Controls how many requests a user or client can make in a given time period.

Data transformation Changes request or response formats, such as converting XML to JSON.

Monitoring and logging Tracks the number of requests, response times, errors, and usage patterns.

API versioning Allows clients to use different versions of an API without breaking existing applications.

Future of API Gateways

As applications become more distributed and cloud-based, the need for effective API management will grow. API gateways will continue to evolve with better performance, security, and integration features. They will also work closely with service meshes and container orchestration platforms like Kubernetes.

With the rise of event-driven architecture and real-time systems, future API gateways may also support new communication protocols and smarter routing strategies.

About Hexadecimal Software

Hexadecimal Software is a trusted expert in software development and cloud-native technologies. We help businesses design, build, and manage scalable applications with modern tools like API gateways, microservices, and container platforms. Whether you are starting your cloud journey or optimizing an existing system, our team can guide you at every step. Visit us at https://www.hexadecimalsoftware.com

Explore More on Hexahome Blogs

For more blogs on cloud computing, DevOps, and software architecture, visit https://www.blogs.hexahome.in. Our blog platform shares easy-to-understand articles for both tech enthusiasts and professionals who want to stay updated with the latest trends.