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What is Yozh Scraper?

Yozh Scraper is a sophisticated open-source toolkit crafted for web scraping and crawling, tailored to handle substantial data extraction endeavors. By leveraging Playwright, Python, and Redis, it skillfully maneuvers through complex JavaScript-rendered sites while adeptly bypassing modern anti-bot defenses. Notable Features: • Anti-Detection Scraping: Employs Camoufox in conjunction with authentic Chrome instances to mask browser fingerprints, allowing it to effectively navigate rigorous anti-scraping systems. • Dual Microservices Architecture: Provides an asynchronous Scraper API for page rendering, alongside an Open Crawler that includes SSE streaming, site-mapping, and deduplication functionalities. • Native MCP Integration: Effortlessly connects with AI agents like Claude Code/Desktop, LangChain, and n8n via built-in Model Context Protocol (/mcp) endpoints. • Intelligent Parsing & Configurations: Comes pre-configured for well-known platforms such as Amazon, Google, LinkedIn, and others, featuring optional self-healing parsing driven by LLMs. • Scalable Enterprise Solutions: Promotes horizontal scaling using Docker Compose, supports multiple types of proxies (Residential/Mobile/Data Center), and offers an intuitive web interface for testing. • This robust toolkit is particularly beneficial for developers aiming to enhance their data extraction workflows while ensuring adherence to anti-bot regulations, making it a valuable asset in the realm of data-driven projects.

What is Amazon EC2 Auto Scaling?

Amazon EC2 Auto Scaling promotes application availability by automatically managing the addition and removal of EC2 instances according to your defined scaling policies. With the help of dynamic or predictive scaling strategies, you can tailor the capacity of your EC2 instances to address both historical trends and immediate changes in demand. The fleet management features of Amazon EC2 Auto Scaling are specifically crafted to maintain the health and availability of your instance fleet effectively. In the context of efficient DevOps practices, automation is essential, and one significant hurdle is ensuring that fleets of Amazon EC2 instances can autonomously launch, configure software, and recover from any failures that may occur. Amazon EC2 Auto Scaling provides essential tools for automating every stage of the instance lifecycle. Additionally, integrating machine learning algorithms can enhance the ability to predict and optimize the required number of EC2 instances, allowing for better management of expected shifts in traffic. By utilizing these sophisticated capabilities, organizations can significantly boost their operational effectiveness and adaptability to fluctuating workload requirements. This proactive approach not only minimizes downtime but also maximizes resource utilization across their infrastructure.

Media

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Integrations Supported

Amazon EC2
Amazon EC2 UltraClusters
Amazon Web Services (AWS)
CyberYozh
Observe

Integrations Supported

Amazon EC2
Amazon EC2 UltraClusters
Amazon Web Services (AWS)
CyberYozh
Observe

API Availability

Has API

API Availability

Has API

Pricing Information

$0
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

CyberYozh

Date Founded

2014

Company Location

Serbia

Company Website

data.cyberyozh.pro/

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/ec2/autoscaling/

Categories and Features

Categories and Features

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