Software training Videos And Materials

Friday, 5 December 2025

Linux System Administrator with AWS Cloud Online training

🐧 Linux System Administrator with AWS Cloud ☁️

Duration: 35 Days

📚 Introduction to Linux System

Concepts: Basic concepts of Linux.
Distro Differences: Differences between Red Hat Enterprise Linux & CentOS.
Shell Basics: Basic bash commands of Linux.

⚙️ Core Linux System Administration

💾 Disk Management: Managing Partitions and File Systems.
🗃️ Storage Volume: Logical Volume Management (LVM) and RAID Levels.
👤 Access Control: User and Group Administration, SUDO, and Permissions.
🌐 Connectivity: Network Configuration and Troubleshooting.
🛡️ Security: Managing SELinux.
🔄 System Startup: Booting Procedure and Kernel parameters.
⏰ Automation: Job Automation (e.g., Cron).
🖥️ Remote Access: Administrating Remote Systems (SSH).
🧠 Resource Management: Memory Management (Swap).
📦 Software: Software Management (e.g., YUM/RPM).
↩️ Data Safety: Backup and Restore.
🔧 System Daemons: Managing Installed Services.
🔬 Process Control: Managing Processes.

📡 Server Configuration & Networking Services

📤 File Transfer: FTP (File Transfer Protocol) Server.
🔗 Network Sharing: NFS (Network File System) Server, Autofs, and LDAP Client.
🗂️ Windows Interop: Samba Server.
⏱️ Time Sync: NTP (Network Time Protocol) or Chrony.
📍 Naming Service: DNS (Domain Naming System).
🔌 IP Assignment: DHCP (Dynamic Host Configuration Protocol).
🌎 Web Hosting: Web Server (Apache).
📧 Communication: Mail Server.
☁️ Remote Storage: ISCSI (Remote Storage).
🗄️ Databases: MySQL Server and MariaDB.
📝 Monitoring: Log Server and Log Files.
🔥 Firewall: Configuring IPtables and Firewall.
💻 Infrastructure: Virtualization.

🚀 Advanced Deployment & High Availability

💿 Automated Install: Kickstart Installation and PXE (Network) Installation.
⚖️ High Availability: VERITAS Volume Manager and VERITAS Cluster (Note: Number 28 seems to be missing in the original content).
🛑 Issue Resolution: Troubleshooting Linux.

☁️ AWS with Linux Overview

Overview: AWS with Linux Overview.
- Instances: EC2 Instance
- Traffic: Load Balancer
- Storage: S3 Bucket
- Security: IAM (Identity and Access Management)
- Networking: VPC (Virtual Private Cloud)
- Monitoring: CloudWatch
- Gateway: NAT (Network Address Translation)
- Data Tier: RDS Data Base (Relational Database Service)

💡 Project-Based Learning

🏗️ Practical: Real Time Project based live Workshops.

📞 Contact Information

For More Details Please Contact- +91 9059868766

Wednesday, 3 December 2025

Cyber Security Fundamentals and Vulnerability Management Training

🛡️ Module 1: Cyber Security Fundamentals

This module provides the essential foundation of cybersecurity, its principles, and the landscape of threats.

1.1 Introduction and Core Concepts

What is Cybersecurity?
The CIA Triad (Confidentiality, Integrity, Availability)

Getty Images

Different careers in Cyber Security.
Cybersecurity Terminology & Frameworks (e.g., NIST, ISO 27001).

1.2 Threats and Attacks

Types of Threats:
- Malware (Viruses, Worms, Trojans)
- Phishing and other social engineering attacks.
- Insider threats.
- Ransomware.
Common Cyberattacks & Threat Actors:
- Social engineering.
- DDoS (Distributed Denial of Service).
- Brute force attacks.
- Advanced Persistent Threats (APT).

1.3 Defense Mechanisms and Best Practices

Security Policies & Best Practices.
User Security: Password hygiene and Multi-Factor Authentication (MFA).
Access Control: Access control models and the Least Privilege Principle.
Secure Configuration Practices (Hardening).
Network Fundamentals (Review of networking concepts).
Network Security Mechanisms (Firewalls, IDS/IPS, VPNs).
Endpoint & Server Security.

🔍 Module 2: Introduction to Vulnerability Management

This module introduces the key concepts, terminology, and importance of managing vulnerabilities.

2.1 Foundational Vulnerability Concepts

Definition and importance of Vulnerability Management in cybersecurity.
Difference between vulnerabilities, threats, and risks.
Common vulnerability types (e.g., misconfigurations, outdated software, design flaws).
Understanding and identifying vulnerabilities:
- What is a CVE? (Common Vulnerabilities and Exposures).
- What is the CVSS scoring system? (Common Vulnerability Scoring System).
- What is NVD? (National Vulnerability Database).

2.2 The Vulnerability Management Lifecycle

Detailed review of the six stages of the Vulnerability Management Lifecycle:
1. Discover – Identifying Assets and Vulnerabilities.
2. Assess – Analyzing and Validating Vulnerabilities.
3. Prioritize – Determining What to Fix First.
4. Remediate – Fixing and Mitigating Vulnerabilities.
5. Verify – Confirming the Effectiveness of Fixes.
6. Report – Communicating Results and Insights.
Roles and responsibilities in Vulnerability Management.

⚙️ Module 3: Vulnerability Identification and Assessment

This module focuses on the practical techniques used to find, scan, and interpret vulnerabilities.

3.1 Asset and Scope Management

Asset discovery and inventory management.
Vulnerability Scanning Tools: Selection criteria, licensing, and deployment models.
Setting scan scopes, credentials, and schedules.
Avoiding disruptions in production environments.

3.2 Scanning Techniques and Results

Active vs passive scanning.
Authenticated vs unauthenticated scans.
Common vulnerability scanning challenges.
Interpreting Scan Results (Understanding the output from scanning tools).

🎯 Module 4: Prioritization and Remediation

This module covers how to move from a list of vulnerabilities to effective mitigation and repair.

4.1 Prioritization Strategies

How to map findings to asset criticality.
Prioritization Strategies: Using CVSS, threat intelligence, and business context.
Risk-Based Vulnerability Management (RBVM).

4.2 Remediation and Mitigation

Vulnerability Remediation & Mitigation Techniques (Patching, configuration changes, workarounds).
Setting vulnerability remediation SLAs (Service Level Agreements) based on severity and risk levels.
Patch Management Best Practices:
- Patch lifecycle.
- Testing and deployment.
- Rollback procedures.

📈 Module 5: Program Management and Integration

The final module focuses on building, maintaining, and integrating a formal Vulnerability Management program.

5.1 Reporting and Metrics

Vulnerability remediation Reporting & Metrics (e.g., Time to Remediate, Coverage %).

5.2 Building the Program

Building a Vulnerability Management Program (Strategy and governance).
Drafting a Vulnerability Management policy.
Creating process flow diagrams and escalation paths.

Shutterstock

Explore

Integrating VM with Other Security Processes:
- Ties to Incident Response.
- Integration with SOC operations.
- Use of Threat Intelligence.

what is Value in Big data in data analytics , exaplin with examples

💡 Value in Big Data Analytics

In the context of Big Data analytics, Value is the usefulness and measurable business benefit that an organization can derive from effectively processing and analyzing its large, diverse, and rapidly changing datasets.

Value is often considered one of the "V's" of Big Data (alongside Volume, Velocity, Variety, and Veracity). Data itself is a raw resource; its true worth is unlocked only when it is transformed into actionable insights that lead to improved decision-making, greater operational efficiency, increased revenue, or better customer experiences.

🎯 Key Ways Big Data Creates Value

The value from Big Data is realized through various business outcomes:

Improved Decision-Making: Moving from intuition-based decisions to data-driven choices.
- Example: A retail chain analyzes historical sales data, local weather patterns, and social media sentiment to predict demand for specific products at specific stores. This leads to stocking the correct inventory (Value: reduced waste from overstocking and increased sales from fewer stockouts).
Enhanced Customer Experience and Personalization: Understanding individual customer behaviors and preferences at a granular level.
- Example: A streaming service like Netflix analyzes viewing history, search queries, and content ratings (Variety and Volume of data) to build highly personalized user profiles. They then use these profiles to recommend movies and shows tailored to each user. (Value: increased customer engagement and reduced customer churn).
Operational Efficiency and Cost Reduction: Optimizing internal processes, often through automation and predictive maintenance.
- Example: A manufacturing company uses IoT sensor data from its factory equipment (Velocity and Variety) to predict when a machine is likely to fail. They schedule maintenance before the failure occurs. (Value: less unscheduled downtime, lower repair costs, and more efficient production).
Risk Management and Fraud Detection: Identifying abnormal patterns or potential threats in real time.
- Example: A bank monitors the billions of daily transactions and user login patterns (High Velocity) to flag suspicious activities that deviate from a customer's normal behavior. (Value: real-time fraud prevention and minimized financial losses).
Innovation and New Revenue Streams: Discovering new market opportunities or developing new products/services based on data.
- Example: A car manufacturer analyzes vehicle performance data (telematics) to identify common stress points or feature requests. This data helps them design better, more reliable next-generation vehicles or even offer new premium maintenance services. (Value: competitive advantage and new product revenue).

Value is the ultimate goal of any Big Data initiative; it measures the Return on Investment (ROI) for the effort and resources spent on collecting, managing, and analyzing the massive datasets.

what is Veracity in Big data in data analytics , exaplin with examples

Veracity in Big Data refers to the quality, accuracy, and trustworthiness of the data. It is one of the "Vs" often used to describe the challenges and characteristics of big data (alongside Volume, Velocity, and Variety).

🎯 Understanding Veracity

When dealing with the massive scale (Volume) and rapid generation (Velocity) of diverse data types (Variety), the quality of that data is often inconsistent and challenging to control. Veracity addresses the inherent uncertainty in the data and the degree to which it can be relied upon for analysis and decision-making.

High veracity data is clean, reliable, consistent, and error-free, ensuring that the insights derived from it are accurate. Low veracity data, conversely, contains a significant amount of noise (irrelevant or non-valuable information), inconsistencies, biases, or errors, which can lead to flawed analysis and costly business mistakes.

Shutterstock

Explore

💡 Sources of Low Veracity

Veracity issues can stem from several factors:

Inconsistencies: Data from different sources may use conflicting formats (e.g., one system lists "CA" for California, another lists "Calif.").
Ambiguity or Uncertainty: Unstructured data, such as social media posts or sensor readings, can be vague or open to multiple interpretations.
Noise: Irrelevant or corrupted data points (e.g., a sensor recording a clearly impossible temperature reading).
Bias: Data collection methods or sources may unintentionally favor certain outcomes, skewing the overall representation.
Human Error: Mistakes during manual data entry, processing, or labeling.
Security Issues: Data that has been tampered with or falsified.

🏢 Examples in Data Analytics

Here are two examples demonstrating the impact of veracity in real-world data analytics:

1. E-commerce Customer Sentiment Analysis

Low Veracity Scenario	High Veracity Scenario
Problem: An e-commerce company collects millions of product reviews. The data includes many fake or automated (bot-generated) reviews, which are difficult to distinguish from genuine customer feedback.	Solution: The company uses advanced algorithms (like machine learning and anomaly detection) to filter out bot-generated comments, duplicate reviews, and reviews that are statistically out of line with customer history.
Impact: If the analysis is based on low-veracity data, the company might mistakenly conclude that a product is highly rated (due to fake positive reviews) or poorly rated (due to competitor-generated negative reviews). This leads to poor inventory decisions, misguided marketing campaigns, and ultimately, wasted resources.	Impact: By analyzing high-veracity data, the company gets an accurate picture of customer satisfaction. They can confidently improve genuinely criticized products or invest more in marketing successful ones, leading to better product development and increased sales.

2. Autonomous Vehicle Sensor Data

Low Veracity Scenario	High Veracity Scenario
Problem: An autonomous vehicle relies on real-time data from various sensors (Lidar, camera, radar) to make driving decisions. Due to a software bug or a faulty sensor, the system receives inconsistent or noisy readings (e.g., misidentifying a plastic bag on the road as a large obstacle).	Solution: The system has robust data validation checks (data cleansing and consistency algorithms) that compare input from multiple, redundant sensors. It can cross-reference the data with known objects and historical patterns to confirm the reading's accuracy.
Impact: Low veracity leads to unreliable decision-making, such as the car performing an unnecessary emergency stop for a harmless object or, worse, failing to recognize a real hazard. This compromises safety and trust in the technology.	Impact: High veracity ensures the car's decisions are safe and reliable. The system trusts the data to differentiate between a critical obstacle and minor road debris, ensuring a smooth, safe, and efficient driving experience.

what is Variety in Big data in data analytics , exaplin with examples

Variety** in Big Data refers to the diversity of data types and sources that organizations need to manage, analyze, and process to gain insights. It is one of the three (or more) "Vs" (Volume, Velocity, Variety, etc.) that define Big Data.

🧭 Understanding Data Variety

The complexity of data variety arises because data is no longer confined to neat, organized rows and columns in traditional databases. It now comes from numerous, heterogeneous sources and exists in different formats, structures, and types. This requires specialized tools and techniques for effective analysis.

The concept of Variety is typically broken down into three main categories based on structure:

1. Structured Data 📊

This data is highly organized and fits neatly into traditional relational databases with fixed fields and defined schemas. It is the most straightforward to store, manage, and analyze using conventional methods.

Examples:
- Transaction Data: Records of sales (e.g., date, amount, product ID, customer ID).
- Relational Database Tables: Employee records (e.g., name, salary, department).
- Sensor Data: Simple numerical readings from IoT devices (e.g., temperature in degrees Celsius).

2. Semi-Structured Data 📝

This data has some organizational properties (like tags or markers) that can group or separate data elements, but it does not conform to the rigid structure of a relational database. It sits between structured and unstructured data.

Examples:
- XML and JSON Files: Data transferred between web applications, where tags define the data elements but the overall structure can be flexible.
- Email: The header fields (Sender, Recipient, Subject, Date) are structured, but the body of the message is unstructured text.
- Web Log Files: Records of user activity on a website, often containing semi-structured fields like timestamps and IP addresses alongside less structured details.

3. Unstructured Data 📹

This data lacks a predefined format or schema and cannot be easily stored in a traditional database table. It is the most challenging type to process and analyze, often requiring techniques like Natural Language Processing (NLP) and machine learning. Estimates suggest this type makes up the majority of modern enterprise data.

Examples:
- Text: Social media posts (tweets), customer reviews, doctor's clinical notes, and legal documents.
- Multimedia: Images, videos, and audio recordings.
- Satellite Imagery: Geospatial data used for monitoring environmental changes.

🎯 Example of Variety in Data Analytics

A Retail Company wants to get a comprehensive view of a new product launch. To do this, they must pull and analyze data from various sources and formats (Variety):

Data Type	Source & Format	How it Contributes to Insight
Structured	Sales Database (SQL tables, fixed format)	Daily unit sales, revenue figures, and inventory levels.
Semi-Structured	Website/App Log Files (JSON or XML)	User clickstreams, session durations, and error reports to understand online engagement.
Unstructured	Social Media (Text, Images, Video)	Text (tweets, comments) for sentiment analysis; Images/Video for tracking mentions and unboxing content.
Unstructured	Customer Service Records (Text documents/Audio)	Transcripts of calls and chat logs to identify common issues, complaints, and feature requests.

By combining and analyzing this variety of data, the company can form a richer, more accurate picture: sales are high (Structured), but customer service complaints are spiking (Unstructured/Semi-Structured), indicating a quality control or setup issue with the product.

Pages

Translate