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IT & Software Development Vocabulary Guide

category: Technology
tags: IT, software, development, technology, programming

Software Development Lifecycle (SDLC)

Understanding the complete process of software creation and maintenance.

Development Methodologies

Agile - Iterative development approach with short cycles
Scrum - Agile framework with defined roles and ceremonies
Kanban - Visual workflow management method
Sprint - Short development cycle (1-4 weeks)
Stand-up - Daily team synchronization meeting
Retrospective - Meeting to improve team processes
Waterfall - Sequential development approach
DevOps - Culture combining development and operations
Continuous Integration (CI) - Frequently integrating code changes
Continuous Deployment (CD) - Automatically deploying code to production
Lean development - Eliminating waste in development process
Extreme Programming (XP) - Agile methodology emphasizing technical practices
Feature-driven development - Organizing around feature sets
Test-driven development (TDD) - Writing tests before code
Behavior-driven development (BDD) - Focusing on behavior specifications

Development Phases

Requirements gathering - Understanding what needs to be built
Design phase - Planning system architecture and user interface
Implementation - Writing the actual code
Testing - Verifying software works as expected
Deployment - Releasing software to users
Maintenance - Ongoing support and updates
Code review - Peer examination of code changes
Quality assurance (QA) - Systematic testing of software
User acceptance testing (UAT) - Final testing by end users
Integration testing - Testing how components work together
System testing - Testing complete integrated system
Regression testing - Ensuring new changes don't break existing functionality
Performance testing - Testing system speed and responsiveness
Security testing - Testing for vulnerabilities
Load testing - Testing system under expected usage

Architecture & System Design

Core concepts for understanding how software systems are structured.

System Architecture

Monolithic architecture - Single deployable unit containing all functionality
Microservices - Breaking application into small, independent services
Service-oriented architecture (SOA) - Services communicate through well-defined interfaces
Serverless - Running code without managing servers
Event-driven architecture - Components communicate through events
API-first design - Building APIs before user interfaces
Three-tier architecture - Presentation, application, and data layers
N-tier architecture - Multiple layers for different functions
Client-server architecture - Clients request services from servers
Peer-to-peer architecture - Nodes act as both clients and servers
Distributed architecture - Components spread across multiple machines
Centralized architecture - All processing done in central location
Layered architecture - Organized into horizontal layers
Component-based architecture - Built from reusable components
Plugin architecture - Core system extended through plugins

Technical Concepts

Scalability - Ability to handle increased load
Horizontal scaling - Adding more servers
Vertical scaling - Adding more power to existing servers
Load balancing - Distributing traffic across multiple servers
Caching - Storing frequently accessed data for faster retrieval
Database sharding - Splitting database across multiple machines
Redundancy - Having backup systems to prevent failure
Fault tolerance - System continues operating despite failures
High availability - System remains operational most of the time
Disaster recovery - Plan for restoring systems after major failure
Failover - Switching to backup system when primary fails
Circuit breaker - Preventing cascading failures in distributed systems
Rate limiting - Controlling the rate of requests
Throttling - Limiting resource usage
Monitoring - Continuously observing system performance

Cloud Computing & Infrastructure

Modern approaches to hosting and managing technology resources.

Cloud Service Models

SaaS (Software as a Service) - Complete applications delivered over internet
PaaS (Platform as a Service) - Platform for developing and deploying applications
IaaS (Infrastructure as a Service) - Virtual computing resources
FaaS (Function as a Service) - Running individual functions in cloud
BaaS (Backend as a Service) - Backend services for mobile/web apps
DaaS (Data as a Service) - Data delivered on-demand over network
CaaS (Container as a Service) - Container orchestration platforms
STaaS (Storage as a Service) - Cloud-based storage solutions
DBaaS (Database as a Service) - Managed database services
MLaaS (Machine Learning as a Service) - AI/ML capabilities as service

Cloud Concepts

Multi-cloud - Using services from multiple cloud providers
Hybrid cloud - Combination of private and public cloud
Private cloud - Cloud infrastructure for single organization
Public cloud - Cloud services available to general public
Community cloud - Shared cloud for specific community
Cloud migration - Moving applications and data to cloud
Auto-scaling - Automatically adjusting resources based on demand
Elasticity - Ability to scale resources up or down quickly
Container - Lightweight package containing application and dependencies
Orchestration - Automated management of containers
Virtualization - Creating virtual versions of physical resources
Edge computing - Processing data closer to where it's generated
CDN (Content Delivery Network) - Distributed servers for faster content delivery
API Gateway - Entry point for managing APIs
Service mesh - Infrastructure layer for service-to-service communication

Data Management & Analytics

Understanding how organizations handle and analyze data.

Database Technologies

SQL - Structured Query Language for relational databases
NoSQL - Non-relational database types
Document database - Storing data as documents (MongoDB, CouchDB)
Key-value store - Simple database storing key-value pairs (Redis, DynamoDB)
Graph database - Storing data as nodes and relationships (Neo4j)
Column-family - Storing data in column families (Cassandra)
Data warehouse - Central repository for integrated data
Data lake - Storage for raw data in various formats
Data mart - Subset of data warehouse for specific business area
ETL (Extract, Transform, Load) - Process for moving and cleaning data
ELT (Extract, Load, Transform) - Loading raw data then transforming
Data pipeline - Series of data processing steps
Master data management - Ensuring consistency of key business data
Data governance - Management of data availability, usability, integrity
Data quality - Ensuring data is accurate, complete, and reliable

Data Analytics

Big Data - Extremely large datasets requiring special tools
Business Intelligence (BI) - Technologies for analyzing business data
Machine Learning - Algorithms that improve automatically through experience
Artificial Intelligence (AI) - Computer systems performing human-like tasks
Data mining - Discovering patterns in large datasets
Predictive analytics - Using data to predict future outcomes
Descriptive analytics - Understanding what happened in the past
Prescriptive analytics - Recommending actions based on data
Real-time analytics - Analyzing data as it's generated
Batch processing - Processing data in large chunks
Stream processing - Processing data continuously as it flows
Data visualization - Presenting data in graphical format
Dashboard - Visual display of key metrics and data
KPI dashboard - Dashboard focused on key performance indicators
Reporting - Regular presentation of data and analysis

Security & Compliance

Critical concepts for protecting systems and data.

Security Fundamentals

Authentication - Verifying identity of users
Authorization - Determining what authenticated users can access
Encryption - Converting data into coded format
Firewall - Network security system controlling traffic
VPN (Virtual Private Network) - Secure connection over public network
Zero-trust security - Never trust, always verify approach
Multi-factor authentication (MFA) - Using multiple verification methods
Single sign-on (SSO) - One login for multiple applications
Identity and access management (IAM) - Managing user identities and permissions
Role-based access control (RBAC) - Permissions based on user roles
Principle of least privilege - Giving minimum necessary access
Security by design - Building security into systems from start
Defense in depth - Multiple layers of security controls
Network segmentation - Dividing network into separate segments
Intrusion detection system (IDS) - Monitoring for malicious activity

Security Practices

Penetration testing - Simulated cyber attacks to find vulnerabilities
Vulnerability assessment - Systematic review of security weaknesses
Security audit - Comprehensive evaluation of security measures
Incident response - Plan for handling security breaches
Backup and recovery - Protecting against data loss
Business continuity - Maintaining operations during disruptions
Disaster recovery - Restoring operations after major incident
Risk management - Identifying and mitigating security risks
Compliance - Adhering to regulations and standards
Security awareness training - Educating users about security
Patch management - Keeping software updated with security fixes
Threat modeling - Identifying potential security threats
Security monitoring - Continuously watching for security issues
Forensics - Investigating security incidents
Data loss prevention (DLP) - Preventing unauthorized data disclosure

Integration & Interoperability

Connecting different systems and applications.

Integration Concepts

API (Application Programming Interface) - Way for software to communicate
REST API - Web-based API following REST principles
GraphQL - Query language for APIs
SOAP - Protocol for exchanging structured information
Webhook - HTTP callback triggered by events
Message queue - System for storing messages between applications
Enterprise service bus (ESB) - Infrastructure for integrating applications
Middleware - Software connecting different applications
Data integration - Combining data from different sources
Application integration - Connecting different software applications
B2B integration - Connecting systems between different companies
Cloud integration - Connecting cloud and on-premises systems
Real-time integration - Immediate data synchronization
Batch integration - Periodic data synchronization
Point-to-point integration - Direct connection between two systems

Key Concepts Summary

Technical debt - Shortcuts that create future maintenance costs
MVP (Minimum Viable Product) - Basic version with core features only
Legacy system - Older technology still in use but needing updates
Migration - Moving from one system/platform to another
Integration - Connecting different systems to work together
Refactoring - Improving code structure without changing functionality
Version control - Tracking changes to code over time
Repository - Central location where code is stored
Branching - Creating separate lines of development
Deployment pipeline - Automated process for releasing software
Environment - Separate instance of application (dev, test, prod)
Configuration management - Managing system configurations
Infrastructure as code - Managing infrastructure through code
Observability - Understanding system behavior through monitoring
Documentation - Written information about systems and processes

Best Practices / Tips

Start with fundamentals - Understand basic concepts before diving into complex topics
Hands-on learning - Practice with actual tools and technologies
Stay updated - Technology vocabulary evolves rapidly
Context matters - Same terms may have different meanings in different contexts
Ask questions - Technical teams usually appreciate curiosity about their work
Learn acronyms - IT is full of acronyms, keep a reference list
Understand relationships - Many concepts are interconnected

Common Issues / Troubleshooting

Acronym Overload

Symptom: Conversations filled with unexplained acronyms
Cause: Technical teams often use shorthand without thinking
Solution: Keep a running list of acronyms and their meanings, ask for clarification

Mixing Up Similar Terms

Symptom: Using terms interchangeably when they have different meanings
Cause: Many technical terms sound similar but have distinct meanings
Solution: Create comparison charts for similar terms

Version Confusion

Symptom: Discussions about different versions of same technology
Cause: Technology evolves rapidly with frequent updates
Solution: Always clarify which version is being discussed

Scope Confusion

Symptom: Not understanding what a technology does or doesn't do
Cause: Technologies often have overlapping capabilities
Solution: Ask for specific use cases and examples

References / Further Reading

Stack Overflow - Programming Q&A community
GitHub - Code repository and collaboration platform
AWS/Azure/Google Cloud documentation
Mozilla Developer Network (MDN) - Web development resources
TechTarget - IT terminology definitions
IEEE Computer Society - Technical standards and definitions
NIST - Technology standards and guidelines

Last updated: 2025-08-26 20:00 UTC