Loading
Essential terminology across all disciplines. Search, filter, and bookmark terms for quick reference.
Application Programming Interface — a set of rules allowing different software systems to communicate with each other. REST APIs and GraphQL are the most common types in web development.
Code that runs non-blocking, allowing multiple operations to proceed simultaneously without waiting for one to complete. Uses callbacks, promises, or async/await in JavaScript.
Continuous Integration / Continuous Deployment — automated workflows that test code when changes are submitted (CI) and automatically deploy those changes to production (CD).
A peer-review process where engineers read and comment on each other's code before it is merged. Improves quality, shares knowledge, and catches bugs early.
The process of identifying, isolating, and fixing defects (bugs) in software code.
A software design pattern where objects are provided their dependencies from outside rather than creating them internally. Improves testability and loose coupling.
A culture and practice combining development and operations — emphasizing automation, collaboration, and continuous delivery to shorten the feedback loop between code and production.
A containerization platform that packages applications and their dependencies into portable containers, ensuring consistent behavior across environments.
A software principle emphasizing code reuse to reduce redundancy. Avoids copying code across multiple places, which makes maintenance harder and bugs more likely.
A developer who works across both the frontend (user interface) and backend (server/database) layers of an application.
A distributed version control system used to track changes in code over time, enable collaboration, and manage branches of development.
A query language for APIs that allows clients to request exactly the data they need. Contrasts with REST, which returns fixed data structures.
Managing and provisioning infrastructure (servers, networks, databases) through code and configuration files rather than manual processes. Terraform is a common tool.
An open-source container orchestration platform that automates deployment, scaling, and management of containerized applications.
The delay or time it takes for a request to travel from a client to a server and receive a response. Critical metric for user experience and system performance.
An architectural style where an application is built as a collection of small, independently deployable services — each responsible for a specific business capability.
A single source code repository that contains the code for multiple projects or services. Contrasted with a polyrepo (separate repository per service).
Software whose source code is publicly available for anyone to view, modify, and distribute. GitHub hosts most open source projects.
A mechanism for proposing code changes in a shared repository. Engineers submit PRs to trigger code review before code is merged into the main branch.
Improving the internal structure of existing code without changing its external behavior — making it cleaner, more readable, or more efficient.
Representational State Transfer — an architectural style for APIs using HTTP methods (GET, POST, PUT, DELETE) to interact with resources identified by URLs.
The ability of a system to handle increased load (more users, more data, more requests) without performance degradation. Vertical (bigger servers) vs. horizontal (more servers).
A time-boxed development cycle (usually 2 weeks) in which a team delivers a defined set of work. Core to Scrum and many agile frameworks.
The accumulated cost of shortcuts, poor design decisions, or deferred maintenance in a codebase. Like financial debt, it accrues "interest" in the form of slower future development.
Unit tests verify individual functions; integration tests verify multiple components working together; end-to-end tests verify the full user workflow. All are critical for reliability.
A controlled experiment comparing two variants (A and B) shown to different user groups to determine which performs better on a target metric.
Combining or summarizing data (e.g., SUM, COUNT, AVG) to create higher-level insights from raw records. Common operations in SQL and data analysis.
A role bridging data engineering and analytics — uses SQL and tools like dbt to transform raw data into clean datasets for analysts and business users.
A systematic error in a model or analysis that leads to incorrect conclusions. Different from fairness bias — in ML it refers to underfitting or skewed training data.
The process of collecting, analyzing, and acting on data to make business decisions. Tools like Tableau, Looker, and Power BI are BI platforms.
The percentage of customers who stop using a service over a given period. A key metric for subscription businesses.
Analyzing groups of users who share a common characteristic (e.g., sign-up month) over time to understand behavior patterns and retention.
Correlation means two variables move together. Causation means one directly causes the other. A fundamental distinction in data analysis — correlation does not imply causation.
A visual display of key metrics and data, built in tools like Tableau, Looker, or Power BI, enabling stakeholders to monitor business performance at a glance.
Policies, procedures, and controls for managing data quality, security, and compliance across an organization. Ensures data is trustworthy and used responsibly.
The degree to which data is accurate, complete, and reliable. Poor quality data leads to flawed analysis and bad decisions.
An open-source tool that enables analytics engineers to transform raw warehouse data using SQL, with version control, testing, and documentation built in.
An initial step in data analysis where analysts explore datasets to summarize characteristics, find patterns, and detect anomalies before modeling.
Tracking how users progress through a series of steps (e.g., view → click → purchase) to identify where they drop off.
A statistical method for testing assumptions about data. Formulate a hypothesis, collect data, and use statistical tests to determine if the data supports the hypothesis.
A measurable value that indicates how well an organization is achieving a key business objective.
A quantifiable measure of performance or outcome (e.g., revenue, user growth, engagement). Business metrics drive decision-making.
Scaling numerical features to a common range (e.g., 0–1) so that no single feature dominates a model due to its scale.
A data point that differs significantly from other observations. Outliers can indicate errors, fraud, or genuine anomalies worth investigating.
When a model learns the training data too precisely — including noise — and fails to generalize to new, unseen data. Leads to poor real-world performance.
A statistical measure indicating the probability of observing results at least as extreme as the data, assuming the null hypothesis is true. Typically, p < 0.05 is considered statistically significant.
The percentage of users or customers who remain engaged or active over a given period. A key health metric for product-driven companies.
A statistical technique for predicting a continuous outcome variable based on one or more predictor variables. Linear regression is the most fundamental form.
Structured Query Language — the standard language for querying and manipulating relational databases. Foundational for data roles of all types.
A measure of how spread out data is. High variance means data points are scattered; low variance means they cluster tightly. Also refers to variance in statistical models.
An AI system that can autonomously take actions in an environment to achieve goals, often using tools, memory, and reasoning to plan multi-step tasks.
A technique that allows neural networks to focus on the most relevant parts of input data. The foundation of transformer architectures and modern LLMs.
A machine learning task where the model predicts which category or class a piece of data belongs to (e.g., spam vs. not spam). Different from regression which predicts continuous values.
An unsupervised machine learning technique that groups similar data points together without predefined labels. Used for customer segmentation and data exploration.
A numerical vector representation of data (text, images, audio) that captures semantic meaning — similar items have similar embeddings. Core to search and RAG systems.
Continuing the training of a pre-trained model on a domain-specific dataset to improve performance on a target task without training from scratch.
AI systems capable of generating new content — text, images, audio, video, or code — based on training data and prompts. LLMs like GPT are a core example.
Graphics Processing Unit — specialized hardware originally for graphics, now the primary accelerator for deep learning training and inference due to its parallel compute architecture.
When a large language model generates plausible-sounding but factually incorrect or fabricated information. A key challenge in deploying LLMs reliably.
Using a trained model to generate predictions on new data. Contrasted with training (learning from data). Low-latency inference is critical for production AI systems.
A deep learning model trained on massive text datasets to understand and generate human language. Examples: GPT-4, Claude, Gemini, Llama.
A mathematical function that measures how far a model's predictions are from the actual values. The model learns by minimizing loss during training.
The degradation of a model's performance over time as real-world data distribution changes from the data it was trained on.
A computational model loosely inspired by the human brain, made of layers of interconnected nodes (neurons) that learn patterns from data.
A model that has already been trained on large datasets by another team or organization. Fine-tuning a pre-trained model is faster and cheaper than training from scratch.
The craft of designing effective inputs to large language models to produce desired outputs — including instructions, examples, context, and formatting guidance.
A technique that enhances LLM responses by first retrieving relevant documents from a knowledge base and providing them as context, reducing hallucinations.
A machine learning paradigm where an agent learns by taking actions and receiving rewards or penalties. Used for game AI, robotics, and optimization problems.
A training technique where human raters score model outputs, and those scores are used to fine-tune the model to align with human preferences — used to train ChatGPT and Claude.
Machine learning with labeled training data — the model learns to map inputs to known outputs. Used for classification and regression tasks.
The process of breaking text into smaller units (tokens) that an LLM can process. Different tokenizers split text differently, affecting model behavior.
The neural network architecture underlying virtually all modern LLMs, introduced in the 2017 paper "Attention Is All You Need." Uses self-attention to process sequences.
Machine learning without labeled data — the model finds patterns in data on its own. Clustering and dimensionality reduction are common unsupervised techniques.
A database optimized for storing and querying high-dimensional embeddings via similarity search. Examples: Pinecone, Weaviate, Chroma, Qdrant.
Apache Airflow — an open-source workflow orchestration platform for scheduling, monitoring, and managing data pipelines expressed as DAGs (Directed Acyclic Graphs).
Processing large volumes of data at scheduled intervals (e.g., nightly jobs) as opposed to real-time streaming. Common for ETL and reporting workloads.
The process of combining data from multiple sources into a unified, usable format. Tools like Fivetran, Stitch, and custom pipelines handle data integration.
An automated sequence of processes that extract, transform, and load data from sources to destinations. Critical infrastructure for moving and preparing data at scale.
A data structure representing a workflow as a graph with no cycles — each node is a task and edges represent dependencies. Used heavily in Airflow and similar orchestrators.
A centralized repository storing raw data in its native format at scale. Flexible and cost-effective, but requires careful governance to avoid becoming a "data swamp."
An architecture combining the storage flexibility of data lakes with the reliability and structure of data warehouses. Platforms: Databricks, Apache Iceberg, Delta Lake.
Tracking the origin, movement, and transformation of data throughout its lifecycle — critical for debugging, compliance, and trust in data.
A decentralized data architecture where domain teams own and operate their own data products, as opposed to a centralized data platform team.
A structured repository for analyzing large volumes of business data. Modern cloud warehouses include Snowflake, BigQuery, and Redshift.
Extract, Load, Transform — data is loaded raw into the warehouse first, then transformed using the warehouse's compute. The modern alternative to ETL for cloud warehouses.
Extract, Transform, Load — data is extracted from sources, transformed/cleaned, then loaded into a target system. The traditional data integration pattern.
The process of selecting and creating relevant features (input variables) from raw data for machine learning models. Often the most time-consuming part of ML workflows.
Infrastructure that stores, manages, and serves ML features consistently for both training and real-time inference, eliminating redundant computation.
Apache Kafka — a distributed event streaming platform used for real-time data pipelines, event sourcing, and high-throughput message queuing between systems.
Tracking model performance in production, detecting data drift, model drift, and other issues that degrade predictions over time.
An open-source platform for managing the ML lifecycle — experiment tracking, model versioning, reproducibility, and model deployment.
A centralized repository for managing different versions of trained models, their metadata, and transition to production. Critical for reproducibility and governance.
The structure of data — defines column names, types, and constraints. Data quality depends on consistent, enforced schemas.
Apache Spark — a distributed computing framework for processing large datasets. Widely used for batch ETL, streaming, and ML workloads at scale.
Processing data as it arrives rather than batching — enables near real-time insights and dashboards. Kafka and Apache Spark Streaming are common platforms.
Cleaning, filtering, enriching, and reshaping raw data into formats suitable for analysis or model training. Often the most time-consuming part of data work.
The specific conditions a user story must meet to be considered complete and accepted by the Product Owner. Defines the "definition of done" for a story.
A software development philosophy emphasizing iterative delivery, continuous feedback, and responding to change. Contrasts with waterfall's upfront planning.
An ordered list of work items (user stories, bugs, tasks) for a product or sprint. The Product Owner is responsible for maintaining and prioritizing it.
The process of clarifying, estimating, and prioritizing items in the backlog before they enter a sprint. Ensures the team understands what they're building.
A visual representation of work remaining versus time during a sprint. Helps teams track progress and identify if they're on pace to complete sprint goals.
A shared quality standard a team uses to determine when work is truly complete — typically including coded, reviewed, tested, and deployable.
A large body of work that spans multiple sprints and can be broken down into features and user stories. Epics represent significant product initiatives.
An Agile methodology using a continuous flow model (as opposed to sprints) where work items move through stages from to-do to done. WIP limits prevent bottlenecks.
A prioritization framework: Must Have, Should Have, Could Have, Won't Have. Used to align stakeholders on feature priority within a release.
The smallest, simplest version of a product that delivers value to early adopters and enables learning. Not a low-quality product — it's a targeted learning vehicle.
Objectives and Key Results — a goal-setting framework where qualitative Objectives are paired with measurable Key Results. Widely adopted in tech organizations.
Program Increment Planning — a SAFe event where all teams in an Agile Release Train align on goals, dependencies, and capacity for the next 8–12 week increment.
A strategic plan documenting what features and improvements the product will deliver over time. Communicated to stakeholders to align on direction.
Product Requirements Document — the primary artifact PMs use to define the purpose, users, features, and success metrics of a product or feature.
A prioritization scoring framework: Reach × Impact × Confidence ÷ Effort. Helps PMs make data-informed prioritization decisions.
A Scrum ceremony held at the end of each sprint for the team to inspect their process and commit to improvements. The foundation of continuous improvement.
See Product Roadmap — a timeline of planned features and releases.
An Agile framework using fixed-length sprints (usually 2 weeks) with ceremonies: standup, planning, review, and retrospective. Most widely adopted Agile methodology.
The ceremony at the start of a sprint where the team estimates and commits to work for the upcoming iteration.
A brief daily synchronization meeting (15 minutes) where team members share: what they did yesterday, what they're doing today, and any blockers.
A relative unit of estimate used in Scrum to measure the effort, complexity, and uncertainty of a user story. Not equivalent to hours.
Detailed technical documentation of how a feature will be implemented — often created by tech leads or architects before engineering begins.
A short, simple description of a product feature from the end user's perspective: "As a [role], I want [goal] so that [benefit]."
The number of story points a team completes per sprint. Used as an internal planning tool — not a measure of productivity or quality.
Work in Progress Limit — a Kanban constraint on how many items can be in a given workflow stage simultaneously. Reduces multitasking and improves throughput.
The discipline of securing software during the development lifecycle — through threat modeling, secure code review, SAST/DAST scanning, and developer education. A bridge between software engineering and security.
A publicly available framework cataloguing adversary tactics, techniques, and procedures (TTPs) mapped to real-world threat actors. The shared vocabulary of modern offensive and defensive security teams.
Defenders — the security personnel responsible for monitoring, detecting, and responding to attacks. Contrasted with the red team (attackers) and purple team (collaborative offense/defense).
Infrastructure that adversaries (and red team operators) use to issue commands to compromised systems and receive data. Cobalt Strike, Brute Ratel, and Sliver are common C2 frameworks used by both attackers and red teams.
Tools that continuously scan cloud environments for security misconfigurations — public storage buckets, overly permissive IAM roles, unencrypted data. Wiz, Orca Security, and Prisma Cloud are the leading vendors.
Testing a running application by sending attack inputs and observing responses. Finds vulnerabilities like injection flaws and authentication bypasses that are only visible at runtime. Contrasted with SAST (static analysis).
Security software deployed on endpoints (laptops, servers) that monitors for malicious behavior in real time and enables investigation and response. CrowdStrike Falcon, SentinelOne, and Carbon Black are the leading platforms.
A security track focused on regulatory frameworks (ISO 27001, SOC 2, HIPAA, GDPR), risk management, and audit processes. Distinct from technical security engineering — GRC skills do not transfer to hands-on AppSec, pentesting, or IR roles.
Technical artifacts that indicate a system has been compromised — malicious IP addresses, file hashes, domain names, registry keys. Shared between organizations and used to build detection rules and blocklists.
The structured process of containing, eradicating, and recovering from a confirmed security incident. Follows the lifecycle: Preparation → Identification → Containment → Eradication → Recovery → Lessons Learned.
The gold-standard penetration testing certification. Requires a 24-hour hands-on exploitation exam against a live lab network with no multiple choice. Validates practical offensive skill, unlike multiple-choice-only certifications.
An authorized, scoped offensive engagement to identify exploitable vulnerabilities in systems, networks, or applications. Distinct from red teaming — pentesters maximize vulnerability discovery; red teamers simulate realistic adversary behavior to test detection.
A group that simulates sophisticated adversaries to test an organization's detection and response capabilities. Engagements are stealthy, long (weeks to months), and designed to test the entire attack lifecycle — not just find vulnerabilities.
Analyzing source code for security vulnerabilities without running the application. Tools include Semgrep, Snyk, Checkmarx, and Veracode. Deployed in CI/CD pipelines to catch issues before deployment.
A platform that aggregates logs and security events from across an organization's infrastructure, applies detection rules, and surfaces alerts for SOC analysts. Splunk, Microsoft Sentinel, and Chronicle are the leading platforms.
The team and environment responsible for monitoring, detecting, and triaging security events in real time. SOC analysts work in tiers — Tier 1 (alert triage), Tier 2 (incident investigation), Tier 3 (threat hunting and detection engineering).
Research and analysis of adversary behavior — tactics, infrastructure, malware — translated into actionable outputs for defenders. Produced at tactical (IOCs), operational (campaign analysis), and strategic (executive briefing) levels.
The behavior patterns of threat actors — the "how" of adversary operations. Tactics are high-level goals (Initial Access), Techniques are the method (Phishing), Procedures are specific implementations. Mapped in the MITRE ATT&CK framework.
A structured process for identifying security risks in a system design before code is written. Common frameworks: STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, DoS, Elevation of Privilege) and PASTA.
A security model that assumes no user or system should be trusted by default, regardless of network location. Requires continuous verification of identity and least-privilege access — contrasted with traditional perimeter-based "castle and moat" security.
A deployment strategy running two identical production environments (blue and green). Traffic switches to the new version (green) once verified, enabling instant rollback if issues arise.
Rolling out a new version to a small percentage of users first, monitoring for issues before gradually expanding to all users. Reduces risk of widespread outages.
How often code is deployed to production — a key DORA metric. Higher frequency enables faster feedback and smaller, less risky releases.
The allowable amount of downtime or failed requests based on an SLO. Once exhausted, the team focuses on reliability over new features.
A package manager for Kubernetes that simplifies deployment and versioning of applications. Uses charts to define Kubernetes resources.
See Software Engineering section. Managing infrastructure through code (Terraform, CloudFormation) rather than manual console configuration.
Distributes incoming traffic across multiple servers to prevent any single server from being overwhelmed. Critical for scaling and high availability.
The average time it takes to recover from an incident. A key reliability metric — SREs focus on reducing MTTR through automated recovery and incident response processes.
The ability to understand a system's internal state from its external outputs (logs, metrics, traces). The foundation of reliable production systems.
A contractual commitment to customers about system availability and performance. Breaking an SLA may incur penalties or refunds.
A specific metric measuring service performance (e.g., "99.9% of requests succeed within 200ms"). SLIs are measured to track SLOs.
An internal reliability target (e.g., "99.9% availability"). Defines the acceptable error rate and drives the error budget.
Repetitive, manual operational work that doesn't provide permanent value. SREs focus on automating toil to free time for reliability improvements.
Deploying new code without interrupting service. Achieved through load balancers, gradual rollouts, and stateless design.
A recurring meeting between a manager and direct report focused on career development, blockers, feedback, and wellbeing — not status updates.
A framework defining levels, titles, and expectations for career progression in engineering. IC (Individual Contributor) and Manager tracks are typical.
A leadership skill focused on asking questions and helping others discover their own solutions rather than providing direct answers. Builds autonomy and ownership.
A challenging discussion about performance issues, conflicts, or misalignment. Leaders who handle these well build trust and address problems early.
Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Time to Recovery — measures of software delivery performance and team health.
Information given to an employee about their performance, behavior, or impact. Regular, specific feedback is more helpful than infrequent, vague feedback.
The interview process for evaluating and hiring candidates — typically includes phone screen, technical interviews, and team interviews.
The process of integrating new team members — assigning mentors, setting up environments, clarifying roles, and establishing relationships.
Systems and processes for evaluating employee performance, setting goals, giving feedback, and making compensation decisions.
Advancing an employee to a higher level or role. Should be based on demonstrated capability at the next level, not just tenure.
An environment where team members feel safe to take risks, admit mistakes, and ask questions without fear of embarrassment or punishment. Essential for high-performing teams.
The ability to recover from setbacks and maintain performance under pressure. A key trait in high-stress leadership roles.
Keeping talented team members from leaving. High-performing teams have clear career paths, mentorship, interesting work, and good management.
A meeting between a leader and the direct reports of their direct reports. Builds relationships, surfaces problems, and provides independent feedback on manager effectiveness.
Investing in engineers' growth through challenging assignments, mentorship, and opportunities. Leaders who develop talent create future leaders.
A document recording the decision, context, and tradeoffs for a significant technical choice (e.g., database selection, architectural pattern). Provides context for future engineers.
A lightweight architecture diagramming framework using context, containers, components, and code — simpler than UML but captures essential structure.
A reusable solution to a common design problem (e.g., MVC, singleton, observer). Provides vocabulary for discussing architecture.
An approach to architecture emphasizing deep understanding of business domains and modeling code around those domains. Useful for complex, evolving systems.
A design where services communicate through asynchronous events rather than direct calls. Enables loose coupling and scalability.
Adding more servers to handle load, as opposed to vertical scalability (bigger servers). Preferred for distributed systems.
A single, tightly integrated application serving all functions. Simple to deploy but hard to scale and modify independently. Often evolved into microservices.
Five design principles (Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, Dependency Inversion) for writing maintainable, extensible code.
The process of designing large-scale systems considering scalability, reliability, consistency, and performance tradeoffs.
The Open Group Architecture Framework — a comprehensive enterprise architecture framework used by Enterprise Architects to structure organizational technology strategy.
Increasing the capacity of a single server (more CPU, RAM). Simpler than horizontal scalability but hits limits. Typically used with caching or optimization first.
A full-featured frontend framework built on TypeScript by Google. Opinionated structure makes it popular in enterprise and finance. Not chosen for maximum flexibility.
Microsoft's modern backend framework for C#. Strong in enterprise and government organizations using the Microsoft stack.
A high-level Python web framework following the "batteries included" philosophy. Includes ORM, admin panel, and authentication out of the box.
A containerization platform packaging applications with dependencies into portable containers. Standard practice for deployment and local development.
A minimal JavaScript/Node.js web framework. Paired with TypeScript becomes NestJS for larger applications.
A modern Python framework for building APIs quickly with type hints, automatic documentation, and async support. Popular for ML and data applications.
A lightweight Python web framework giving developers flexibility to choose components. Smaller than Django, more control over architecture.
Google's cloud platform strong for data and ML workloads. BigQuery (data warehouse) and Vertex AI (ML platform) are leading products.
A query language for APIs allowing clients to request exactly the data needed. Contrasts with REST which returns fixed structures.
Open-source container orchestration platform automating deployment, scaling, and management of containers. Industry standard for cloud-native applications.
Microsoft's cloud platform dominant in enterprise and government due to strong Windows and SQL Server integration.
A meta-framework built on React by Vercel. Adds routing, SSR, and server-side rendering enabling full-stack development.
A JavaScript runtime allowing JavaScript to run on servers. Enables full-stack JavaScript development.
An open-source relational database and default choice for modern product companies. More robust than MySQL with advanced features.
Meta's deep learning framework favored by AI researchers and practitioners. Dynamic computation graphs enable intuitive model building.
A JavaScript library for building UIs with components and reactive state management. Created by Meta; dominant in frontend development.
An in-memory data store used for caching, sessions, and real-time applications. Extremely fast but data is lost on restart unless persisted.
A Java framework simplifying enterprise Java development with auto-configuration and embedded servers. Standard in enterprise and fintech.
A compiler-based frontend framework where reactivity happens at compile time. Results in smaller bundle sizes and less runtime overhead.
Google's deep learning framework with strong production and serving infrastructure. Widely used in large organizations and Google ecosystem.
Infrastructure as Code tool for AWS, Azure, and GCP using declarative configuration. Version-controlled, reviewable infrastructure changes.
A superset of JavaScript adding static typing, interfaces, and better tooling. Nearly universal in serious frontend and Node.js codebases.
A community-maintained JavaScript framework with gentler learning curve than React or Angular. Popular in startups and less common in FAANG.
The proprietary Java-like programming language used to write code extensions on the Salesforce platform. Used for triggers, batch jobs, and backend logic.
Vendor-specific credentials that signal competence in a platform ecosystem. Examples: Salesforce Admin Cert, ServiceNow CAD, SAP module certifications. More meaningful than general software certs.
A specialist hired to configure, extend, and implement enterprise platforms at customer organizations. Works on project-based engagements (6–24 months) through consulting partners.
Enterprise software for managing customer interactions, sales pipelines, and service cases. Salesforce is the dominant cloud CRM.
Building platform functionality using visual tools and UI configuration rather than code. Examples: Salesforce Flows, ServiceNow UI Policies. Typically done by Administrators.
Comprehensive enterprise software integrating finance, supply chain, manufacturing, HR, and other business processes. SAP and Oracle are the dominant global vendors.
The ServiceNow API used for server-side and client-side scripting. Includes GlideRecord, GlideSystem, GlideAjax — the foundation of ServiceNow development.
The moment when an enterprise platform implementation launches and users switch from legacy systems to the new platform. High-pressure, critical milestone in platform projects.
Hard resource constraints enforced by Salesforce at runtime — limits on SOQL queries, DML operations, heap memory, and CPU time per transaction. Fundamental to Apex development.
Enterprise software discipline managing IT service delivery. Incident, change, and problem management are the core functions. ServiceNow dominates this category.
The modern web component framework used on Salesforce to build reusable, efficient UI components. Replaces the legacy Aura component framework.
Salesforce-owned integration platform (iPaaS) for connecting enterprise systems via APIs. Market leader for Salesforce integrations.
A single instance of an enterprise platform for one customer. Salesforce orgs are often multi-tenant but logically isolated.
The proprietary query language used to retrieve records from Salesforce databases. Similar to SQL but designed for Salesforce's data model.
The financial and operational burden of migrating from one enterprise platform to another. Massive for platforms like SAP, ERP migrations cost millions and take years.
Platform-native tools for automating business processes without coding. Examples: Salesforce Flows, ServiceNow Flow Designer, Dynamics 365 Business Process Flows.