Engineering Glossary

A variation of Git pull that fetches remote changes and rebases local commits on top of them instead of merging.

A safer alternative to force push, which only updates the branch if it hasn't been modified since your last fetch.

A command that compares the changes between two commit ranges, useful for reviewing rebased branches.

A low-level command that reads tree objects into the index, used in complex merging scenarios.

A log of all reference updates in a repository, useful for recovering lost commits or branches.

Same as Git Reflog, logs that record when the tips of branches and other references were updated in the repository.

The system Git uses to store references (like branches and tags) as files in the .git directory.

A string that specifies the mapping between remote and local references when fetching or pushing.

Adds a new remote repository to your local Git configuration, associating a name with a URL.

A command that removes remote-tracking branches that no longer exist on the remote repository.

Removes a remote repository from your local Git configuration, deleting its associated references.

A feature allowing one Git object to be transparently used in place of another, useful for history rewriting.

A feature that remembers how you resolved merge conflicts and automatically reapplies the resolution.

A command used to undo changes by moving the current branch head to a specific commit.

A Git reset option that updates the working directory and staging area to match the specified commit.

A Git reset option that moves the HEAD to a specified commit without changing the working directory or staging area.

A command that lists commit objects in reverse chronological order, often used as a basis for other commands.

A command used to parse Git revision syntax and return the corresponding object name.

A command that creates a new commit that undoes the changes made by a previous commit.

A comparison of two Git commands used to undo changes, with revert creating new commits and reset modifying history.

A command to remove files from both the working directory and the Git repository.

A clone that only fetches a limited number of commits, reducing download size and time for large repositories.

A command that summarizes git log output, typically used to create release announcements.

Commits that are cryptographically signed to verify the identity of the committer and ensure integrity.

Tags that are cryptographically signed to verify the identity of the tagger and ensure the tag's integrity.

The process of combining multiple commits into a single commit, often used to simplify history before merging.

A command that applies a stored stash to the working directory without removing it from the stash list.

A command that applies a stored stash to the working directory and removes it from the stash list.

A command that displays the state of the working directory and the staging area.

A repository embedded within another repository, allowing you to keep a Git repository as a subdirectory of another Git repository.

A command that executes a specified shell command for each initialized submodule in a Git repository.

A comparison of two Git features used to include external repositories in a project, each with different use cases.

A feature for managing nested repositories as subdirectories of a parent repository, an alternative to submodules.

A low-level Git command that manipulates the staging area, updating file information and preparing changes for commit.

The communication protocol used between Git clients and servers for efficient data transfer.

A feature allowing multiple working trees to be attached to the same repository, useful for working on different branches simultaneously.

Creates a new working tree associated with the repository, allowing work on multiple branches simultaneously.

A command to list details of each working tree associated with the repository, showing paths and branch information.

A low-level Git command that creates a tree object from the current index, representing a directory structure.

A Git command used to create an archive (zip or tar) of files from a specified commit, useful for creating release packages.

Agreed-upon patterns for naming branches to improve organization and clarity in collaborative projects.

A command to examine the contents and type of Git objects, useful for inspecting repository internals.

A command to resume the cherry-pick process after resolving conflicts in a paused cherry-pick operation.

Levels at which Git configuration can be set: system (all users), global (user), and local (repository).

A command providing detailed statistics about the number and size of objects in a Git repository.

Mechanisms to securely store and retrieve credentials for Git operations, reducing manual authentication.

Custom scripts that determine how Git should generate diffs for specific file types.

A versatile tool for rewriting Git repository history, offering more power and flexibility than git filter-branch.

A more thorough garbage collection process in Git, optimizing repository storage and performance.

A low-level Git command that computes the object ID (SHA-1 hash) for a given file or string, used in Git's internal storage.

Scripts triggered by specific events in Git workflows, used to automate tasks or enforce policies.

The intermediate area in Git where changes are prepared before committing, also known as the staging area.

Individual, uncompressed Git objects stored separately in the object database before being packed.

A command to list the contents of a tree object in Git, showing files and subdirectories.

Custom scripts defining how Git should handle merges for specific file types or patterns.

A low-level Git command that creates a tree object from a specific index or tree listing.

The four fundamental object types used by Git to store repository data and history.

A command to pack references into a single file for improved performance in repositories with many references.

Compressed files containing multiple Git objects, optimizing storage and transfer efficiency in repositories.

Server-side scripts that run after commits have been accepted, often used for notifications or deployments.

Server-side scripts that run before pushed commits are accepted, used to enforce policies or checks.

An improved version of the Git transfer protocol, offering better performance and feature negotiation.

A command to move a series of commits to a new base commit, useful for changing the parent of a branch.

A command to remove old entries from the reflog, useful for cleaning up Git history and freeing space.

A command to combine all loose objects in a repository into packfiles, improving storage efficiency.

Commands to undo changes in Git, with varying impacts on the working directory and staging area.

Techniques and tools for securely storing and accessing sensitive information in Git repositories.

A command that lists references in a Git repository along with their associated commit hashes.

A feature allowing users to check out only a subset of files from a repository, saving space and time.

A command to validate the contents of Git pack files, ensuring data integrity in the object database.

An application that can be installed directly on organizations and user accounts to extend GitHub's functionality.

A version of Markdown with additional features specifically supported on GitHub.

A tool for importing repositories to GitHub from other version control systems or source code hosts.

A feature in GitHub for tracking tasks, enhancements, and bugs for a project.

A platform for posting and finding software development jobs, integrated with GitHub profiles, that was discontinued in 2021.

A platform for developers to sell and buy integrations for GitHub, enhancing workflow and productivity.

A static site hosting service that takes files directly from a GitHub repository to publish websites.

A collaborative documentation feature in GitHub repositories, allowing easy creation and editing of project documentation.

A lightweight, branch-based workflow that supports teams and projects where deployments are made regularly.

A version control workflow that combines feature-driven development and feature branches with issue tracking.

Branching model for Git that defines a strict branching model designed around project releases.

A graphical interface for viewing Git repositories, providing a visual representation of commit history.

Query language for APIs and a runtime for executing those queries with existing data.

A pointer in Git that refers to the latest commit in the current checked-out branch.

A simple program often used as an introduction to a new programming language or environment, including Git tutorials.

A method for quickly patching production releases by branching directly from the main branch or a tag.

Also known as the staging area, where changes are prepared before committing in Git.

Predefined structures for creating new issues in Git repositories, ensuring consistent and complete information.

A static site generator often used with GitHub Pages to create websites from Markdown files in a Git repository.

A tool in GitHub Pages that allows users to easily select and apply pre-designed themes to their Jekyll sites.

Git Large File Storage, an extension for versioning large files in Git repositories.

A library used by GitHub to detect the programming languages used in repositories.

A lightweight markup language commonly used for documentation in Git repositories.

Formerly the default name for the primary branch in Git repositories, now often replaced by 'main'.

The process of combining changes from different branches into a single branch, integrating multiple lines of development.

An application that uses the OAuth protocol to access Git platform APIs on behalf of users.

A secure string used for authentication in Git operations, allowing access to resources without sharing passwords.

The default name for the remote repository from which a local repository was cloned.

A file describing changes between versions, used to share and apply modifications across different Git repositories.

A Git command that fetches changes from a remote repository and merges them into the current branch.

Process of teaching machine learning algorithms to make predictions using data in cloud environments.

Managing different iterations of machine learning models in cloud-based development pipelines.

Repository for storing and managing reusable software components in cloud development environments.

Experimental technique using molecules to store digital data, potentially integrated with cloud systems.

Tools and practices for managing multiple cloud accounts within an organization.

Using services from multiple cloud providers in a single architecture, optimizing for cost, performance, and redundancy.

Centralized platforms for managing data and application integrations across multiple cloud providers.

Distributing workloads across multiple cloud providers to optimize performance and reliability.

Tools and practices for administering resources and applications across multiple cloud providers.

Technologies enabling seamless networking across multiple cloud provider environments.

Coordinating microservices communication and security across multiple cloud environments.

Security system requiring two or more verification methods to access cloud resources.

Database systems supporting multiple data models (e.g., document, graph, relational) in a single backend.

Copying data across multiple geographic regions in cloud environments for redundancy and disaster recovery.

Bringing cloud capabilities closer to the network edge for improved performance and reduced latency.

Tools and strategies for monitoring and optimizing expenses across multiple cloud providers.

Strategies to minimize expenses and maximize efficiency when using multiple cloud providers.

Implementing backup and failover systems across multiple cloud providers for enhanced resilience.

Connecting and managing network resources across multiple cloud provider environments.

Strategies and tools for protecting data and applications across multiple cloud provider environments.

Tools for managing and orchestrating data across multiple cloud provider environments.

Unified network architecture spanning multiple cloud providers for seamless connectivity.

Cloud services supporting AI models that can process and integrate multiple types of data (e.g., text, image, audio).

User interfaces that combine multiple input/output methods (e.g., voice, touch, gesture) for cloud service interaction.

Cloud services for integrating and analyzing data from various sources and formats.

Service that enables private subnet instances to connect to the internet or other AWS services.

Isolated environment within a cluster to organize and manage resources in cloud platforms.

Systems allowing users to interact with cloud services using conversational language.

AI technology for understanding, interpreting, and generating human language in cloud environments.

Cloud-based platforms offering pre-built NLP models and APIs for text analysis and generation.

Smaller data centers located closer to end-users for reduced latency in cloud service delivery.

UI design approach mimicking physical objects for intuitive cloud service interaction.

Rules controlling inbound and outbound traffic for subnets in cloud virtual networks.

Stateless firewall for controlling traffic in and out of cloud subnets, enhancing network security.

Process of modifying network address information to route traffic between private and public networks.

Virtualizing network services traditionally run on proprietary hardware in cloud environments.

Tools for monitoring, analyzing, and troubleshooting cloud network performance and behavior.

Tools and practices for measuring and optimizing network performance in cloud environments.

Technology for managing and securing service-to-service communication in cloud-native environments.

Collecting and analyzing network data for visibility and troubleshooting in cloud environments.

Techniques for improving the efficiency and performance of data flow in cloud networks.

Defining and managing network infrastructure using code in cloud environments, enabling automated and version-controlled networking.

Cloud-based automated design of optimal neural network architectures for specific AI tasks.

Cloud platforms combining neural networks with symbolic reasoning for more robust AI systems.

Cloud-based AI integrating neural networks and symbolic AI for enhanced reasoning capabilities.

Cloud services using brain-inspired computing architectures for improved efficiency in AI tasks.

Cloud platforms providing access to brain-inspired computing architectures for AI applications.

Cloud-based offerings of brain-inspired computing architectures for specialized AI workloads.

Cloud-based AI processing using brain-inspired chip designs for improved efficiency and performance.

Cloud databases combining the scalability of NoSQL with the ACID guarantees of traditional relational databases.

Non-relational databases designed for flexible, scalable data storage and retrieval in cloud environments.

High-performance storage protocol for accessing SSDs over network fabrics in cloud environments.

Technology for quickly analyzing multidimensional data from multiple perspectives in cloud databases.

Cloud storage for unstructured data, accessed via APIs and organized as objects rather than files.

Scalable cloud storage services for unstructured data, accessible via APIs, ideal for large-scale data storage and retrieval.

Devices or software providing local access to cloud-based object storage systems.

Centralized repositories for storing and analyzing large volumes of observability data in cloud environments.

Systems for collecting, processing, and routing observability data in cloud environments.

Software development approach prioritizing built-in monitoring and debugging in cloud applications.

Analyzing and interpreting scent-related data using cloud computing resources, enabling digital olfactory applications.

Running ML models on edge devices, reducing reliance on cloud processing for certain tasks.

Technology for rapid analysis of multidimensional data in cloud-based data warehouses.

Open-source framework for building serverless functions with Docker and Kubernetes in cloud environments.

Kubernetes extension for automating application management tasks in cloud-native environments.

Using light-based processing for high-speed, energy-efficient computation in cloud data centers.

Remote software updates for devices connected to cloud services, often used for IoT.

Adhering to Payment Card Industry Data Security Standard requirements in cloud environments.

Dividing large datasets or databases into smaller, more manageable parts in cloud storage systems.

Pricing structure where users only pay for the cloud resources they actually use.

Billing model where customers are charged based on their actual usage of cloud resources.

Pricing scheme where users are billed only when their serverless functions are invoked.

Direct network link between two virtual private clouds, often in different accounts or regions.

Simulated cyberattacks to evaluate the security of cloud-based systems and applications.

Non-volatile memory that combines speed of DRAM with data persistence, used in cloud storage systems.

Storage resources in cloud environments that exist independently of any specific pod or container.

Light-based chips for high-speed data processing in cloud infrastructure, improving energy efficiency.

Defining and managing continuous integration/deployment workflows using code in cloud environments.

Ability to restore cloud-based data to a specific moment in the past, enabling granular data recovery options.

Implementing and maintaining security and compliance rules across cloud resources.

Creating, updating, and enforcing rules governing cloud resource usage and security.

Serverless platform supporting multiple programming languages for cloud function development.

Using multiple data storage technologies to handle varied data storage needs in cloud applications.

Creating serverless applications using multiple programming languages in cloud environments.

Refining and improving cloud-based systems after initial migration from on-premises.

Deploying encryption methods resistant to quantum computer attacks in cloud systems.

Metric for measuring energy efficiency of cloud data centers, comparing total power consumption to IT equipment power usage.

Using AI to anticipate resource needs and adjust cloud capacity automatically, optimizing performance and cost-efficiency.

Using data analytics and machine learning to forecast future cloud resource requirements and optimize infrastructure.

Using data analytics to anticipate and prevent equipment failures in cloud infrastructure.

Advanced analytics providing recommendations for actions in cloud-based decision making.

Tool for estimating costs of cloud services based on expected usage, helping in budgeting and resource planning.

Techniques for processing sensitive data while preserving privacy in cloud environments.

Tools and methods for protecting personal data privacy in cloud computing, ensuring compliance and user trust.

Techniques for analyzing data in the cloud while maintaining individual privacy.

Extracting useful information from large datasets without compromising individual privacy in the cloud.

Performing data analysis in the cloud while protecting sensitive information, balancing insights with data privacy.

Dedicated 5G networks deployed for specific enterprise use, often integrated with cloud services.

Service enabling private connectivity between VPCs, AWS services, and on-premises applications.

Tools for securing, controlling, and monitoring access to critical cloud resources and data.

Memory-efficient data structures for approximate data analysis in cloud environments.

Design pattern using DaemonSets to run system daemons or agents on every node in a cluster.

Processes and tools for preserving and restoring data in containerized environments, ensuring data durability.

Techniques ensuring data integrity across distributed container-based applications, maintaining reliability.

Distributed data management architecture providing consistent data access across containerized applications.

Unified data architecture combining data lake and data warehouse features in containerized environments.

Process of moving data between containers or from traditional systems to containerized applications.

Techniques for maintaining data across container lifecycles, including volume mounts and persistent volumes.

Network layer responsible for forwarding container traffic based on control plane decisions.

Techniques for creating and maintaining copies of data across multiple containerized instances.

Partitioning data across multiple database containers for improved scalability and performance.

Process of running database systems within containers for improved portability and resource utilization.

Microservices design pattern where each service has its own dedicated database, ensuring data isolation.

Platform for observability and monitoring of containerized environments and microservices.

Design pattern for handling failed message processing in containerized message-driven systems.

Approach to deploying containers by specifying desired state rather than imperative commands.

Techniques for rolling out container updates, including rolling updates, blue-green, and canary deployments.

Kubernetes component that evicts pods from nodes based on specific policies to optimize cluster resource usage.

Standardized format defining the structure and metadata of Docker container images, ensuring portability.

Kubernetes feature allowing nodes to advertise system hardware resources to the cluster, enabling specialized hardware use.

Network optimization technique in container load balancing for improved performance by bypassing the load balancer for responses.

Implementation of tracing in containerized microservices for end-to-end request visibility.

Implementation of distributed tracing using Jaeger to monitor and optimize performance in microservices architectures.

Implementation of OpenTelemetry for standardized observability in containerized environments.

Minimal container images containing only the application and its runtime dependencies, reducing attack surface.

Command to attach local standard input, output, and error streams to a running container for interaction and debugging.

Set of files located in the specified PATH or URL, used during the docker build process to create images.

Docker CLI plugin for extended build capabilities, including multi-platform builds and enhanced build options.

Component enabling Docker to be used as a container runtime in Kubernetes via the Container Runtime Interface.

Command-line tool for interacting with Docker daemon and managing containers, images, and other Docker objects.

Command creating a new image from a container's changes, useful for saving modifications made to a running container.

Tool for defining and running multi-container Docker applications, simplifying the process of managing complex application stacks.

Process of viewing detailed information about a container's configuration and state, aiding in troubleshooting.

Feature providing digital signing and verification of Docker images, ensuring image authenticity and integrity.

Process of validating the authenticity and integrity of signed Docker images before use.

Feature allowing management of multiple Docker endpoints from a single Docker client, simplifying multi-environment management.

Background service managing Docker objects like images, containers, networks, and volumes.

Command allowing users to run a new process inside a running container, useful for debugging and maintenance tasks.

Commands for exporting a container's filesystem as a tarball and importing it as an image.

Cloud-based repository service for finding and sharing container images, central to the Docker ecosystem.

Command showing the history of an image's layers, providing insight into how the image was built and modified.

Process of examining Docker image metadata, including layers, environment variables, and exposed ports.

Standardized format defining the structure and metadata of Docker container images, ensuring portability.

Virtualized network providing connectivity between Docker containers, isolating container traffic from the host network.

Different networking modes in Docker, including bridge, host, overlay, and macvlan, offering varied connectivity options.

Command for removing unused Docker objects like containers, networks, images, and volumes.

Commands for exporting Docker images to tar archives and importing them back, useful for image transfer and backup.

Command-line tool for scanning Docker images for vulnerabilities, providing detailed reports on potential security issues.

Deprecated component that provided CRI compatibility for Docker in Kubernetes, bridging Docker and CRI.

Unix socket or named pipe used for communication between Docker client and daemon, enabling Docker API access.

Command displaying a live stream of container resource usage statistics, including CPU, memory, and network I/O.

Native clustering and orchestration solution for Docker, allowing you to create and manage a swarm of Docker nodes as a single virtual system.

Real-time stream of Docker daemon events, providing insights into container, image, volume, and network activities.

Command displaying the running processes of a container, similar to the Unix top command but container-specific.

Persistent data storage mechanism for containers, independent of container lifecycle, enabling data persistence.

Different storage backends for Docker volumes, including local, NFS, and cloud-specific drivers.

Text document containing all the commands a user could call on the command line to assemble a Docker image.

Instruction defining build-time variables that users can pass at build-time to the builder.

Guidelines for creating efficient, secure, and maintainable Dockerfiles, optimizing image builds and runtime performance.

Instruction telling Docker how to test a container to check its health status, improving reliability.

Set of commands used in Dockerfiles to build images, including ADD, COPY, RUN, CMD, and ENTRYPOINT.

Set of commands used in Dockerfiles to build images, including ADD, COPY, RUN, CMD, and ENTRYPOINT.

Instruction setting the system call signal that will be sent to the container to exit, customizing shutdown behavior.

File specifying which files and directories should be excluded when building a Docker image.

Approach to software design focusing on the core domain in containerized microservices architectures.

P2P-based image and file distribution system to improve efficiency of image downloads in container environments.

Process of cordoning a node and evicting its pods in preparation for maintenance or decommissioning.

Networking configuration supporting both IPv4 and IPv6 in container environments, enabling broader connectivity options.

Kubernetes feature allowing custom logic to be applied to object creation or modification requests.

Kubernetes feature enabling runtime configuration of audit policies without API server restart.

Automatic creation of storage when persistent volume claims are made in Kubernetes, simplifying storage management.

Monitoring solution for containerized environments, providing deep visibility into application performance.

Combination of Elasticsearch, Logstash, and Kibana for logging and monitoring containerized environments.

Network communication between containers or services within the same cluster or data center.

Deployment of artificial intelligence and machine learning models on edge devices using containers.

Processing and analyzing data at the network edge using containerized analytics applications.

Tools and practices for managing containerized workloads on edge devices, ensuring consistent deployment and updates.

Management and coordination of containerized applications across distributed edge locations.

Security measures and practices specific to containerized edge computing environments, protecting distributed workloads.

Techniques for efficiently distributing containerized workloads across edge devices, optimizing resource usage.

Synchronization of data and state between edge containers and cloud-based systems, ensuring consistency.

Component controlling outbound traffic from a container cluster to external services, enhancing security and traffic management.

Service mesh components managing outbound traffic from the mesh to external services, providing fine-grained control.

Management and policies for outbound network traffic from containers, ensuring security and compliance.

Application Performance Monitoring tool for containerized applications in the Elastic Stack.

Sensitive data stored in an encrypted form in container orchestration platforms, enhancing security.

Comprehensive testing of containerized applications from start to finish, validating entire system functionality.

Kubernetes API resource providing a more scalable alternative to Endpoints for service discovery.

Key-value pairs passed to containers at runtime, used for configuration and runtime behavior control.

High-performance proxy often used in service mesh implementations for containerized environments.

Temporary containers used for troubleshooting or debugging in Kubernetes pods, without affecting the main containers.

Short-lived storage volumes in Kubernetes that share the lifecycle of a pod, useful for temporary data storage.

Routing strategy for load balancing network traffic across multiple paths in container networks.

Distributed key-value store that provides a reliable way to store data across a cluster of machines.

Destinations for cluster-level events in container orchestration platforms, used for monitoring and alerting.

Components or services generating events in containerized environments, often used in event-driven architectures.

Pattern of storing changes to application state as a sequence of events, often used in microservices architectures.

Design pattern capturing all changes to application state as a sequence of events, useful in distributed systems.

Implementation of event sourcing pattern in containerized microservices architectures, ensuring data consistency.

Design approach where containerized services produce, detect, and react to events, enabling loose coupling and scalability.

Rules determining which pods to terminate when a node is under resource pressure in Kubernetes clusters.