6 repositorios
Architectural patterns for offloading infrastructure management to cloud providers for event-driven code execution.
Distinct from Serverless Execution Environments: Distinct from Serverless Execution Environments: focuses on the architectural model rather than the runtime environment.
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This project serves as a comprehensive technical reference for the architecture and design of data-intensive applications. It provides a structured analysis of the fundamental principles required to build reliable, scalable, and maintainable software systems, covering the core trade-offs inherent in modern data infrastructure. The repository explores the mechanics of distributed data management, including strategies for replication, partitioning, and achieving consensus across multiple nodes. It details the design of storage engines, indexing techniques, and transaction management models, whi
Discusses the architectural implications of utilizing serverless execution for data processing tasks.
This repository provides a comprehensive library of code examples for implementing event-driven, serverless backend architectures. It serves as a practical guide for building scalable cloud-native applications that execute logic in isolated environments, triggered by infrastructure events or HTTP requests rather than persistent server processes. The collection demonstrates how to leverage managed infrastructure to automate backend workflows, including the use of asynchronous task queuing to maintain system stability during high traffic. It highlights patterns for secure API hosting, enabling
Executes functions in isolated containers triggered by cloud events rather than persistent processes.
Azure Docs is the official technical documentation repository for Microsoft Azure, the cloud computing platform. It provides comprehensive guidance on the full spectrum of Azure services, covering everything from core infrastructure components like virtual machines, Kubernetes clusters, and serverless computing to platform services for AI, machine learning, data analytics, and storage. The documentation details how to provision, manage, and govern cloud resources at scale, including policy enforcement, identity management, and cost optimization. The documentation distinguishes Azure through i
Processes events with serverless code that scales automatically without managing infrastructure.
Wandb is a centralized platform for machine learning experiment tracking, model registry management, and workflow orchestration. It provides a comprehensive suite of tools for logging, visualizing, and versioning training metrics, model artifacts, and hyperparameter sweeps to ensure reproducibility across development cycles. The platform also functions as an observability tool for large language model applications, enabling the tracing of execution steps, token usage, and reasoning processes. The project distinguishes itself through its event-driven automation capabilities, which allow users
Executes fine-tuning and post-training tasks on managed, auto-scaling GPU infrastructure.
This repository provides a collection of starter templates, reference projects, and implementation guides for integrating Firebase services into Android applications. It serves as a boilerplate for building mobile apps with built-in cloud backend integration. The project includes examples for connecting Android applications to large language models for generative AI features. It also provides sample code for managing user identity and authentication, as well as demonstrations for integrating cloud databases and serverless functions. The codebase covers a broad range of capabilities, includin
Demonstrates how to trigger backend logic in response to cloud events or HTTP requests using serverless functions.
workerd is a serverless edge runtime designed for executing lightweight, distributed functions at the network edge. It utilizes a V8-based JavaScript engine to provide fast startup and low memory overhead, while maintaining a WebAssembly-compatible execution environment that allows modules to run alongside JavaScript for high-performance computational tasks. The runtime supports isolate-based multi-tenancy to run multiple independent execution contexts within a single process. It implements an event-driven execution model that triggers code based on network requests or scheduled events and in
Employs an event-driven model that triggers code execution based on network requests or scheduled events.