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Orchestrating large-scale data processing tasks across a cluster of compute nodes.
Distinct from Cloud Batch Processing: Focuses on SLURM-driven batch processing for datasets, not cloud-native training trials.
Explore 7 awesome GitHub repositories matching data & databases · Cluster Batch Processing. Refine with filters or upvote what's useful.
Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis
Processes finite datasets to perform repetitive tasks like report generation.
Pinot is a distributed, columnar analytical database designed for high-concurrency, low-latency query processing. It functions as a real-time OLAP datastore, enabling interactive, user-facing analytics by ingesting and querying massive datasets from both streaming and batch sources. The system architecture relies on a centralized controller for cluster coordination and a distributed segment-based storage model to ensure horizontal scalability. The platform distinguishes itself through a hybrid ingestion pipeline that unifies real-time event streams and historical batch data into a single quer
Launches a standalone job that builds segments from source data and pushes them to the cluster.
This is an educational repository that teaches container orchestration fundamentals through hands-on guides and practical examples. It covers core Kubernetes concepts including Pods, Services, Deployments, and Namespaces, providing step-by-step exercises that demonstrate how to manage containerized applications across a cluster. The tutorials walk through essential Kubernetes capabilities such as decoupling configuration from application code using ConfigMaps and Secrets, exposing Pods with stable network endpoints via Services, and routing external traffic with Ingress controllers. The mater
Covers running batch jobs to completion using Kubernetes Job resources.
This project is a comprehensive guide and collection of best practices for testing Node.js backend applications. It provides a curated set of patterns and reference examples for writing reliable unit, integration, and component tests. The project distinguishes itself through specific strategies for backend integration, including detailed methods for API contract testing against OpenAPI specifications and shared schemas. It offers specialized guidance on managing message queue testing, focusing on idempotency, resilience, and asynchronous event synchronization. The guide covers a broad range
Provides methods to test message queue consumers using batches with mixed failures to ensure system resilience.
This project is a Python software development kit and framework for building applications that integrate with large language models. It serves as a multimodal content generator and vector embedding library, enabling the production and editing of text, images, audio, and video. The toolkit provides specialized capabilities for adapting base models through supervised and reinforcement training. It further distinguishes itself by offering tools for orchestrating complex workflows, including stateful chat sessions, the enforcement of structured output via schemas, and the integration of external
Implements batch processing jobs for handling large volumes of requests with configurable dataset inputs and outputs.
Good Job is a background job processor for Ruby on Rails that utilizes a PostgreSQL database as its primary storage engine. By leveraging relational database transactions, it ensures persistent and reliable task execution, integrating directly with the Active Job framework to handle asynchronous operations and recurring job scheduling within existing application environments. The system distinguishes itself through an in-process execution model that allows background workers to run within the same process as the web server, simplifying deployment by removing the need for separate worker servi
Allows grouping multiple tasks into a single collection to track collective progress and trigger lifecycle callbacks.
Map-anything is a 3D scene reconstruction framework and neural geometry estimator designed to transform two-dimensional images into metric three-dimensional spatial representations using feed-forward neural networks. It provides a specialized toolkit for predicting camera intrinsics and ray directions from single images without requiring external geometric metadata. The project includes a 3D model benchmarking suite that utilizes a unified model wrapper to standardize outputs from diverse reconstruction models. This allows for consistent evaluation and accuracy measurement across various spat
Automates the execution of data processing stages across multiple datasets using a SLURM cluster launcher.