This repository is a curated list of "awesome" resources for big data technologies, covering frameworks, databases, processing engines, and related tools. It serves as a comprehensive reference for engineers, researchers, and practitioners looking to explore or implement solutions within the big data ecosystem.

How It Works

The list is organized into logical categories, such as RDBMS, Frameworks, Distributed Filesystems, Data Models (Key-Map, Key-Value, Graph, Columnar), NewSQL, Time-Series Databases, SQL-like processing, Data Ingestion, Service Programming, Scheduling, Machine Learning, Benchmarking, Security, System Deployment, and Applications. Each entry provides a brief description of the technology.

Quick Start & Requirements

This is a curated list, not a software project. No installation or execution is required.

Highlighted Details

• Extensive categorization of technologies within the big data landscape.
• Includes links to papers, books, videos, and other "awesome" lists for deeper learning.
• Covers a wide range of data models and processing paradigms, from traditional RDBMS to modern stream processing and graph databases.
• Features sections on machine learning, benchmarking, security, and system deployment relevant to big data.

Maintenance & Community

The list is community-driven, with contributions welcomed. Specific maintainers or community links are not detailed in the README.

Licensing & Compatibility

The repository itself is likely under a permissive license (e.g., MIT, CC0) as is common for "awesome" lists, but this is not explicitly stated. The listed technologies have their own diverse licenses.

Limitations & Caveats

As a curated list, its comprehensiveness and up-to-dateness depend on community contributions. Some entries might be outdated or superseded by newer technologies.