Awesome Big Data

A community-maintained, curated list of big data frameworks, tools, and educational resources, published as an open GitHub repository.

• Website/Repo: https://github.com/onurakpolat/awesome-bigdata
• Category: Themed directory (big data)
• Tags: big-data, data-science, distributed
• Open Source: Yes (see LICENSE in repository)

Overview

Awesome Big Data aggregates links to notable big data technologies and learning materials across the ecosystem. It is inspired by other "awesome" lists (awesome-php, awesome-python, awesome-ruby) and focuses on tools and frameworks used for large-scale data storage, processing, and analysis.

Features

• Curated list format

  • Manually curated collection of big data resources.
  • Community contributions accepted via GitHub.

• Broad coverage of big data stack (sections in the list include, but are not limited to):

  • RDBMS – traditional relational database systems relevant in big data contexts.
  • Frameworks – general-purpose big data and analytics frameworks.
  • Distributed Programming – libraries and platforms for distributed/parallel data processing.
  • Distributed Filesystem – systems for distributed storage of large data sets.
  • Distributed Index – technologies for indexing and searching large-scale data.
  • Document Data Model – document-oriented databases.
  • Key Map Data Model – distributed, persistent databases centered around key→map-of-values data models (often called column family stores).
  • Key-value Data Model – systems exposing simpler key→value interfaces.
  • Graph Data Model – graph databases and graph processing tools.
  • Columnar Databases – systems that store data column-by-column rather than row-by-row.

• Conceptual clarifications and notes

  • Distinguishes between:
    • Column-family-style stores (treated as Key Map Data Model), where each key maps to one or more maps of key–value pairs (often called column families and columns).
    • Storage-format-based Columnar Databases, where data is stored column-wise on disk or in memory to optimize column scans.
  • Provides references to external explanations, such as Prof. Daniel Abadi’s blog post on the two major types of column stores.

• Educational resources

  • Includes learning materials and references for understanding big data concepts and tools (framework documentation, blog posts, ecosystem tables like hadoopecosystemtable, etc.).

• Ecosystem inspiration and cross-linking

  • Inspired by multiple established "awesome" lists (awesome-php, awesome-python, awesome-ruby) and other big data overviews (e.g., hadoopecosystemtable, usefulstuff.io/big-data).

• License information

  • Repository includes a LICENSE file specifying usage and redistribution terms.

Pricing

• Not applicable. This is a free, open GitHub list of links and resources.

Ideal Use Cases

• Discovering big data tools and frameworks by category.
• Getting an overview of different data models (relational, key-value, document, graph, columnar, etc.).
• Using as a starting point for exploring the big data ecosystem in research, learning, or project planning.
• Contributing additional big data resources to a public, community-driven directory.