awesome-bigdata README

The project's README from github.com/oxnr/awesome-bigdata. As one markdown file: README.md.

Awesome Big Data

Awesome

A curated list of awesome big data frameworks, resources and other awesomeness. Inspired by awesome-php, awesome-python, awesome-ruby, hadoopecosystemtable & big-data.

Your contributions are always welcome!

RDBMS

Frameworks

Distributed Programming

Distributed Filesystem

Distributed Index

Document Data Model

Key Map Data Model

Note: There is some term confusion in the industry, and two different things are called "Columnar Databases". Some, listed here, are distributed, persistent databases built around the "key-map" data model: all data has a (possibly composite) key, with which a map of key-value pairs is associated. In some systems, multiple such value maps can be associated with a key, and these maps are referred to as "column families" (with value map keys being referred to as "columns").

Another group of technologies that can also be called "columnar databases" is distinguished by how it stores data, on disk or in memory -- rather than storing data the traditional way, where all column values for a given key are stored next to each other, "row by row", these systems store all column values next to each other. So more work is needed to get all columns for a given key, but less work is needed to get all values for a given column.

The former group is referred to as "key map data model" here. The line between these and the Key-value Data Model stores is fairly blurry.

The latter, being more about the storage format than about the data model, is listed under Columnar Databases.

You can read more about this distinction on Prof. Daniel Abadi's blog: Distinguishing two major types of Column Stores.

Key-value Data Model

Graph Data Model

Columnar Databases

Note please read the note on Key-Map Data Model section.

NewSQL Databases

Time-Series Databases

Lakehouse Table Formats

SQL-like processing

Vector Databases

Data Ingestion

Data Quality and Observability

Service Programming

Scheduling

Machine Learning

Benchmarking

Security

System Deployment

Applications

Search engine and framework

MySQL forks and evolutions

PostgreSQL forks and evolutions

Memcached forks and evolutions

Embedded Databases

Business Intelligence

Data Visualization

Internet of things and sensor data

Interesting Readings

Interesting Papers

2015 - 2016

2013 - 2014

2011 - 2012

2001 - 2010

Videos

Books

Streaming

Distributed systems

Graph Based approach

Data Visualization

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awesome-bigdata (oxnr/awesome-bigdata)

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What is this?A curated list of frameworks, databases, tools, papers, and resources related to big data, covering everything from distributed processing engines to key-value stores and visualization tools.
Is this a tool or just a list of links?It is purely a markdown index of links to external projects and papers, organized into categories like RDBMS, distributed programming, distributed filesystems, and data models.
How is the list organized?Entries are grouped into categories such as RDBMS, Frameworks, Distributed Programming, Distributed Filesystem, Key-value and Columnar data models, Data Ingestion, Machine Learning, Security, Business Intelligence, and more, each with a short description per entry.
Where should a beginner start?Since this is a reference list rather than software, a beginner can start from the RDBMS or Frameworks sections to understand foundational tools like MySQL, PostgreSQL, and Apache Hadoop before exploring more specialized categories.
Are entries vetted or still updated?The list was created in 2014 and includes mostly older frameworks and papers (dating back to 2001-2016), so newer big data tools may not be represented and some listed projects may be outdated or deprecated.
Is this only about processing frameworks, or does it include databases too?It covers both: distributed processing engines (Hadoop, Spark, Flink, Storm) and a huge range of databases across document, key-value, columnar, graph, time-series, and NewSQL data models.
Does it include learning resources beyond tool links?Yes, it has dedicated sections for Interesting Readings, Interesting Papers (organized by year), Videos, and Books in addition to the tool listings.
Why was this list created?It was inspired by other awesome-style curated lists (awesome-php, awesome-python, awesome-ruby) and the Hadoop ecosystem table, aiming to be a comprehensive reference for the big data landscape.
Creator
oxnr
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Updated
CommitJul 31
Age
12y
Commits · 90 Days