Guide · Open source
How to use awesome-bigdata: browse big data tools and reading
Updated · written with AI from the project's own documentation and checked against our catalogue data
awesome-bigdata is a curated, hand-picked list of links to big data frameworks, databases, tools, papers, books and videos. It is a reference you read, not software you install. This guide shows you how the list is organised and how to use it to find tools in a category or starting points for further reading.
awesome-bigdataoxnr/awesome-bigdata · ★ 14.7k · MIT Install options & downloads Quick start
- 1Open the repository. Go to https://github.com/oxnr/awesome-bigdata in your browser. The full list is in the README.
- 2Pick a topic. Use the table of contents to choose a section that matches your question, such as Distributed Filesystem or Columnar Databases.
- 3Scan the descriptions. Read the one-line description next to each link to shortlist tools that fit your needs.
- 4Open the linked projects. Follow the links to each project's own site or repository to check its documentation, licence and activity.
- 5Note your shortlist. Write down the candidates you want to evaluate, since the list itself does not rank or compare them.
- 6Explore further reading (optional). Check the Papers, Books and Videos sections for background material on distributed systems.
- 7Clone locally (optional). Run git clone https://github.com/oxnr/awesome-bigdata if you want an offline copy of the README.
What awesome-bigdata is and what it is not
The project is a single README file of links, grouped by topic. Each entry is a link followed by a one-line description, such as a short summary of what a framework does.
It does not install, run or compare anything. The README gives no ratings or reviews, so you need to check each linked project yourself before deciding on a tool.
- It is a directory of links, not a product
- It contains no installable package
- It is licensed under MIT
Open the list and find a topic
Open the repository on GitHub at https://github.com/oxnr/awesome-bigdata. The README starts with a table of contents linking to each section, so you can jump straight to the topic you need.
Sections cover areas such as RDBMS, Frameworks, Distributed Programming, Distributed Filesystem, Columnar Databases, Time-Series Databases, Vector Databases, Data Ingestion, Scheduling, Machine Learning, Security and Data Visualization. Papers are grouped by year ranges and books are grouped by theme such as streaming and distributed systems.
- Use the table of contents to jump to a section
- Look at the one-line description to decide whether to open the link
- Follow the link to the project's own site or repository for details
Compare tools within one category
The list works best when you already know the category you need. For example, if you are researching stream processing, the Frameworks and Distributed Programming sections list several options, including Apache Flink, Apache Spark Streaming, Apache Storm, Apache Samza and Apache Beam.
Because the descriptions are brief, use the list to build a shortlist. Then check each project's documentation for features, licence and maintenance status before you commit.
- Pick one category, such as stream processing or distributed filesystems
- Note the candidates whose descriptions match your needs
- Read each linked project's documentation before choosing
Clone the repository locally (optional)
You can read the list online without installing anything. If you want a local copy to search with your own editor or tools, the README gives this command:
git clone https://github.com/oxnr/awesome-bigdataUse the list for further reading
The Interesting Readings, Interesting Papers, Videos and Books sections point to material on distributed systems and big data. The papers section lists classic work organised by period, from 2001 to 2016. These are useful starting points for students and engineers who want background on how systems like MapReduce and stream processors were designed.
- Interesting Papers: grouped into 2015-2016, 2013-2014, 2011-2012 and 2001-2010
- Books: grouped into Streaming, Distributed systems and Graph Based approach
- Other Awesome Lists: links to related curated lists
What people use it for
- A developer scanning the Frameworks section to shortlist stream processing tools to evaluate
- A data engineer comparing options in the Distributed Filesystem or Columnar Databases sections
- A student using the Interesting Papers section as a reading list on the history of MapReduce and distributed systems
- Someone looking for a Data Visualization or Business Intelligence tool to research further
Frequently asked questions
Is awesome-bigdata free to use?
Yes. The repository is licensed under the MIT licence. You can read the list online at no cost, and cloning it is optional.
Do I need to install awesome-bigdata?
No. It is a list of links in a README file, so there is nothing to install. You can browse it on GitHub. The only optional step is cloning the repository with git clone.
What are the requirements to use awesome-bigdata?
The README lists no prerequisites. A web browser is enough to read it. Git is only needed if you choose to clone the repository locally.
Does awesome-bigdata rank or review big data tools?
No. The README gives one-line descriptions for each link and no ratings or comparisons. Check each project's own documentation before choosing a tool.
What are good alternatives to awesome-bigdata?
Related curated lists include awesome-python, awesome-php and hadoopecosystemtable, which the project cites as inspiration. The README also links to other awesome lists in its final section.
Is awesome-bigdata a tutorial for learning big data?
No. It points to other resources rather than teaching. For step-by-step learning, use the documentation of the individual projects it links to, or the books and papers listed in its reading sections.
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Our verdict
Use awesome-bigdata if you want a broad map of big data tools and a reading list on distributed systems. Pick something else if you need a single tool to install, reviews or comparisons, or step-by-step tutorials.
Install options & downloads