Curated List of Big Data Learning Resources from w3cschool

In today's society, we have entered the era of big data, and the explosive amount of information poses challenges to traditional computing and information technologies. Big data technology is helping the public and enterprises open the doors to the DT world. Learning big data not only has good prospects but also high salaries. To help programmers better and more deeply understand big data, w3cschool has compiled a GitHub Awesome Big Data resource list for reference. The resource types mainly include big data frameworks, papers, and other practical collections.

1. Relational Database Management Systems (RDBMS)

Relational Database Management Systems manage relational databases and organize data into related rows and columns. MySQL, SQL Server, PostgreSQL, Oracle, etc., are examples of RDBMS.

2. Distributed File Systems

Distributed file systems refer to file systems where the physical storage resources are not necessarily directly connected to the local node but are linked via a computer network, i.e., cluster file systems, supporting a large number of nodes and petabyte‑scale storage. w3cschool senior users have collected distributed file system resources, mainly including Apache HDFS, BeeGFS, Ceph Filesystem, Disco DDFS, etc.; details can be found at http://123.w3cschool.cn/bigdata .

3. Key‑Value Data Model

Key‑value databases are especially aggregation‑oriented, meaning they are built around aggregations. Each aggregation contains a key or ID used to retrieve data. This section includes Aerospike, Amazon DynamoDB, ElephantDB, EventStore, GridDB, etc.

4. Distributed Programming

Distributed programming is a program design method that coordinates execution across several computers in a distributed system; its main features are distribution and communication. When using distributed programming, a program consists of several independently executable modules. w3cschool senior users have collected distributed programming resources, mainly including AddThis Hydra, AMPLab SIMR, Apache Beam, Apache Crunch, Cascalog, etc.

5. File Data Model

w3cschool senior users have collected file data model resources, which, although not many, are essential, including Actian Versant, Crate Data, Facebook Apollo, jumboDB, etc.; see http://123.w3cschool.cn/bigdata .

6. Key‑Map Data Model and Framework

This part mainly includes Apache Accumulo, Apache Cassandra, Apache HBase, etc.

In the framework resources, w3cschool senior users are still collecting, and the currently organized resources include Apache Hadoop, Tigon.

The above big data learning resource collection compiled by w3cschool senior users aims to help everyone. This content is continuously being organized; those interested can click the original article to read more. The future society is a big data society, and the prospects for big data development are limitless—start learning now!

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