Big Data and Hadoop

Start with data characteristics

The big data mind map introduces volume, variety and velocity. It describes how organizations store large amounts of data from different sources and the storage capacity this can require. Start here to see the data characteristics before moving on to the maps about Hadoop.

As you study, use the branches to connect each characteristic with the descriptions in the map. The Computing key words flashcard deck can also help you work with terminology alongside the maps.

Explore Hadoop components

Apache Hadoop is a useful next step. Its map brings together Hadoop features and environment, system requirements, HDFS, MapReduce and YARN. Read the overview first, then follow the component branches to see how the map organizes them.

For another view, the Hadoop map on distributed storage describes data being split into blocks and stored across multiple machines. Hadoop Conceptual Map also brings together HDFS, MapReduce and YARN, with flexible handling of structured, semi-structured and unstructured data.

Look more closely at connections

Internal Components of Apache Hadoop YARN focuses on the client, resource manager, node manager and application container. Its branches describe roles such as requesting application execution and allocating resources. After the broader component map, this provides a more focused look at YARN.

Another mind map connects big data analytics and cloud computing through themes including scalability, accessibility, cost efficiency and security. It offers a way to compare those themes with the Hadoop maps.

Use the maps for self-checking

This topic is part of Data, Information and Data Handling in Computing and ICT (programming, computer science, digital skills, educational technology). The listed maps bring data characteristics, storage and processing together within that unit. There are no quizzes on this topic page, so use the maps for self-checking: cover a branch and explain it before looking back. Choose a map and follow its branches at your own pace.