Master the art of manipulating files within a distributed storage enterprise platform. It's easier than you think!

The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. It has many similarities with existing distributed file systems.

We will be manipulating the HDFS File System, however why are Enterprises interested in HDFS to begin with?

However, the differences from other distributed file systems are significant.

HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware.

HDFS provides high throughput access to application data and is suitable for applications that have large data sets.

HDFS relaxes a few POSIX requirements to enable streaming access to file system data.

HDFS is part of the Apache Hadoop Core project.

Hardware failure is the norm rather than the exception. An HDFS instance may consist of hundreds or thousands of server machines, each storing part of the file system’s data. The fact that there are a huge number of components and that each component has a non-trivial probability of  failure means that some component of HDFS is always non-functional. Therefore, detection of faults and quick, automatic recovery from them is a core architectural goal of HDFS.

Applications that run on HDFS have large data sets. A typical file in  HDFS is gigabytes to terabytes in size. Thus, HDFS is tuned to support large files. It should provide high aggregate data bandwidth and scale  to hundreds of nodes in a single cluster. It should support tens of   millions of files in a single instance.

A computation requested by an application is much more efficient if it  is executed near the data it operates on. This is especially true when the size of the data set is huge. This minimizes network congestion and  increases the overall throughput of the system. The assumption is that 
it is often better to migrate the computation closer to where the data  is located rather than moving the data to where the application is running. HDFS provides interfaces for applications to move themselves  closer to where the data is located.

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Here I present a curriculum as to the current state of my Cloudera courses.

My Hadoop courses are based on Vagrant so that you can practice and destroy your virtual environment before applying the installation onto real servers/VMs.