Hadoop Fundamentals

Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from a single server to thousands of machines, each offering local computation and storage.

Key Definitions

• Big Data: Data sets that are so large or complex that traditional data processing applications are inadequate.
• Distributed Computing: A model in which components located on networked computers communicate and coordinate their actions by passing messages.
• MapReduce: A programming model for processing large data sets with a distributed algorithm on a cluster.

The Hadoop architecture consists of two main components:

1. Hadoop Distributed File System (HDFS): A distributed file system that stores data across multiple machines.
2. MapReduce: A processing engine that handles the processing of data in a distributed manner.

The Hadoop ecosystem includes various tools that complement Hadoop's functionality:

• Apache Hive
• Apache Pig
• Apache HBase
• Apache Spark
• Apache Zookeeper

Step-by-step Installation Process

# Update package index
sudo apt-get update

# Install Hadoop
sudo apt-get install hadoop
            

Configure Hadoop by editing the core-site.xml and hdfs-site.xml files located in the /etc/hadoop directory.

Basic HDFS Commands

# List files in HDFS
hadoop fs -ls /

# Copy a file from local to HDFS
hadoop fs -put localfile.txt /hdfspath/

# Get a file from HDFS to local
hadoop fs -get /hdfspath/remotefile.txt localdir/
            

Follow these best practices for effective use of Hadoop:

• Use proper partitioning to enhance performance.
• Regularly monitor and optimize the performance of HDFS.
• Secure your Hadoop cluster by configuring authentication and authorization.
• Utilize Apache Hive or Apache Pig for easier data processing.