Learn BigData & Hadoop with Practical

Explore how big data grows from daily smartphone and log file generation, and how data centers store, provision storage, and enable local processing for online business needs.

Compare big data handling with conventional databases: relational systems can't handle big data; Hadoop distributes data across machines, partitions and replicates data for fault tolerance, forming a data ecosystem.

Explore the five dimensions of big data—volume, velocity, variety, veracity, and value—and how they influence data quality, structure, and distributed processing with Hadoop.

Explore how big data enables smarter decisions through analysis and trend insight. Show how Hadoop-backed data storage and replication improve reliability and risk identification.

Learn how Hadoop, the open source framework, enables distributed storage with a distributed file system and distributed processing via map reduce on commodity hardware clusters for scalable big data.

Explore the Hadoop cluster architecture, including HDFS with NameNode, secondary NameNode, and DataNodes, MapReduce components, and three-copy replication for reliability.

Explore how MapReduce runs on Hadoop’s dfs, with blocks replicated across machines and a name node metadata system guiding map and reduce tasks.

install and explore the cloudera hadoop distribution by setting up the cloudera virtual machine with Oracle VirtualBox, understanding the distributed file system (dfs) and the open source ecosystem.

Learn to copy files between the local system and the Hadoop filesystem, and verify presence with listings. Delete files when needed and manage duplicates.

Move files from the local system to the hdfs, compare moving with copying, and display directory sizes in a human readable format to manage data in hdfs.

Run map reduce to compute the standard deviation of word lengths in input data using Hadoop, jars, and example programs, then save results to an output file.

Execute a map reduce word count job on hadoop, from input to output file, and verify results by reading the dfs output and copying data to local storage.