Learn BigData & Hadoop with Practical

Explore how big data arises from exponential data growth, cheap storage, and distributed, parallel processing, highlighting why conventional databases struggle with size, velocity, and data variety.

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.

Explore how social media, smartphones, health records, satellites, sensors, CCTV, weather data, and e-commerce activity generate massive big data from terabytes to exabytes, and why conventional databases can't scale.

Explore how big data grows exponentially, fueled by smartphones and social platforms, and drives health care savings and marketing growth through pattern discovery.

Discover big data from diverse sources and why big data testing is an emerging field, with Hadoop tools that don't require Java.

Understand what big data is and why traditional management systems can't handle it; discover how Hadoop and Cloudera, an enterprise solution, manage, store, and manipulate the data ecosystem.

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 how big data fuels analysis across industries, revealing customer behavior, predicting outcomes, and guiding decisions in e-commerce, healthcare, elections, and media.

Big data handles structured, semi-structured, and unstructured data, while conventional databases struggle with unstructured data; big data can read these formats and extract meaningful information from vast, noisy sources.

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.

Explore how data science derives insights from big data and turns them into action, using the Hadoop ecosystem, architecture, data storage, and hands-on reviews.

Explore the Hadoop ecosystem as a collection of software components, not a single program, and learn how MapReduce and related tools integrate with other platforms to handle big data.

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 distributed file system concept, including the master-slave model, server-client access, block-based storage with 64 mb blocks, and metadata for concurrent, consistent data across racks.

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

Explore map reduce, the Google origin of 2004, and how Hadoop uses map reduce and HDFS to process large data in parallel across a cluster.

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.

Copy a local file into the Hadoop file system using terminal commands, then verify with ls and manage files in a Cloudera virtual machine.

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.