Hadoop 3 Big Data Processing Hands On [Intermediate Level]

Trace data history and the emergence of big data with Hadoop, then define four traits—volume, velocity, variety, veracity—and real-time processing of structured, semi-structured, and unstructured data.

Learn how Apache Hadoop 3x enables open-source, Java-based data storage and processing across distributed clusters using commodity hardware, via HDFS and MapReduce.

Explore the high-level architecture of Hadoop 1.x, including HDFS and MapReduce, and learn how NameNode, DataNode, Secondary NameNode, and JobTracker orchestrate distributed storage and computation.

Explore the Hadoop 1.x architecture, including the hdfs storage layer, name node and data nodes, and block replication. Examine the map and reduce computation model, splits, and data locality concepts.

Introduces Hadoop 3x architecture, highlighting the resource manager, node managers, and containerized processing. Explains high availability with active and standby name nodes and zookeeper failover.

Explore Hadoop 3x architecture, including resource manager, application master, node managers, and containers, and how yarn scheduling, data locality, and high availability improve over 1x.

Explore why Hadoop 3.x adds high availability with one active and multiple standby nodes, and reduces storage overhead with air show coding while expanding data balancing and cloud storage support.

Discover why Hadoop 3.x demands Java 8, embrace read Solomon parity coding, and reduce storage overhead with striped data and parity blocks for archival and cold storage efficiency.

Explore Hadoop 3.0 features, including timeline server version 2 with visualization. See enhanced high availability, port changes removal, and a modern yarn ui with memory and heat maps.

Explore Hadoop 3 improvements, including daemonized services and single-jar client configuration. It covers opportunistic containers, native map output processing, federated sub-clusters, and cloud storage connectors with Amazon S3 consistency.

Prepare a multi-node Hadoop 3 lab by provisioning three virtual machines with 1.5 GB RAM and 50 GB disk each using Oracle VirtualBox on Ubuntu 16.04, with apt-get setup.

Set up an Ubuntu virtual machine for Hadoop 3 lab with VirtualBox. Allocate memory, create a 15 GB dynamic disk with 64-bit Linux, attach the ISO, and enable NAT networking.

Explore Linux fundamentals and the command line interface, learning Bash basics, shells like Dash, and essential commands such as ls, sort, grep, and man in a Linux environment.

Master Linux fundamentals through practical command usage, exploring recursive listing, file and directory operations, navigation shortcuts, vim editing, sudo privileges, and essential text tools like cat, head, tail, and grep.

Manage Linux users and groups, switch identities, and control access with file permissions. Explore octal and symbolic chmod, ownership with chown, and sudo in a multi-user Hadoop setup.

Install and configure Hadoop 3x on virtual machines by updating apt repositories, installing Java 8, SSL, and editors, then download Hadoop 3.0.1.0 from the Apache archive.

Set up static network configurations for Hadoop cluster nodes and enable passwordless ssh by generating rsa keys and configuring authorized_keys, ensuring reliable inter-node communication over port 22.

Set up a Hadoop 3 environment by launching the VM, connecting via SSH, installing Hadoop 3.x, creating a Hadoop user and group, and updating bashrc for environment variables.

Explore the inside of Hadoop 3.x, examining core-site.xml, yarn-site.xml, and mapred-site.xml, along with essential directories and java home setup for passwordless ssh.

Explore how Hadoop 3x extends architecture with erasure coding, introducing stripe-based block groups, extended name node and client roles, and decoding workflows for recovery and parity blocks.

Set up a Hadoop 3x lab cluster by configuring environment and core-site for high availability, adjusting Java and Hadoop homes, memory settings, and directory permissions.

Configure a three-node Hadoop 3x cluster by setting core-site and HDFS properties, including the name service, data and name directories, replication factor, and zookeeper-based automatic failover.

Set up a Hadoop 3x cluster by configuring Yarn as MapReduce framework, defining resource manager, node manager, and workers across three machines, with ZooKeeper preparation.

Advance your Hadoop 3x cluster setup by configuring zookeeper, editing the zookeeper configuration, setting data directories and ports, defining server IDs, and cloning VMs for a scalable deployment.

Learn to set up a Hadoop cluster by cloning lab machines, then individually update IP addresses and host names for machines 33 and 34, and verify connections.

Connect three machines, configure Zookeeper IDs, format both name nodes, bootstrap the failover controller, start Zookeeper and data nodes to form a synchronized Hadoop cluster.

Learn how to start and stop a Hadoop cluster across three machines, verify services, and monitor via name node, data node, zookeeper, yarn, and history server.

Learn to use hdfs dfs and dfs admin commands to manage a Hadoop cluster, view dfs report, inspect active vs standby name nodes, and balance using dfs balancer.

Learn erasure coding in Hadoop 3 by enabling, applying, and viewing policies in the SDF filesystem, and applying them to a directory like degree_easy.

Learn to run a yarn application on a Hadoop 3.x cluster by configuring environments across multiple machines and executing a sample mapreduce job with yarn jar.

Clone a new machine for commissioning, set its hostname and IP, update /etc/hosts, prepare Hadoop directories, adjust permissions, and start A.P., the data node, and node manager to join cluster.

Commission a new data node into a Hadoop cluster by updating hosts, configuring commissioning and decommissioning files, and refreshing the cluster with DFS and YARN commands.

Edit the decommissioning json on all nodes to mark machines for maintenance or decommission, then refresh and verify status with dfs admin report. Verify replication across all four machines.

Install Hive on Hadoop to enable SQL queries on HDFS; Hive acts as a data warehouse on Hadoop that compiles SQL into MapReduce jobs and supports partitioning and bucketing.

Load data in Hive using an external table, configure the data warehouse location, define delimiters, and extract the max salary by city with a subquery.

Hadoop uses three schedulers: fifo, fair, and capacity, to allocate cluster resources. The lecture explains queues, pools, minimum shares, priorities, and preemption.

Examine typical Hadoop production environments, including on-prem and cloud distributions, master–slave architecture, and Yarn. Understand storage, memory sizing, networking, and workload patterns for scalable big data processing.