Hadoop Training

Explore what big data means: data volumes that overwhelm traditional storage and processing. See how social networks generate vast data streams and the processing challenges.

Explore the key challenges in processing big data, including storage and management of vast datasets and timely extraction, and preview practical solutions to overcome these hurdles.

Explore how the open source Hadoop framework, rooted in Java, enables distributed processing and storage of large datasets across clusters of computers, scaling from one to thousands of machines.

Hadoop training explains HDFS as a primary storage system for large data, organizing data into blocks, managed by a name node, and spread across a cluster for high throughput.

Discover how HDFS architecture manages the complete file system, organizes data into blocks, and tracks spaces across sites, with checkpoints and a Java program.

Explore deeper HDFS architecture in Hadoop training, focusing on blocks, logs, and how destination choices influence data placement.

Learn to install Hadoop on Windows and compare standalone, pseudo-distributed, and fully distributed modes. See how local daemon processes simulate a cluster for development and debugging.

Learn the prerequisite software and tools for installing Hadoop on Windows, including supported Windows versions and Oracle JDK 1.6 or higher. This guide covers setup steps and points to videos.

Learn the requirements and how to download the setup files for Hadoop, including choosing the binary package over the sports package, and begin configuring Java on Windows.

Set up the downloaded Hadoop by opening a command prompt, copying the extracted Hadoop folder to the C drive, and updating the environment variable to include its location.

Learn how to modify XML files to add properties and initialize property names within a Hadoop training context.

Begin the next step in modification of xml files to add properties by adjusting the properties order for Hadoop projects.

Learn how to modify XML files to add properties within an application, and perform the final modification to manage data and properties.

Modify XML files to add properties for Hadoop configuration, covering yarn settings and delegation aspects, as a next step in the training.

Modify XML and then verify that Hadoop processes are up and running by setting up the bin, copying necessary files, and starting the four main distributed filesystem processes.

Use the Hadoop UI to monitor MapReduce jobs and push a sample file into the cluster, then verify the file's processing and deletion after execution.

Know yarn in brief explains the core components, including a resource manager for submitting jobs and a node manager to run tasks, and how map reduce jobs are scheduled.

Learn how to install yarn, install it globally, run yarn in a project, and understand the basic syntax differences in the package workflow.

Review the log file to identify the dependencies and setup chores your Hadoop project needs to function.

Yarn allocates resources for a job by submitting an application to the resource manager, which launches the application to execute and monitor the job using containers and map tasks.

Explore what Apache Pig is as an abstraction and a tool on Hadoop, enabling users to analyze large data sets and streamline Hadoop data processing.

Explore Apache Pig to simplify big data operations like join, sort, and filter, deploy extensible user-defined functions in Java and other languages, and store results in HDFS.

Explore an example of pig to understand how to scan petabytes of data, test theories with scripts, and manage semi-structured or unstructured data with reusable code.

Install Apache Pig on a Hadoop local setup, configure environment variables, and run a sample Pig script in local and DFS file system modes, with basic log and property adjustments.

Discover how Scoop transfers data between Hadoop and relational databases, importing from relational databases into the Hadoop distributed file system and exporting back to relational databases within the Hadoop ecosystem.

Download required libraries from Apache, extract scoop files, and move the libraries to the system global library and the user library directories, completing the scoop setup.

View the library folder contents, then export the scoop library to the PATH by updating your bashrc, making scoop binaries globally accessible from the bin directory.

Connect to the MySQL database, check the current tables, and run a simple scoop import to verify the workflow and enable future data transfers.

import records to hdfs in step-2 and verify the imported employee data, exiting safe mode and viewing the script contents to confirm successful imports.

Open a new terminal window and run Scoop help to view available commands. Check Scoop version to see what is installed.

Explain what Apache Flume is as a simple, robust, flexible, and extensible data-injection tool, with a simple illustrative example and guidance on basic usage for practice.

Explore what Flume is and the applications that use it, and review a visual example to reinforce the concept.

Understand Flume architecture basics, including the Flume agent, channels such as the file channel, and sinks, and how data flows through channels affecting throughput and reliability.

Learn how to start and stop a Flume agent using flow engine commands, specify its configuration file with -f, and set Java system properties with -D to enable console logging.

Configure the flume agent with a properties file, defining an agent, memory channel, and logger sink; map sources to the channel, bind the sink, and set capacity and transaction capacity.

Configure a hello world in Flume using Cloudera by defining AGENT 1 with a memory channel, then run flume-ng and test with telnet to localhost:4444 to see Hello World.

Hive sits on top of Hadoop and uses hdfs and mapreduce for data analysis, exposing cli, browser, and Hive server interfaces while organizing data in databases, tables, partitions, and buckets.

Explore basic HiveQL commands to create databases and show tables, define a Hive table with fields like student_id int and name string, and load data into the table.

Learn how map reduce counts spades in a deck using a map phase, shuffle and sort, and a reduce phase, driven by a job driver.