Delve into Big Data essentials, explore data types, and gain insights into Hadoop components like YARN, MapReduce, HDFS, and Spark. Discover foundations to excel in the growing Big Data field.

data.tar.gz

HADOOP_HOME

JAVA_HOME

HDFS_NAMENODE_USER

HDFS_DATANODE_USER

HDFS_SECONDARYNAMENODE_USER

YARN_RESOURCEMANAGER_USER

YARN_NODEMANAGER_USER

HADOOP_CONF_DIR

ZK_HOME

PIG_HOME

AvroWriteExample

AvroReadExample

AvroGeneratedCodeReadExample

AvroGeneratedCodeWriteExample

AvroRPCExample

ParquetReadExampleJob

ParquetWriteExampleJob

ParquetAvroReadExampleJob

ParquetAvroWriteExampleJob

ParquetProjectionReadExampleJob

SequenceFileReadExampleJob

SequenceFileWriteExampleJob

SequenceFileSyncPointExampleJob

TestCarMapperJob

TestCarReducerJob

CarCounterMrProgramJob

MyLiveAppJob

DataNodeWebUI2

YarnWebUI

YarnWebUI-copy

YarnWebUI-copy-copy

JHS-UI

Spark-UI-copy

Spark-History-Server-UI-3

This course offers a one-of-a-kind rich and interactive experience to learn the fundamentals and basics of Big Data. Throughout this course, you will have plenty of opportunities to get your hands dirty with functioning Hadoop clusters.

You will start off by learning about the rise of Big Data as well as the different types of data like structured, unstructured, and semi-structured data. You will then dive into the fundamentals of Big Data such as YARN (yet another resource manager), MapReduce, HDFS (Hadoop Distributed File System), and Spark.

By the end of this course, you will have the foundations in place to start working with Big Data, which is a massively growing field.

Introduction to Big Data and Hadoop

## Architecture

Spark is a distributed parallel data-processing framework and bears many similarities to the traditional MapReduce framework. Spark has the same master-slave architecture as MapReduce, where one process, the master, coordinates and distributes work among slave processes. These two processes are formally called:

+ Driver
+ Executor


## Driver
The driver is the master process that manages the execution of a Spark job. It is responsible for maintaining the overall state of the Spark application, responding to a user's program or input and analyzing, distributing and scheduling work among executor processes. The driver process is the heart of the Spark application and maintains all application related information during an application's lifetime.

## Executor
Executors are the slave processes that execute the code assigned to them by the driver process. They report the state of the computation on that executor back to the driver.

# Architecture

Spark is a distributed parallel data-processing framework and bears many similarities to the traditional MapReduce framework. Spark has the same master-slave architecture as MapReduce, where one process, the master, coordinates and distributes work among slave processes. These two processes are formally called:

+ Driver
+ Executor


# Driver
The driver is the master process that manages the execution of a Spark job. It is responsible for maintaining the overall state of the Spark application, responding to a user's program or input and analyzing, distributing and scheduling work among executor processes. The driver process is the heart of the Spark application and maintains all application related information during an application's lifetime.

# Executor
Executors are the slave processes that execute the code assigned to them by the driver process. They report the state of the computation on that executor back to the driver.

This lesson describes the architecture of Spark.

Architecture

Hadoop

YARN

Map Reduce

HDFS

Spark

Input & Output Formats

Misc

Quiz

Reference: Replication

Reference: Partitioning

Reference: Transactions

Reference: Issues in Distributed Systems

Architecture

Architecture

Driver