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.

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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

# Big Data Defined

How can we identify Big Data? Is it 1 gigabytes, 10 terabytes or a 1000 petabytes? With each passing year as the processing and storing capacity of hardware improves, we can process bigger and bigger data sizes. We can't say that any size greater than a number constitutes Big Data. Big Data is the exponential increase and availability of data in our world. More formally, Big Data is defined as data with greater variety arriving in increasing volumes and with ever higher velocity and requires a scalable architecture for efficient storage, manipulation, and analysis. These  properties are also known as the three Vs. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems that couldn't be tackled before.

+ __Volume__: The sheer amount of data matters.  It’s estimated that 40 zettabytes of data will be created by 2020, an increase of 300 times from 2005. With Big Data, one has to process high volumes of data. These can be data of unknown value, such as Twitter data feeds, click streams on a webpage or a mobile app, or sensor-enabled equipment. For some organizations, this might be tens of terabytes of data. For others, it may be hundreds of petabytes.

+ __Velocity__: Velocity is the  rate at which data is received and acted on. Normally, the highest velocity of data streams directly into memory, instead of being written to disk. Some internet-enabled smart products operate in real-time or near real-time and will require real-time evaluation and action.

+ __Variety__: Variety refers to the many types of available data. Data comes in different forms. Structured data can be organized neatly within the columns of a database. This type of data is relatively easy to enter, store, query, and analyze. Unstructured data is more difficult to sort and extract value from. Examples of unstructured data include emails, social media posts, word-processing documents; audio, video, or photo files, web pages, and more.

This lesson formally presents the definition of Big Data.

Big Data Defined

Hadoop

YARN

Map Reduce

HDFS

Spark

Input & Output Formats

Misc

Quiz

Reference: Replication

Reference: Partitioning

Reference: Transactions

Reference: Issues in Distributed Systems

Big Data Defined

Big Data Defined