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

# Parquet

Parquet literally means _a patterned wooden surface_. But, in Hadoop world, the term refers to a columnar storage format. Parquet is based on [Google's Dremel](https://research.google/pubs/pub36632/) paper. It is the love-child of collaboration between Cloudera and Twitter engineers. What sets Parquet apart from other columnar formats is its ability to efficiently store nested data. Deeply nested fields are also stored in a truly columnar fashion and can be read independently of other fields. Let's see an example. Consider the following record which represents the structure for a car record in JSON:

```json
{
  make        : "",
  model       : "",
  year        : "",
  horsepower  : "",
  stats       : {
                   zeroToSixty: "",
                   torque: "",
                   topSpeed: ""
                }
}
```

We used JSON for readability, but the record could be in any format. The record is an example of nested data. The nested __stats__ field is another object with three fields of its own. When the car record is stored in columnar format, all the top-level fields (make, model, year, horsepower, and stats) can be considered rows. Here’s a chart on of how data from several car records, gets stored as columnar format :


This lesson introduces the Parquet format used for storing data in columnar format.

Parquet: Intro

Hadoop

YARN

Map Reduce

HDFS

Spark

Input & Output Formats

Misc

Quiz

Reference: Replication

Reference: Partitioning

Reference: Transactions

Reference: Issues in Distributed Systems

Parquet: Intro

Parquet