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

## Projection Schema & Misc. Tools

In the previous section, we learned to write a Parquet file using an Avro model. The allure of storing data in Parquet format is the ability to read columns independent of other columns for a given record. So how can we do that programmatically? Specify a _projection schema_; set it on the reader object to filter the columns we want to read from a Parquet file. The projection schema is a subset of the schema used to write a Parquet file with.

Thrift and Protocol Buffers also allow for projection in a similar manner. However, Avro also comes with an option to set a read schema. The read schema resolves Avro records, as Avro allows for the evolution of schema.

In the code widget below, we read only the horsepower column for all the car records, and then compute the average horsepower for the car records in our Parquet file. We use the auto-generated `Car` class to read-in the records. The unread columns get assigned their default values of null.

# Projection Schema & Misc. Tools

In the previous section, we learned to write a Parquet file using an Avro model. The allure of storing data in Parquet format is the ability to read columns independent of other columns for a given record. So how can we do that programmatically? Specify a _projection schema_; set it on the reader object to filter the columns we want to read from a Parquet file. The projection schema is a subset of the schema used to write a Parquet file with.

Thrift and Protocol Buffers also allow for projection in a similar manner. However, Avro also comes with an option to set a read schema. The read schema resolves Avro records, as Avro allows for the evolution of schema.

In the code widget below, we read only the horsepower column for all the car records, and then compute the average horsepower for the car records in our Parquet file. We use the auto-generated `Car` class to read-in the records. The unread columns get assigned their default values of null.

This lesson demonstrates the use of a projection schema and other miscellaneous parquet related tools.

Parquet: Projection Schema & Misc. Tools

Hadoop

YARN

Map Reduce

HDFS

Spark

Input & Output Formats

Misc

Quiz

Reference: Replication

Reference: Partitioning

Reference: Transactions

Reference: Issues in Distributed Systems

Parquet: Projection Schema & Misc. Tools

Projection Schema & Misc. Tools