Hadoop Overview and Market Trends

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Questions and Answers

Which component is NOT a part of Hadoop's major components?

Application Manager

Data Processor (correct)

Nodes Manager

Resource Manager

What is the query language used by HIVE for processing data?

Pig Latin

XQL

SQL

HQL (correct)

Which feature distinguishes HIVE from traditional SQL systems?

Does not support SQL datatypes

Uses a proprietary query language

Allows both real-time and batch processing (correct)

Supports real-time processing only

What programming language does Pig utilize?

Pig Latin Signup and view all the answers

Which of the following functionalities is NOT provided by Mahout?

Data streaming Signup and view all the answers

How does Avro store the data definition?

In JSON format Signup and view all the answers

What aspect of machine learning does Mahout facilitate?

Machine learnability Signup and view all the answers

After processing, where does Pig store the results?

In HDFS Signup and view all the answers

What is the main advantage of the multiple master nodes architecture in Hadoop 2 over Hadoop 1?

It improves fault tolerance. Signup and view all the answers

What is a key advantage of using Hadoop in terms of data storage?

It scales to Petabytes of data more easily. Signup and view all the answers

How does Hadoop 2 handle data access compared to traditional RDBMS?

Supports batch, interactive, streaming, and real-time in version 2.0. Signup and view all the answers

What is a common expectation regarding server failures in a large distributed system with 1,000 servers?

1 server fails every day. Signup and view all the answers

Which of the following describes the integrity level of Hadoop compared to traditional RDBMS?

Hadoop has lower integrity. Signup and view all the answers

How does a Distributed File System ensure data reliability?

By replicating each chunk of data across different machines. Signup and view all the answers

What role does HDFS play in the Hadoop ecosystem?

It stores large data sets across nodes and maintains metadata. Signup and view all the answers

What is YARN used for in Hadoop 2?

Resource management. Signup and view all the answers

Which component of Hadoop is responsible for processing large data sets using distributed algorithms?

MapReduce Signup and view all the answers

What role does the Master Node play in Hadoop’s HDFS?

Stores metadata about the file locations. Signup and view all the answers

What is a notable difference in data schema between Hadoop and traditional RDBMS?

Hadoop allows for dynamic schema. Signup and view all the answers

What is a typical chunk size used in a Distributed File System?

64-128MB Signup and view all the answers

Which function in MapReduce is responsible for aggregating data?

Reduce() Signup and view all the answers

Compared to Hadoop 1.0, what significant change was introduced in Hadoop 2.0 regarding data processing?

Support for real-time processing. Signup and view all the answers

What is the primary reason for bringing computation to data in a distributed environment?

To minimize network latency and data transfer time. Signup and view all the answers

What is the primary function of YARN in the Hadoop system?

To manage resources across clusters. Signup and view all the answers

Which of the following is NOT a common Hadoop distribution mentioned?

Oracle Database Signup and view all the answers

How does Apache HBase enhance Hadoop's capabilities?

By offering a NoSQL database capable of handling diverse data types. Signup and view all the answers

Which programming model is commonly associated with Spark and Hadoop?

MapReduce Signup and view all the answers

Which characteristic is NOT associated with Hadoop?

It provides a single-point data management interface. Signup and view all the answers

What is a key challenge in large-scale computing on commodity hardware as indicated in the context?

Distributing computation. Signup and view all the answers

What is a characteristic of typical usage patterns in a Distributed File System?

Huge files ranging from hundreds of GB to TB are common. Signup and view all the answers

What happens to chunks during machine or disk failures in a Reliable Distributed File System?

Seamless recovery occurs from replicated chunks on other machines. Signup and view all the answers

What is the primary purpose of the Map() function in MapReduce?

To filter and sort data into groups. Signup and view all the answers

What is the primary purpose of the MapReduce programming model?

To enable easy management of very-large-scale data. Signup and view all the answers

In the MapReduce process, what does the 'Group by key' step do?

It sorts and groups all pairs with the same key. Signup and view all the answers

Which of the following represents a common implementation of MapReduce?

Hadoop Signup and view all the answers

What is a key function of the MapReduce environment?

To handle machine failures and ensure task completion. Signup and view all the answers

What output does the Mapper produce in the MapReduce model?

A set of key-value pairs generated from all inputs. Signup and view all the answers

During the Reduce step of MapReduce, what is primarily combined?

Values belonging to each key from the group stage. Signup and view all the answers

Which of the following statements best describes the Map function in MapReduce?

It applies a user-defined function to each input element. Signup and view all the answers

What is a common application of the MapReduce model?

Counting occurrences of distinct elements in a dataset. Signup and view all the answers

Study Notes

Hadoop Overview

Hadoop offers cost-effective solutions for managing large-scale data, scaling efficiently to petabytes.
Provides faster performance by enabling parallel processing of data.
Effective for specific big data challenges, outperforming traditional database systems.

Key Components of Hadoop

HDFS (Hadoop Distributed File System): Core component for storing large datasets, organized across nodes while maintaining metadata via logs.
Apache HBase: A NoSQL database capable of handling diverse data types, similar to Google’s BigTable, optimized for big data operations.
MapReduce: A programming model for processing large datasets in a distributed manner using two functions, Map() for sorting/filtering, and Reduce() for aggregating results.
YARN (Yet Another Resource Negotiator): Resource management layer, facilitating the scheduling and allocation of resources across clusters, composed of Resource Manager, Node Manager, and Application Manager.
Hive: A data warehouse infrastructure for SQL-like querying of large datasets, using Hive Query Language (HQL), supporting real-time and batch processing.
Pig: Developed by Yahoo, uses Pig Latin language for data flow management, processing large datasets, and executing commands while handling MapReduce operations.
Mahout: Provides machine learning capabilities, offering tools for clustering, classification, and collaborative filtering based on user patterns and algorithms.
Avro: Stores data definitions in JSON and the data itself in a binary format for efficiency; designed for compatibility with MapReduce processes.

Hadoop Evolution

Hadoop 1: Employed a Master-Slave architecture, susceptible to a single point of failure. If the master node failed, the entire cluster became non-operational.
Hadoop 2: Improved architecture with multiple masters, allowing for redundancy and failover capabilities, enhancing system reliability.

Hadoop Distributions

Open Source: Apache Hadoop.
Commercial: Cloudera, Hortonworks, MapR, AWS MapReduce, Microsoft Azure HDInsight.

Comparison: RDBMS vs. Hadoop

RDBMS: Optimized for gigabytes to terabytes, providing immediate query responses and supporting high data integrity with a static schema.
Hadoop: Handles data sizes from petabytes to exabytes, capable of batch and real-time processing, featuring dynamic schema and lower data integrity.

Storage and Programming Infrastructure

Distributed File System: Utilizes chunk servers to split files into manageable chunks (64-128MB), which are replicated for reliability across different machines.
MapReduce Programming Model: Facilitates easy parallel programming, autonomous hardware failure management, and efficient handling of very large datasets through a three-step process: Map, Group by Key, and Reduce.

Example: Word Counting in MapReduce

Implements a task to count distinct words in a huge text document, demonstrating the practical applications of analysis on log data and machine translation.

Environment Management in MapReduce

Automates data partitioning, scheduling, key grouping, managing machine failures, and inter-machine communication for optimal operation.

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Description

This quiz provides insights into the Hadoop ecosystem, focusing on its advantages such as cost-effectiveness, speed, and suitability for big data problems. It also covers companies utilizing Hadoop and forecasts for job market growth in this field.