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Hadoop Big Data

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Big Data Engineer – Hadoop/Spark, Senior Big Data Hadoop Engineer

 

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What is Big Data?

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Reasons of Big Data Generation

Why Big Data deserves your attention

Use cases of Big Data

Different options of analyzing Big Data

What is Hadoop,

History of Hadoop

How Hadoop name was given

RDBMS and Hadoop

Hadoop Architecture

Features of Hadoop

Hadoop Components- HDFS, Mapreduce

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Hadoop

 

Apache Hadoop is a collection of open-source software utilities that facilitate using a network of many computers to solve problems involving massive amounts of data and computation.

 

Hadoop key words

 

Architecture

 

Hadoop follows a master slave architecture design for data storage and distributed data processing using HDFS and Map Reduce respectively. The master node for data storage is hadoop HDFS is the Name Node and the master node for parallel processing of data using Hadoop Map Reduce is the Job Tracker.

 

Alternatives

 

  •         Apache Spark. Apache Spark promises faster speeds than Hadoop Map Reduce along with good application programming interfaces. …
  •         Cluster Map Reduce. …
  •         High Performance Computing Cluster. …
  •         Hydra. …

 

Advantages

 

Hadoop is an excellent open source platform. Hadoop gives in the best data management provisions too. The advantages of Hadoop – the big data platform include – that Hadoop is cost effective, is a highly scalable storage platform, Hadoop works on distributed file system that works on ‘mapping’ data.

 

Applications

 

  •         Capacity: Hadoop stores large volumes of data. …
  •         Speed: Hadoop stores and retrieves data faster. …
  •         HDFS: Maintaining the Distributed File System. …
  •         YARN: Yet Another Resource Negotiator. …
  •         Map Reduce. …

 

Basics

 

  •         Hadoop Distributed File System (HDFSTM) – Provides access to application data. …
  •         Hadoop YARN – Provides the framework to schedule jobs and manage resources across the cluster that holds the data.
  •         Hadoop Map Reduce – A YARN-based parallel processing system for large data sets.

 

Benefits

 

Hadoop is an excellent open source platform. Hadoop gives in the best data management provisions too. The advantages of Hadoop – the big data platform include – that Hadoop is cost effective, is a highly scalable storage platform, Hadoop works on distributed file system that works on ‘mapping’ data.

 

What do you mean by Hadoop?

 

Apache Hadoop YARN Apache  Hadoop YARN is the resource management and job scheduling technology in the open source Hadoop distributed processing … See complete definition Hadoop data lake A Hadoop data lake is a data management platform comprising one or more Hadoop clusters.

 

What is the Hadoop platform?

 

Apache Hadoop is an open source software platform for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. Hadoop services provide for data storage, data processing, data access, data governance, security, and operations.

 

What is Hadoop and what is it used for?

 

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

 

Configuration

 

The core-site.xml file informs Hadoop daemon where Name Node runs in the cluster. It contains the configuration settings for Hadoop Core such as I/O settings that are common to HDFS and Map Reduce. … Here, we can configure hdfs-site.xml to specify default block replication and permission checking on HDFS.

 

Disadvantages

 

  •         Security Concerns. Just managing a complex application such as Hadoop can be challenging. …
  •         Vulnerable By Nature. Speaking of security, the very makeup of Hadoop makes running it a risky proposition. …
  •         Not Fit for Small Data. …
  •         Potential Stability Issues. …
  •         General Limitations.

 

Definition

 

Hadoop is an open source distributed processing framework that manages data processing and storage for big data applications running in clustered systems.

 

Features

 

  •         Hadoop Brings Flexibility In Data Processing: …
  •         Hadoop Is Easily Scalable. …
  •         Hadoop Is Fault Tolerant. …
  •         Hadoop Is Great At Faster Data Processing. …
  •         Hadoop Ecosystem Is Robust: …

 

History

 

History. According to its co-founders, Doug Cutting and Mike Cafarella, the genesis of Hadoop was the “Google File System” paper that was published in October 2003. … Development started on the Apache Nutch project, but was moved to the new Hadoop subproject in January 2006.

 

Who invented the Hadoop?

 

Hadoop was created by Doug Cutting and Mike Cafarella in 2005. It was originally developed to support distribution for the Nutch search engine project. Doug, who was working at Yahoo! at the time and is now Chief Architect of Cloud era, named the project after his son’s toy elephant.

 

Where did the name Hadoop come from?

 

The name Hadoop is not an acronym; it’s a made-up name. The project’s creator, Doug Cutting, explains how the name came about: The name my kid gave a stuffed yellow elephant. Short, relatively easy to spell and pronounce, meaningless, and not used elsewhere: those are my naming criteria.

 

Limitations

 

  •         3.1. Issues with Small Files. The main problem with Hadoop is that it is not suitable for small data. …
  •         3.2. Slow Processing Speed. …
  •         3.3. Support for Batch Processing only. …
  •         3.4. No Real-time Processing. …

 

Overview

 

Hadoop is an open source framework, from the Apache foundation, capable of processing large amounts of heterogeneous data sets in a distributed fashion across clusters of commodity computers and hardware using a simplified programming model. Hadoop provides a reliable shared storage and analysis system.

 

Purpose

 

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

 

Requirements

 

  •         1) Intel Core 2 Duo/Quad/hex/Octa or higher end 64 bit processor PC or Laptop (Minimum operating frequency of 2.5GHz)
  •         2) Hard Disk capacity of 1- 4TB.
  •         3) 64-512 GB RAM.
  •         4) 10 Gigabit Ethernet or Bonded Gigabit Ethernet.

 

Security

 

Kerberos is the basis for authentication in Hadoop secure mode. … The Knox API from the Apache Hadoop project is used to extend Active Directory or LDAP to Hadoop clusters. It is also used to extend federated identity management solutions into the environment.

 

Performance

 

The foremost step to ensure maximum performance for a Hadoop job, is to tune the best configuration parameters for memory, by monitoring the memory usage on the server. Apache Hadoop has various options on memory, disk, CPU and network that helps optimize the performance of the hadoop cluster.

 

 

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