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Big Data Tutorial Pdf

ing, big data, analytics, software development, service management, and secu- solutions for big data, real-time analytics, social intelligence, and community. Brdo, Nov 10th Big Data Tutorial: ruthenpress.info /big-datatutorial-grobelnikfortunamladenicsydneyiswc “Big Data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze.” (McKinsey Global.

Or, contact hall coreservlets. Following is an extensive series of tutorials on developing Big-Data Applications with Hadoop. Since each section includes exercises and exercise solutions, this can also be viewed as a self-paced Hadoop training course. All the slides, source code, exercises, and exercise solutions are free for unrestricted use. Click on a section below to expand its content. The relatively few parts on IDE development and deployment use Eclipse, but of course none of the actual code is Eclipse-specific. These tutorials assume that you already know Java; they definitely move too fast for those without at least moderate prior Java experience. If you don't already know the Java language, please see the Java programming tutorial series. For options for customized Hadoop training onsite at your organization, please see the Hadoop training course page or email hall coreservlets. Overview of the Hadoop Tutorial Series It is becoming increasingly common to have data sets that are too large to be handled by traditional databases, or by any technique that runs on a single computer or even a small cluster of computers. In the age of Big-Data, Hadoop has evolved as the library of choice for handling it. This tutorial gives a thorough introduction to Hadoop, along with many of the supporting libraries and packages. It also includes a free downloadable virtual machine that already has Hadoop installed and configured, so that you can quickly write code and test it out. See the "Source Code and Virtual Machine" section at the bottom of this tutorial. These tutorials are written by Hadoop expert Dima May , and are derived from the world-renowned coreservlets.

The basic Hadoop programming language is Java, but this does not mean you can code only in Java. Hadoop efficiently processes large volumes of data on a cluster of commodity hardware.

Hadoop Tutorial

Hadoop is developed for processing huge volume of data. Commodity hardware is the low-end hardware; they are cheap devices which are very economical. Hence, Hadoop is very economic. Hadoop can be setup on a single machine pseudo-distributed mode , but it shows its real power with a cluster of machines.

We can scale it to thousand nodes on the fly ie, without any downtime. Therefore, we need not to make the system down to add more nodes in the cluster. Follow this guide to learn Hadoop installation on a multi-node cluster.

HDFS is the most reliable storage system on the planet. MapReduce is the distributed processing framework, which processes the data at lightning fast speed. Apache Hadoop is not only a storage system but is a platform for data storage as well as processing. It is scalable as we can add more nodes on the fly , Fault tolerant Even if nodes go down, data is processed by another node. It is not bounded by a single schema.

Its scale-out architecture divides workloads across many nodes. Another added advantage is that its flexible file-system eliminates ETL bottlenecks. Apart from this its open-source nature guards against vendor lock. After understanding what is Apache Hadoop, let us now understand the Hadoop Architecture in detail. Hadoop works in master-slave fashion. There are master nodes very few and n numbers of slave nodes where n can be s. Master manages, maintains and monitors the slaves while slaves are the actual worker nodes.

In Hadoop architecture the Master should be deployed on a good hardware, not just commodity hardware. As it is the centerpiece of Hadoop cluster. Master stores the metadata data about data while slaves are the nodes which store the actual data distributedly in the cluster.

The client connects with master node to perform any task. Now in this Hadoop tutorial, we will discuss different components of Hadoop in detail. Let us discuss them one by one: 5. On all the slaves a daemon called datanode run for HDFS.

75+ Best Free Hadoop Tutorials PDF & eBooks To Learn

Hence slaves are also called as datanode. Namenode stores meta-data and manages the datanodes. On the other hand, Datanodes stores the data and do the actual task. HDFS is a highly fault tolerant, distributed, reliable and scalable file system for data storage. HDFS is developed to handle huge volumes of data.

The file size expected is in the range of GBs to TBs. A file is split up into blocks default MB and stored distributedly across multiple machines. These blocks replicate as per the replication factor. Today Terabytes and Petabytes of data is being generated, captured, processed, stored, and managed. When do we say we are dealing with Big Data? For some people 1TB might seem big, for others 10TB might be big, for others GB might be big, and something else for others.

Big Data Ebooks - PDF Drive

This term is qualitative and it cannot really be quantified. Hence we identify Big Data by a few characteristics which are specific to Big Data. Volume refers to the size of data that we are working with. With the advancement of technology and with the invention of social media, the amount of data is growing very rapidly. This data is spread across different places, in different formats, in large volumes ranging from Gigabytes to Terabytes, Petabytes, and even more.

Today, the data is not only generated by humans, but large amounts of data is being generated by machines and it surpasses human generated data.

This size aspect of data is referred to as Volume in the Big Data world. Velocity refers to the speed at which the data is being generated. In different fields and different areas of technology, we see data getting generated at different speeds. This speed aspect of data generation is referred to as Velocity in the Big Data world.

Apart from the traditional flat files, spreadsheets, relational databases etc. This aspect of varied data formats is referred to as Variety in the Big Data world. Just like the data storage formats have evolved, the sources of data have also evolved and are ever expanding.

There is a need for storing the data into a wide variety of formats. With the evolution and advancement of technology, the amount of data that is being generated is ever increasing. Sources of Big Data can be broadly classified into six different categories as shown below. There are large volumes of data in enterprises in different formats. This data that is spread across the organization in different formats is referred to as Enterprise Data. Every enterprise has some kind of applications which involve performing different kinds of transactions like Web Applications, Mobile Applications, CRM Systems, and many more.

To support the transactions in these applications, there are usually one or more relational databases as a backend infrastructure. This is mostly structured data and is referred to as Transactional Data. This is self-explanatory. There is a large amount of data getting generated on social networks like Twitter, Facebook, etc. This category of data source is referred to as Social Media. There is a large amount of data being generated by machines which surpasses the data volume generated by humans.

These include data from medical devices, censor data, surveillance videos, satellites, cell phone towers, industrial machinery, and other data generated mostly by machines. These types of data are referred to as Activity Generated data.

This data includes data that is publicly available like data published by governments, research data published by research institutes, data from weather and meteorological departments, census data, Wikipedia, sample open source data feeds, and other data which is freely available to the public.

Open source project means it is freely available and we can even change its source code as per the requirements. If certain functionality does not fulfill your need then you can change it. It provides an efficient framework for running jobs on multiple nodes of clusters. Cluster means a group of systems connected via LAN.

Apache Hadoop provides distributed processing of data as it works on multiple machines simultaneously. By getting inspiration from Google, which has written a paper about the technologies it is using technologies like Map-Reduce programming model as well as its file system GFS. Hadoop was originally written for the Nutch search engine project. When Doug Cutting and his team were working on it, very soon Hadoop became a top-level project due to its huge popularity. Apache Hadoop is an open source framework written in Java.

The basic Hadoop programming language is Java, but this does not mean you can code only in Java. Hadoop efficiently processes large volumes of data on a cluster of commodity hardware. Hadoop is developed for processing huge volume of data. Commodity hardware is the low-end hardware; they are cheap devices which are very economical. Hence, Hadoop is very economic. Hadoop can be setup on a single machine pseudo-distributed mode , but it shows its real power with a cluster of machines.

We can scale it to thousand nodes on the fly ie, without any downtime. Therefore, we need not to make the system down to add more nodes in the cluster. Follow this guide to learn Hadoop installation on a multi-node cluster. HDFS is the most reliable storage system on the planet. MapReduce is the distributed processing framework, which processes the data at lightning fast speed. Apache Hadoop is not only a storage system but is a platform for data storage as well as processing. It is scalable as we can add more nodes on the fly , Fault tolerant Even if nodes go down, data is processed by another node.

It is not bounded by a single schema. Its scale-out architecture divides workloads across many nodes. Another added advantage is that its flexible file-system eliminates ETL bottlenecks. Apart from this its open-source nature guards against vendor lock.

After understanding what is Apache Hadoop, let us now understand the Hadoop Architecture in detail. Hadoop works in master-slave fashion. There are master nodes very few and n numbers of slave nodes where n can be s. Master manages, maintains and monitors the slaves while slaves are the actual worker nodes.

In Hadoop architecture the Master should be deployed on a good hardware, not just commodity hardware.

As it is the centerpiece of Hadoop cluster. Master stores the metadata data about data while slaves are the nodes which store the actual data distributedly in the cluster. The client connects with master node to perform any task.

Now in this Hadoop tutorial, we will discuss different components of Hadoop in detail. Let us discuss them one by one: 5.

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