Previously, we gave you an overview of big data and Hadoop technology. Now we are going to share Hadoop tutorial for beginners in pdf format consisting of most of the broader specifications. Hadoop is the well-known technology that is used for Big Data. Hadoop is an open source software framework that is used for storing data and running apps on the clusters of commodity hardware. It can provide you with massive storage for every type of data, huge processing power and potential to handle limitless concurrent tasks and jobs.
Hadoop contains many tools in it, but the two core parts of Hadoop are:
- Hadoop HDFS (Hadoop Distributed File System): It is a virtual file system which mostly looks like any other file system except when you move a file on HDFS. Then this file is split into multiple small files, each of these files are then replicated and is stored on 3 servers for the fault tolerance constraints.
- MapReduce Hadoop: It is a technique to split every request coming into smaller requests which are then sent to many small servers, that allows the true scalable use of the CPU power.
Hadoop Tutorial pdf for beginner
The tutorials that are provided here consist of the detailed data of what is Hadoop and how it’s solving the most severe issues of data management. The pdf is provided in parts including big data overview, Hadoop introduction, Hadoop Distributed File System Overview, Hadoop MapReduce and more.
- Big Data Definition
Here in this part, you will get to know what is big data, big data technologies and what are benefits of big data for all. In simple terms, big data can be defined as the large amount of amount which can be both structured and unstructured. But what we focus on is not the amount of data but only data that matters for the organizations. Big data can improve the decision-making process and strategic business moves. Big Data can be easily described by the 3Vs that are Volume, Velocity, and Variety.
- Volume: It is the collection of data from various sources such as social media, business transactions, machine to machine data and much more as the data sources is vast. In past, data storage was a major issue but new technologies like Hadoop reduced this problem too.
- Velocity: As the data is increasing day by day so it should be dealt in a timely manner.
- Variety: This refers to the data formats, as the data from the different sources can be of any formats like email, audio, video, financial transactions and unstructured text documents.
So here we are going to provide you Big Data Hadoop tutorial which will be very beneficial for all of you to understand the technologies in detail.
Big Data Importance
Big data is very important as most of the major industries have already started using it in their work. Data can be from any of the sources which are then analyzed which enables the cost reduction, smart decision making, time reductions and new product development.
Big Data can unlock the significant values by making all the information transparent: Still, there is a large amount of data that is not available in the digital form which becomes difficult to access and search through networks. So it increases the efforts to search for data and then transfer it to another location which reduces the efficiency. Big Data is used in most of the important industries such as Healthcare, education, transportation, Communication media and entertainment etc.
With the applications of big data, there are also many challenges which are faced in these sectors.
What is Hadoop Technology?
It is an open source framework which allows big data to get stored and also process in a distributed environment across the cluster of computers by using the programming models. It is basically designed so that to scale up from the single servers to a larger number of machines each of them offering local computation and storage.
Hadoop architecture or framework includes 4 modules in it.
- Hadoop Common: This is the java library and utilities which are required by the other Hadoop modules. These libraries provide the filesystem and OS level abstractions and consist the important java files and script that are required to start Hadoop.
- Hadoop YARN: Hadoop yarn is the framework for the cluster resource management and job scheduling.
- HDFS: Hadoop Distributed File System is a distributed file system which provides the high throughput access to application data.
- Hadoop MapReduce: This is the YARN-based system for parallel processing of large data sets.
Hadoop Distributed File System is based on GFS (Google File System) and provides a distributed file system that is designed to run on large clusters of the small computer machines in a reliable and fault-tolerant manner. It also allows rapid transfer of data between compute nodes that enable Hadoop System to continue running if any of the nodes fails.
HDFS breaks the data into smaller parts which are provided to it as input and then distributes it to various nodes in the cluster which allow for the parallel processing of data. Each piece of data is copied multiple times by the file system and then the data is distributed to individual nodes and also placing at least one copy on different server rack from all others. HDFS is usually built to support the applications having large data sets, including the files which reach into terabytes. It basically uses the Master/ slave architecture.
Hadoop MapReduce Tutorial
It is a framework by using which we can write the applications so that to process a large amount of data, parallel on large clusters of commodity hardware in a very reliable manner. MapReduce can also be referred as the processing technique and program model for the distributed computing that is based on java. As the name suggest, MapReduce contains two important tasks into algorithms that are Map and Reduce. The map takes and converts a set of data into key/ tuples. Reduce takes the output of Map as input and combines all the data keys into smaller sets.
The advantage of MapReduce is that it becomes easy to scale the data processing over various computing nodes. MarReduce program is executed into 3 stages named as Map stage, Shuffle Stage, and Reduce stage.
How does Hadoop Work?
Stage 1: A user or application can submit a job to Hadoop for the required process by specifying these items:
- Location of the input as well as the output in distributed file system.
- Job configurations by setting the different parameters that are specific to the job.
Stage 2: The job and configuration are then submitted to JobTracker by the Hadoop job client. The Jobtracker then assumes itself the responsibility of distributing software to slaves, scheduling the tasks and then monitoring them and providing the diagnostic information and status to job client.
Stage 3: The Task trackers on the different nodes executes the task according to MapReduce implementation and output of reduce function is then reduced into output files on file system.
Advantages of using Hadoop
- Hadoop does not depend on the hardware to give fault-tolerance and FTHA, rather its library itself is designed to detect and handle the failures at the application level.
- One of the major advantages of Hadoop is that it is open source and is compatible with all the platforms as it is Java based.
- Hadoop framework allows its user to easily write and test distributed systems. It automatically distributes the data and work across the machines and utilizes the underlying parallelism of CPU cores. It is very efficient.
We hope that the Big Data Hadoop pdf tutorial provided here will be useful for you. Also you can easily save the pdf by hadoop pdf download, opton for download is also provided. There is still a lot to learn about Big Data and Hadoop, we will be updating about each and every aspect in details in our further posts.