Introduction to Advantages of Hadoop

Hadoop is the big data processing paradigm can effectively handle the challenges of the big data (like Variety, Volume, and Velocity of Data) as it has the property of distributed storage, parallel processing, due to which it has multiple advantages like open source, Scalable, Fault-Tolerant, Schema Independent, High Throughput and low latency, Data Locality, Performance, Share Nothing Architecture, Support Multiple Language, Cost-Effective, Abstractions, Compatibility and Support for Various file system.

What is Hadoop?

Hadoop is a big data processing paradigm that provides a reliable, scalable place for data storage and processing. Hadoop was created by Doug Cutting and he is considered as “Father of Hadoop”. Hadoop was the name of his son’s toy elephant. Hadoop had its roots in Nutch Search Engine Project. Hadoop is a processing framework that brought tremendous changes in the way we process the data, the way we store the data. Compared to traditional processing tools like RDBMS, Hadoop proved that we can efficiently combat the challenges of Big data like,

Variety of Data: Hadoop can store and process structured as well as semi-structured and unstructured formats of data.

The Volume of Data: Hadoop is specially designed to handle the huge volume of data in the range of petabytes.

The Velocity of Data: Hadoop can process petabytes of data with high velocity compared to other processing tools like RDBMS i.e. processing time in Hadoop is very less.

Salient Features of Hadoop

Hadoop is open-source in nature.
It works on a cluster of machines. The size of cluster depends on requirements.
It can run on normal commodity hardware.

Advantages of Hadoop

In this section, the Advantages of Hadoop are discussed. Now let us take a look at them one by one:

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1. Open Source

Hadoop is open-source in nature, i.e. its source code is freely available. We can modify source code as per our business requirements. Even proprietary versions of Hadoop like Cloudera and Horton works are also available.

2. Scalable

Hadoop works on the cluster of Machines. Hadoop is highly scalable. We can increase the size of our cluster by adding new nodes as per requirement without any downtime. This way of adding new machines to the cluster is known as Horizontal Scaling, whereas increasing components like doubling hard disk and RAM is known as Vertical Scaling.

3. Fault-Tolerant

Fault Tolerance is the salient feature of Hadoop. By default, each and every block in HDFS has a Replication factor of 3. For every data block, HDFS creates two more copies and stores them in a different location in the cluster. If any block goes missing due to machine failure, we still have two more copies of the same block and those are used. In this way, Fault Tolerance is achieved in Hadoop.

4. Schema Independent

Hadoop can work on different types of data. It is flexible enough to store various formats of data and can work on both data with schema (structured) and schema-less data (unstructured).

5. High Throughput and Low Latency

Throughput means amount work of done per unit time and Low latency means to process the data with no delay or less delay. As Hadoop is driven by the principle of distributed storage and parallel processing, Processing is done simultaneously on each block of data and independent of each other. Also, instead of moving data, code is moved to data in the cluster. These two contribute to High Throughput and Low Latency.

6. Data Locality

Hadoop works on the principle of “Move the code, not data”. In Hadoop, Data remains Stationary and for processing of data, code is moved to data in the form of tasks, this is known as Data Locality. As we are dealing with data in the range of petabytes, it becomes both difficult and expensive to move the data across Network, Data locality ensures that Data movement in the cluster is minimum.

7. Performance

In Legacy systems like RDBMS, data is processed sequentially but in Hadoop processing starts on all the blocks at once thereby providing Parallel Processing. Due to Parallel processing techniques, the Performance of Hadoop is much higher than Legacy systems like RDBMS. In 2008, Hadoop even defeated the Fastest Supercomputer present at that time.

8. Share Nothing Architecture

Every node in the Hadoop cluster is independent of each other. They don’t share resources or storage, this architecture is known as Share Nothing Architecture (SN). If a node in the cluster fails, it won’t bring down the whole cluster as each and every node act independently thus eliminating a Single point of failure.

9. Support for Multiple Languages

Although Hadoop was mostly developed in Java, it extends support for other languages like Python, Ruby, Perl, and Groovy.

10. Cost-Effective

Hadoop is very Economical in nature. We can build a Hadoop Cluster using normal commodity Hardware, thereby reducing hardware costs. According to the Cloud era, Data Management costs of Hadoop i.e. both hardware and software and other expenses are very minimal when compared to Traditional ETL systems.

11. Abstraction

Hadoop provides Abstraction at various levels. It makes the job easier for developers. A big file is broken into blocks of the same size and stored at different locations of the cluster. While creating the map-reduce task, we need to worry about the location of blocks. We give a complete file as input and the Hadoop framework takes care of the processing of various blocks of data that are at different locations. Hive is a part of the Hadoop Ecosystem and it is an abstraction on top of Hadoop. As Map-Reduce tasks are written in Java, SQL Developers across the globe were unable to take advantage of Map Reduce. So, Hive is introduced to resolve this issue. We can write SQL like queries on Hive, which in turn triggers Map reduce jobs. So, due to Hive, the SQL community is also able to work on Map Reduce Tasks.

12. Compatibility

In Hadoop, HDFS is the storage layer and Map Reduce is the processing Engine. But, there is no rigid rule that Map Reduce should be default Processing Engine. New Processing Frameworks like Apache Spark and Apache Flink use HDFS as a storage system. Even in Hive also we can change our Execution Engine to Apache Tez or Apache Spark as per our Requirement. Apache HBase, which is NoSQL Columnar Database, uses HDFS for the Storage layer.

13. Support for Various File Systems

Hadoop is very flexible in nature. It can ingest various formats of data like images, videos, files, etc. It can process Structured and Unstructured data as well. Hadoop supports various file systems like JSON, XML, Avro, Parquet, etc.

Working of Hadoop

Below are the points that show how Hadoop works:

1. Distributed Storage and Parallel Processing

This is the driving principle of all the frameworks of the Hadoop Ecosystem including Apache Spark. In order to understand the working of Hadoop and Spark, first, we should understand what is “Distributed Storage and Parallel Processing.”

2. Distributed Storage

Hadoop doesn’t store data in a single Machine, Instead, it breaks that huge data into blocks of equal size which are 256MB by default and stores those blocks in different nodes of a cluster (worker nodes). It stores the metadata of those blocks in the master node. This way of storing the file in distributed locations in a cluster is known as Hadoop distributed File System – HDFS.

3. Parallel Processing

It is a Processing paradigm, where processing is done simultaneously on the blocks of data stored in HDFS. Parallel Processing works on the notion of “Move the code, not data”. Data remains Stationary in HDFS but code is moved to data for processing. In simple terms, if our file is broken into 100 blocks, then 100 copies of the job are created and they travel across the cluster to the location where block resides and processing on 100 blocks starts simultaneously (Map Phase). Output data from all blocks are collected and reduced to final output (Reduce Phase). Map Reduce is considered to be “Heart of Hadoop”.

Conclusion

In this Data age, Hadoop paved the way for a different approach to challenges posed by Big data. When we say, Hadoop we don’t mean Hadoop alone, it includes Hadoop Ecosystem tools like Apache Hive which provides SQL like operations on top of Hadoop, Apache Pig, Apache HBase for Columnar storage database, Apache Spark for in-memory processing and many more. Although Hadoop has its own disadvantages, it is highly adaptable and constantly evolving with each release.