INTRODUCTION

Organizations these days operate with versatile functions like design, marketing, sales, customer service, etc. All of these functions involve a huge amount of data, and such a huge amount of data in return requires huge and well-maintained storage. This is where the need for Hadoop tools arises. Hadoop is a reservoir of all the sources of data of an organization in one place.

In this article let us look at:

  1. Advantages Of Hadoop Tools
  2. List of Tools

1. Advantages Of Hadoop Tools

Some of the advantages of using Hadoop tools are :

  • Data sharing and Data reliability

Each function in an organization is dependent on another function, and hence sharing data in a secure manner becomes quite critical. With Hadoop ecosystem tools, all information is stored in one place, which enables easy data sharing between various departments. Also, the security feature ensures data shared doesn’t lose its original form or structure.

  • Cost-efficient and Reliable storage

Hadoop, with its low-cost storage ability, is favorable for both big and small organizations. The Hadoop tools not only offer the ability to store high volume data, including the raw data, but they also offer the ability to do so at an affordable cost in a secure environment. Also, since Hadoop an open-source project, it doesn’t need any kind of licensing. Thus, companies can save hundreds of pounds with this.

  • Highly Scalable

When we say Hadoop is highly scalable, we mean, unlike traditional systems, if needed, we can easily increase the data storage by adding nodes or by increasing the capacity of such nodes. Since data are produced in seconds by different functions, this scalability feature ensures no data gets wasted.

  • Fault-tolerance ability

This is one of the major advantages of using Hadoop tools. Fault tolerance ability means creating a copy of each block of data on another server so that if any server goes down, then we can easily access the data from other servers. It is also called a replication mechanism, i.e. replicating the data on another machine.

2. List of Tools

Hadoop ecosystem comprises different components that work individually and in coordination with each other to provide solutions to crunching big data in the system. Following is the list of Hadoop related tools:

  • Hadoop Distributed File System (HDFS)

This core component of Hadoop enables storage of any size and any kind of data, be it structured, unstructured, or semi-structured and maintaining a log file of the same for easy access. This is one of the important tools of Hadoop since, in traditional systems organizations were not able to process the unstructured data.

  • HIVE

One of the software of the apache Hadoop tools, HIVE, assists Hadoop in managing large data sets and structuring the unstructured data sets with ease. With HIVE, a user can store data in different formats.HIVE also offers SQL language known as HiveQL to query any such data if needed.

  • Pig

One of the important Hadoop ecosystem tools enables the simplification of a complex data structure comprising of high-level languages by providing standard functions. This helps users who don’t have a programming background. It also allows the users to create their own languages through a user-defined function when there is no standard function for the same or if they want to do specified processing.

  • Sqoop

This Hadoop tool is designed to import or export bulk data to HDFS from enterprise data warehouses or relational databases, or vice-versa.

  • ZooKeeper

This tool is basically a combination of various other tools in the Hadoop ecosystem. It coordinates with other services, which enable easy synchronization of data. It not only provides synchronization of data but also ensuring the original format as well as grouping and naming the bulk data. This leads to a lot of time-saving.

  • Hbase

This is a NoSQL database that supports random read and writes. To enable faster reading, this tool stores and organizes its data in a column format. You don’t need special language for this tool. In fact, you can easily access it with Java, API, Avro, etc.

  • Mahout

Mahout in Hindi means the keeper of the elephant. Here, the elephant would be Hadoop. Mahout is the combination of popular machine learning algorithms applied to the Hadoop ecosystem to implement and process the scalability of huge data.

  • Lucene

Compatible with almost all applications that require a search in full text, one of the powerful Hadoop tools, it provides information search and retrieval at a very high speed. In other words, it is a high-performance, efficient search library that provides results in sub-seconds.

  • Avro

This Hadoop tool allows the remote transfer of data and also storage of large sets of data. Avro stores data in a row-oriented format. With its small binary format, it is one of the best data serialization Hadoop tools out there.

  • Ambari

This software allows monitoring the health status of other Hadoop ecosystem tools by implementing a dashboard software. It also provides easy updation of the clusters while they are running. It is like supporting software to enhance the functionality of the Hadoop tools. Alternatively, we can call it one of the Hadoop monitoring tools.

CONCLUSION

After reading the above article, you have a basic understanding of essential Hadoop tools which help Hadoop in offering a cost-effective and efficient way of handling large data sets. Hadoop has grown so much in popularity in the last years that everyone from big giants like Google and IBM in the industry to small marketers is using this tool. To conclude, we would just like to say that Hadoop has definitely changed the face of the IT world, and its demand will only increase in the coming few years.

Big data analysts are at the vanguard of the journey towards an ever more data-centric world. Being powerful intellectual resources, companies are going the extra mile to hire and retain them. You too can come on board, and take this journey with our Big Data Specialization course.

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