Top Essential Hadoop Ecosystem Tools to Help in Your Big Data Journey
5+ Top Essential Hadoop Ecosystem Tools To Help In Your Big Data Journey

            
Source: Google

Apache Hadoop is an open-source framework used to efficiently store and process large data sets ranging in size from gigabytes to petabytes of data. Rather than using one large computer to store and process your data, Hadoop allows you to group many computers together to analyze massive data sets in parallel more quickly.

According to Globalnewswire, the Global Hadoop Market will reach USD 87.14 billion by 2022. 

Applications built with HADOOP run on large data sets spread across groups of basic computers. Basic computers are cheap and widely available. These are mainly useful to achieve higher computational power at a low cost.


  
  
  Apache Hadoop is among the best open-source Framework used for data processing, and it possesses various features like:



Hadoop is very suitable for big analytics
Scalability
Fault tolerance


Big Data is becoming famous globally. Companies across all verticals like media, retail, pharmaceuticals, and much more are pursuing this IT concept. 

Big Data Hadoop tools and techniques help companies illustrate the massive amount of data quicker, which helps raise production efficiency and improve new data‐driven products and services. 

Suppose you also have big data to handle for your brand. In that case, you can hire Hadoop developers who can easily manage your data with the help of programming, design, and development of Hadoop applications in the Big Data domain.

Every Framework has few tools that make their working easy, and today we are also here to discuss few tools of Hadoop which can make your journey with Hadoop relatively easy.


  
  
  HDFS


HDFS stands for Hadoop Distributed File System, commonly known as HDFS, designed to store an enormous amount of data, hence, it is quite a lot more efficient than the NTFS (New Type File System) FAT32 File System, which is used in Windows PCs. 

HDFS is used to carter vast chunks of data quickly to applications. Yahoo has been using HDFS to manage above 40 petabytes of data. 


  
  
  HIVE



Source: Google

Apache Hive data warehousing software makes it easy to query and handle large data sets residing on distributed storage. Hive gives a mechanism to project the structure into this data and query the data utilizing a SQL-like programming language known as HiveQL. 

At the same time, this language also provides traditional map/reduction programmers to plug in their custom reducers and mappers when it is inconvenient or incapable of expressing this logic in HiveQL. 

Facebook created Apache HIVE for the ones who have a good understanding of SQL. It becomes easy for them to you HIVE in Hadoop Ecosystem. It has a similar interface to SQL, which helps a lot in reading, writing, and handling large data sets. 

Hive is among the best tools for Hadoop, and if you are looking to develop an application based on HIVE, you can hire Hadoop consulting services.


  
  
  MapReduce


It is the main component of processing in a Hadoop Ecosystem as it gives the logic of processing. MapReduce is a software framework that helps write applications that prepare large data sets using distributed and parallel algorithms inside the Hadoop environment.


Source: Google


  
  
  Few features of MapReduce:



Scalability
Flexibility
Authentication & Security
Fast and cost-effective solution
Simple model of programming
Parallel programming



  
  
  YARN


YARN stands for Yet Another Resource Negotiator, but it's usually related to by the acronym alone; the full name was self-deprecating humor on the part of its developers. 

YARN is responsible for allocating system resources for various applications running on a Hadoop cluster and scheduling tasks to run on different nodes in the cluster.


  
  
  Few features to look for in YARN:



Multi-tenancy
Cluster utilization
Scalability
Compatibility


YARN is considered as the brain of your Hadoop Ecosystem as It functions all processing activities by scheduling tasks and allocating resources. To make it work for your company, you can take Hadoop development services.


  
  
  Apache Spark


Apache Spark is a framework for real-time data analysis in a distributed computing environment. Spark is written in Scala and was initially developed at the University of California, Berkeley.


Source: Google

It runs calculations in memory to increase data processing speed over Map-Reduce.

Apache Spark is 100x faster compared to Hadoop for large-scale data processing by exploiting in-memory calculations and other optimizations. Therefore, it requires more processing power than Map-Reduce.


  
  
  Apache HBase


HBase is an open-source, non-relational distributed database. Or, it can also be known as a NoSQL database. It manages everything inside a Hadoop ecosystem because it supports all types of data. Many companies use HBase a lot as it provides the best Hadoop solutions.


Source:Google

It is created after Google's BigTable, a distributed storage system designed to cope with large data sets. It was designed to operate on top of HDFS and provides BigTable-like skills or capabilities. It gives us a fault-tolerant way of storing sparse data, common in most Big Data use cases.

The HBase is written in Java, whereas its applications can be written in Avro, REST, and Thrift APIs. You can hire dedicated Apache Hadoop programmers for building applications based on Hbase. 


  
  
  Apache Pig


Apache Pig is a platform used for examining big data sets, which consists of a high-level language for expressing data analysis programs and infrastructure for assessing these programs. 

The outstanding property of Pig programs is that their structure is susceptible to substantial parallelization, which in turn allows them to handle massive data sets.

Today, Pig's infrastructure layer consists of a compiler that generates sequences of Map-Reduce programs, for which large-scale parallel implementations already exist (for example, the Hadoop subproject). Pig's language consists of a textual language called Pig Latin.


  
  
  Conclusion


Hadoop is among the best technologies for dealing with big data. And its various tools listed above are among some of the best Hadoop Ecosystem tools, which help analyze big data in different ways. 

Many big companies use these tools to handle their data as the data are very big in numbers, and to manage such numbers, Hadoop tools were made to handle big data. To make use of Hadoop and its tools for your business, you can take Hadoop development services from Hadoop development company. 


          