11 Hadoop Tools for Big Data
Big Data Analytics is being observed as the most crucial task for every organization with the data exploding from digital media.
19:48 08 December 2020
Today, the world is getting flooded with Big Data technologies.
The need for storing and analyzing large sets of unstructured data resulted in the development of an open-source data framework which is popularly known as Apache Hadoop.
A recent press release from Marketwatch states that the Hadoop market is anticipated to grow significantly between 2019 and 2025. Eventually, there is a surge in Hadoop jobs as well.
According to Allied Market Research, the global market of Hadoop is expected to reach USD 84.6 billion by 2021. Also, it will create 1.4 to 1.9 million jobs for Hadoop data analysts in the US alone.
So, what if you have a Hadoop course and get Hadoop certification?
You will be at the top of the list of preferred candidates for this domain.
Let us now learn about what Hadoop is and the top ten Hadoop tools you should know well if you wish to enter into the world of Hadoop.
What is Apache Hadoop?
Put simply, Apache Hadoop is a framework or platform for solving Big Data issues. It is not a programming language or a service. It involves various tasks required for data analytics such as ingestion, storage, analysis, and maintenance of huge chunks of data that are generated every second across the globe.
Hadoop framework is developed in Java and is an open-source platform primarily used for storing and analyzing large data sets.
Top X Hadoop Tools you Should Master
HDFS or Hadoop Distributed File System is the backbone of the Hadoop Ecosystem. It is considered to be the core component of Hadoop which is designed to store a massive amount of data that may be structured, semi-structured, or even unstructured.
HDFS has two components namely NameNode and DataNode. It helps in storing data across different nodes and maintains the reference or log file related to the stored data. The NameNode contains a reference to your data and DataNode contains the actual data.
Just as the CPU is the brain of a computer, YARN serves as the brain of the Hadoop framework. It takes care of all your activities related to processing by resource allocation and task scheduling.
The two main components of YARN are ResourceManager and NodeManager. The primary responsibility of the two components is to process requests and execute the task on every DataNode.
- Apache HIVE
Apache Hive is a data warehouse of the Hadoop framework. It performs reading, writing, and managing huge datasets in a distributed environment. While working on HIVE you feel at home as it is for people who are SQL masters.
Hive supports batch query processing and interactive query processing as well, eventually performing highly scalable operations.
It allows you to manipulate dates, numbers, strings, and other attributes.
NoSQL or Not Only Structured Query Language is required because most of the data is unstructured. The main reason for NoSQL is one of the widely supported unstructured query languages is that it can easily be integrated with Oracle Wallet, Oracle Database, and Hadoop.
NoSQL contains primary key pairs along with secondary indexes.
- Apache MapReduce
MapReduce provides the logic of processing hence it is considered as a core component of processing in the Hadoop framework. It is a framework that allows developers to write applications that can process huge data sets using distributed and parallel algorithms within the Hadoop environment.
The two main components of MapReduce are JobTracker and TaskTracker. To keep track of all the jobs there is a single JobTracker, and TaskTracker is there for every cluster node which monitors all the jobs.
- Apache PIG
Initially developed by Yahoo, PIG is a powerful tool for candidates who are not from a programming background. Wow!
You can write your application in pig Latin and the compiler will convert it into MapReduce code.
It provides you with a framework where you can build data flow for ETL (Extract, Transform, Load) processing and analyzing massive data sets.
- Apache Mahout
A library of different machine learning algorithms is developed by Apache which is known as Mahout. Mahout, acclaimed for machine learning, provides you with an environment where you can create machine learning applications that are scalable.
The main functions performed by Mahout are Collaborative Filtering, Clustering, Classification, and Frequent Itemset missing.
There are inbuilt algorithms you can utilize for different use cases and provide a command line to invoke various algorithms.
- Apache Spark
It is impossible to discuss Hadoop without Spark. Simply put, it is a framework for real-time data analytics for use in a distributed computing environment. To enhance the speed of data processing over MapReduce, it executed in-memory computations.
The most important feature of Spark is that it is 100 times faster than Hadoop for large scale data processing by executing in-memory computations and also other optimizations.
Spark works by loading all the data into clusters of memory which allows the program to query it again and again, thereby making it the best-suited framework for Al and Machine Learning.
- Apache Flume
When it comes to ingesting the data, it is Apache Flume that comes into action. Flume is renowned for ingesting semi-structured and unstructured data into HDFS.
It gives you a reliable and distributed solution to help in the collection, aggregation and moving of huge amounts of data sets.
It assists in ingesting online streaming data from different sources like social media, network traffic, log files, email messages, and more to HDFS.
It uses a simple, extensible data model which allows you to implement online analytic applications easily.
- Apache Drill
As the name itself implies, to drill into any kind of data you need Apache Drill. It is an open-source framework to work with a distributed environment and helps you in analyzing large data sets.
The most crucial feature of Drill is that it supports different kinds of NoSQL databases and file systems that may include Google Cloud Storage, MongoDB, MapR-DB HDFS, Amazon S3, NAS, Swift, MapR-FS, Azure Blob Storage, and other local files.
There are different providers that provide powerful tools that work on Hadoop. They are intended to ease the development of solutions on the same. This has increased the demand for Hadoop developers in almost every domain. If you are willing to make a career in the same, there are online training courses that help you in mastering these tools.
You can have flexible learning hours and the mode of learning of your preference.
Get yourself registered now!