Components of Hadoop Architecture & Frameworks used for Data Science

Data Science | Hadoop   |   
Published January 22, 2018   |   

Every business now recognizes the power of Big Data Analytics in developing deep actionable insights to enjoy business advantages. However, unlike before when businesses were required to deal with gigabytes of data, the present scenario requires to store and process huge piles of data that is measured in petabytes and terabytes as it is produced by rapidly growing internet population, systems, and enterprises. But, as we all have learned all the years growing up, no problem lasts forever in the technology world. Likewise, Hadoop Analytics is one such tech solution that brings an end to all your big data analytics concerns.
Hadoop is an open-source framework that lets an organization to process huge datasets parallelly. Hadoop was designed keeping in mind that system failures are a common phenomenon, therefore it is capable of handling most failures. Besides, Hadoop’s architecture is scalable, which allows a business to add more machines in the event of a sudden rise in processing-capacity demands.

Basic Components of Hadoop Architecture

Hadoop Distributed File System (HDFS) : HDFS is the distributed storage system that is designed to provide high-performance access to data across multiple nodes in a cluster. HDFS is capable of storing huge amounts of data that is 100+ terabytes in size and streaming it at high bandwidth to big data analytics applications.
MapReduce: MapReduce is a programming model that enables distributed processing of large data sets on compute clusters of commodity hardware. Hadoop MapReduce first performs mapping which involves splitting a large file into pieces to make another set of data.
After mapping comes the reducing task, which takes the output from mapping and assemble the results into a consumable solution. Hadoop can run MapReduce programs written in many languages, like Java, Ruby, Python, and C++. Owing to the parallel nature of MapReduce programs, Hadoop easily facilitates large-scale data analysis using multiple machines in the cluster.
YARN: Yet Another Resource Negotiator or YARN is a large-scale, distributed operating system for big data applications. YARN is considered to be the next generation of Hadoop’s computing platform. It brings on the table a clustering platform that helps manage resources and schedule tasks. YARN was designed to set up both global and application-specific resource management components. YARN improves utilization over more static MapReduce rules, that were rendered in early versions of Hadoop, through dynamic allocation of cluster resources.
Every business has different data analytics requirements, which is why the Hadoop ecosystem offers various open-source frameworks to fit your special data analytics needs. Let’s check out below!

Apache Hadoop Frameworks

1. Hive

Hive is an open-source data warehousing framework that structures and queries data using a SQL-like language called HiveQL. Hadoop allows developers to write complex MapReduce applications over structured data in a distributed system. If a developer can’t express a logic using HiveQL, Hadoop allows choosing traditional map/reduce programmers to plug in their custom mappers and reducers. Hive is a very good relational-database framework and can accelerate queries using indexing feature.

2. Ambari

Ambari was designed to remove complexities of Hadoop management by providing a simple web interface that can provision, manage and monitor Apache Hadoop clusters. Ambari, which is an open-source platform, makes it simple to automate cluster operations via an intuitive Web UI as well as a robust REST API.
Ambari’s Core Benefits:

  • Simplified Installation, Configuration and Management
  • Centralized Security Setup
  • Full Visibility into Cluster Health
  • Highly Extensible and Customizable

3. HBase

HBase is an open-source, distributed, versioned, non-relational database model that provides random, real-time read/write access to your big data. HBase is a NoSQL Database for Hadoop. It’s a great framework for businesses that have to deal with multi-structured or sparse data. HBase makes it possible to push the boundaries of Hadoop that runs processes in batch and doesn’t allow for modification. With HBase, you can modify data in real-time without leaving the HDFS environment.
HBase is a perfect fit for the type of data that fall into a big table. HBase first performs the task of storing and searching billions of rows and millions of columns. It then shares the table across multiple nodes, paving the way for MapReduce jobs to run locally.

4. Pig

Pig is an open-source technology that enables cost-effective storage and processing of large data sets, without requiring any specific formats. Pig is a high-level platform and uses Pig Latin language for expressing data analysis programs. Pig also features a compiler that creates sequences of MapReduce programs.
The framework processes very large data sets across hundreds to thousands of computing nodes, which makes it amenable to substantial parallelization. In simple words, we can consider Pig as a high-level mechanism that is suitable for executing MapReduce jobs on Hadoop clusters using parallel programming.

5. ZooKeeper

ZooKeeper is an open-source platform that offers a centralized infrastructure for maintaining configuration information, naming, providing distributed synchronization, and providing group services. The need of a centralized management arises when a Hadoop cluster spans 500 or more commodity servers, which is why Zookeeper has become so popular.
ZooKeeper also avoids the single point of failure situation as it replicates data over a set of hosts, and the servers are in sync with each other. Although Java and C are currently used for ZooKeeper applications, Python, Perl, and REST interfaces could also be used someday for ZooKeeper applications.
Apache Hadoop is a great platform for big data analytics and there are various other technologies, like NOSQL, Avro, Oozie, and Sqoop, available in the tech market that makes Hadoop ecosystem very versatile. You must carefully assess big data analytics needs of your business before jumping on a Hadoop technology so that you get the best results. Hadoop big data analytics has already helped many businesses to touch new heights, you can also help your business grow by using Hadoop analytics for data science.