In 2005, when the Apache Software Foundation released Hadoop, several businesses doubted its potential. However, over the last couple of years, it has evolved as a shiny technology and grown faster than could have been imagined.
But, what made it so popular; what benefits does it come with; what are its challenges; and what features made it stand out?
We cover everything you need to know about Hadoop in this article. So, let’s take a look.
What Is Hadoop?
Hadoop is an open source framework that can be used by any business as a backbone for their big data operations.
It is used to distribute data storage and processing tasks on clusters of commodity hardware which is affordable hardware that can be repurposed to become part of a distributed Hadoop setup and which is easy to obtain.
It comes with a huge storage for all kinds of data, the capability to handle virtually unlimited simultaneous jobs and massive processing power.
Hadoop consists of four modules which perform a specific function essential for a computer system designed for big data analytics.
The four modules are discussed below.
Hadoop Distributed File System
Hadoop Distributed File System (also known as HDFS) is a Java-based scalable system. It stores data on more than one machine without prior organization.
This module offers the tools required for the user’s computer systems to study the stored data under the Hadoop file system.
Yet Another Resource Negotiator
Yet Another Resource Negotiator (also known as YARN) is a resource management framework. It is used for administering and scheduling resource requests from distributed applications.
This module is a programming model for large dataset processing in parallel.
There are many other popular software components that can run alongside or on top of Hadoop, such as:
- – Hive
- – Pig
- – HBase
- – Zookeeper
- – Cassandra
- – Spark
- – Flume
One of the biggest advantages of Hadoop is its ability to store and process a massive amount of data quickly.
The following are some other benefits.
Hadoop programmers coded a high level of fault tolerance that ensures that the application processing and data are safeguarded from hardware failures.
This also ensures that the system does not shut down completely when one or more component fails. If a node goes down, the job automatically gets redirected to other nodes.
The size of your business today may not be the same tomorrow or in the near future. The more you grow, the more data you have to deal with and managing those large datasets could prove to be a tedious task.
Hadoop is a savior here, since it comes with the scalability feature. Just by adding more nodes, you can grow your system easily and the administration task to do this will be meager.
High Computing Power
High computing power is another big benefit that Hadoop offers because it is a distributed computing model and it can thus process data faster. The computing power is directly proportional to the number of computing nodes.
That means that the more nodes you use, the more processing power you will have.
Pricing is a concern for every business across every industry and you would be happy to know that the cost associated with Hadoop is quite low.
The reason for its cost-effectiveness is because the open source framework is free and uses commodity hardware to store large quantities of data.
Traditional databases need you to preprocess data before you store it. But, Hadoop does not have such a need. You can store an unlimited amount of data and make the decision to use the data later. It includes unstructured data, such as images, videos and text.
We have shown you that Hadoop comes with several benefits, but it also brings several challenges.
Let’s take a look at them.
You may find it difficult to find entry-level programmers with good Java skills to be productive with MapReduce.
You may encounter fragmented data security issues, although new tools and technologies are surfacing to deal with this challenge.
MapReduce cannot match all Issues
You will find that MapReduce programming is good for simple problems and information requests, but it is ineffective for interactive and iterative analytical tasks.
It is file sensitive. Nodes do not intercommunicate iterative algorithms and so it needs multiple sort-reduce or map-shuffle phases to complete a task.
As a result, multiple files are created between the several MapReduce phases and these are incompetent for advanced analytic computing.
Full-fledged Data Governance and Management
Hadoop does not come with the full-feature and user-friendly tools for data cleansing, data management, governance and metadata. Moreover, it lacks the tools for data standardization and quality.
The Uses of Hadoop
Hadoop can prove to be useful in several areas, including the following:
- – Implementing web-based recommendation systems, such as Facebook and LinkedIn, is done by showing you the people or jobs you may be interested in
- – Stage massive amounts of raw data for loading into an analytics store or enterprise data warehouse (EDW) for activities, such as advanced analytics, reporting and queries
- – A low-cost solution for storing and combining data, such as social media, machine, click streams, transactional and sensor scientific. This helps to store information that may not currently be critical, but can be analyzed and used later
Some other areas where it can prove to be very useful are:
- – Customer retention
- – Boosting revenue
- – Data structure optimization
- – Advanced analysis
- – Lowering infrastructural expenses by offloading data storage and processing
By now, you must have a basic understanding of Hadoop and how it can benefit your business
However, you must remember that it is not a plug-and-play platform like other smaller-scale data software. You need to have the expertise and proper planning in place to harness the potential of Hadoop.
So, it is advisable to acquire professional support from a Hadoop consultant who have extensive experience in installing and configuring the software and who knows it inside out.
Some of the things to look out for when hiring a Hadoop professional are:
- – Advanced NoSQL knowledge
- – Familiarity with big data ecosystems, such as Hive, Pig and more
- – Experience in processing large sets of structured, unstructured, or semi-structured data
- – A thorough understanding of the different types of software testing methodologies, such as manual testing and automation testing.
So, when do you plan to use Hadoop? Do you have any questions for us? Feel free to drop us a note below and thanks for reading!