In this article, you will get to know about the need of big data Hadoop in Information Technologies and to understand this, you must know the difference between the traditional data and big data first.
In earlier days, when the usage of internet is limited, the data to be analyzed, processed and stored that includes documents, personnel files, finances, stock records etc. requires basic featured tools and software.
But with the advent of newer technologies and higher usage of internet, the term ‘data’ has converted into ‘big data’ that includes audio/ video, images, 3D models, location data, simulations etc. This big data can be structured, semi-structured and unstructured.
Now, what happening is, every day, a flood of structured and unstructured data is getting dumped into the machines. So it becomes a major challenge for organizations to handle this huge amount of data which is mostly the unstructured data(about 80%).
The resources from where the flood of data is coming are signing up of new users into various social network profiles, logging, and tracking, internet archive stores, data warehouse appliances, information of users stored on the web, etc. You may join Our Hadoop training program to get a starter with learning Hadoop technology.
Some of the big data is in terabytes(TB) and some are in Petabytes(PB). In order to process and store this huge amount of data into the computer clusters, there exists an undeniable need of Hadoop in the Information Technology.
Major Challenges In Big Data Processing And Storage
We have described below the major challenges that many organizations come across regarding the processing and storage of large data sets across computer clusters.
Big Data Challenge 1- Risk of Losing the Data and Hardware Failure
There is always a risk of losing the data due to machine or hardware failure. But Hadoop being fault tolerant removes the fear of losing the data.
When a user tries to store a file in HDFS(Hadoop Distributed File System), it goes through a replication process in which HDFS creates three replicas(by default) of the file on the other machines present in the HDFS cluster.
If in case, one of the machines in the cluster fails, the file stored on that machine can be easily recovered from other machines as a result of the file replication.
Big Data Challenge 2- Data Security and Privacy
This is one of the most important challenges with big data. The sensitive information, for example, personal information of a person stored in the database of a social networking website, is always vulnerable to be leaked while processing and storing large data sets.
Big Data Challenge 3- Data Transfer Rates/ Data Velocity
Data velocity or Data Transfer Rate means the speed at which data is transferred from the end user to server or vice versa.
This is the another challenge with the big data. Due to unlimited requests from the end users over mobile phones, laptops or other devices, it has become a big challenge to achieve real time data streaming.
Big Data Challenge 4- Data Volume
Data Volume means, the quantity of data to be analyzed. It is difficult to analyze a large amount of data and needs higher processing speeds which ultimately results in the higher costs.
Big Data Challenge 5- Data Variety
Data Variety means the type of data that is being processed, stored and analyzed. It can be video/ audio files, location data(i.e., coordinates), images etc.
To make these large amounts of unstructured data easily readable to the users accessing it, some feasible sorting techniques should be needed which is again a big data challenge.
Big Data Challenge 6- Data Quality
Whenever a large amount of data is stored, it becomes necessary to concentrate on the quality of data so that it can be used for further research and development.
Big Data Challenge 7- Data Veracity
Data Veracity means, whenever a user is accessing the data, it should be accurate and easily accessible. Apache Hadoop helps to achieve this big data challenge very effectively.
It replicates the files on different machines present in an HDFS cluster thus making it easily accessible irrespective of the failure of one of the machines in the cluster.
Big Data Challenge 8- Scalability
To process and store large volumes of data, the big data tools or software should be scalable.
It means whenever there comes need to process a large amount of data, one can easily add resources or hardware like RAM, CPU etc to the nodes in the cluster.
Big Data Challenge 9- Need Of Data Analysts And Data Scientists
This is another important big data challenge. With the growth of big data technologies, organizations need highly skilled Data Analytics and Data Scientists to handle big data problems.
So it is very important for the organizations and other training institutions to produce highly skilled big data professionals through their training programs.
Need Of Big Data Hadoop
In order to meet the big data challenges, it is essentially required to develop a software that has the capability to analyze, process and store large data sets present on different machines at different locations in a very quick time and cost effective manner.
Hadoop has all the abilities to handle such big data challenges. It uses MapReduce programming model which deliberately divides large volumes of data into smaller independent tasks and process them in parallel.
Hadoop Is Used By A Large Number Of Organizations For The Following Purposes
- Log Processing/Analysis- Facebook, Yahoo, Last.FM, Netseer etc are using Hadoop for log processing.
- Data Warehousing- AOL, Facebook etc.
- Video/ Image Analysis– Eyealike, New York Times etc.
- Search– Amazon, Yahoo, Zvents, etc.
- Clickstream Analytics– Adknowledge.
- Machine Learning and ETL– InMobi, AOL, etc.
Looking at all the big data challenges that several organizations come across, there is a substantial need for Hadoop technology in the information technology.
It is far better than traditional data processing and storage techniques such as RDBMS(Relational Database Management System). It has the ability to process large data sets across computer clusters which cannot be done with traditional RDBMS.
Apache Hadoop comes with several other projects such as Hive, HBase, Mahout, Cassandra, Pig, Zookeeper, Yarn, Avro, Thrift, Sqoop, Flume, Ambari, Drill, HCatalog, and Oozie.
These tools and frameworks make Hadoop a powerful software framework to work on leaving behind the minimum amount of user repudiation.