Look around yourself and you will find that you are surrounded or circled around Big Data. Big Data is nothing but a humungous collection of data. The basket of Big Data is occupied with structured, unstructured data plus semi-structured data- all having their own importance.
Hadoop is the framework from Apache to ease the Big Data World and helps to process and sort all types of data to make the best use of it.
What is Hadoop?
Hadoop is an open source project of Apache Software Foundation (ASF) to handle enormous heaps of Data which run in clustered systems.
Hadoop handles all sorts of data present till date like: structured, unstructured and semi-structured data (The type of data which does not share its place in relational databases but have few organizational properties that turn it into the analysis form factor; example XML data). Hadoop even handles data warehousing.
Big Data Analytic Tools
If we see and consider the analysis of Big Data then we have a number of tools that do the work of analysis from Apache as well as the third party vendors. Some of them are:
Hadoop outstands all of the above because of its Hadoop Distributed File System acronym as HDFS structure (splits the data and circles around the various nodes in a cluster). To learn more about the Apache Hadoop framework one can go for an online Big Data Hadoop training to get useful insights about data management, thus, enhancing your skills and knowledge about the same.
Features and Comparison of Big Data Analysis Hadoop Technologies
|Language||An algorithm of Map and Reduce Functions (Can be implemented in C, Python, Java)||PigLatin (Scripting Language)||SQL-like|
|Lines of code||More lines of code||Fewer (Around|
10 lines of PIG = 200 lines of Java)
|Fewer than MapReduce and Pig due to SQL Like nature|
|Development Time||More development effort||Rapid development||Rapid development|
|Abstraction||The lower level of abstraction (Rigid Procedural Structure)||The higher level of abstraction (Scripts)||The higher level of abstraction (SQL like)|
|Joins||Hard to achieve join functionality||Joins can be easily written||Easy for joins|
|Structured vs Semi-Structured Vs Unstructured data||Can handle all these kind of data types||Works on all these kind of data types||Deal mostly with structured and semi-structured data|
|Complex business logic||More control for writing complex business logic||Less control for writing complex business logic||Less control for writing complex business logic|
|Performance||Fully tuned MapReduce program would be faster than Pig/Hive||Slower than a fully tuned MapReduce program, but faster than badly written MapReduce code||Slower than a fully tuned MapReduce program, but faster than badly written MapReduce code|
Big Data Analysis With Hadoop
Hadoop makes the task of analyzing Big Data easy as it is fault tolerant, highly flexible and scalable. The Map-Reduce feature helps the huge data of businesses to be analyzed easily. The daily DBMS techniques such as joining, indexing and graphing make it simpler for classifying and clustering the data.
At present Big Data has firmed its steps in almost every field of information technology including banking, government sectors, manufacturing, aviation industry, and hospitality. The organizations making use of the technology like AWS (Amazon Web Services), Intel, Cloudera, Ms (Microsoft), MapR Technologies, Teradata.
Basically, Hadoop is implemented in any firm for these reasons:
So the analysis done on data, help market analysts, researchers, business to form profound insights from the collected data, producing effective results in the business tycoon’s laps. The business masters make a precise analysis and the pivot indications from the so analyzed data, rains the fortunes on their desks.
Some of the Use Cases where Hadoop is being implemented
Whenever the peer browses or traverses the web pages, e-commerce sites, search flights, hotels or when a particular object is put into a cart, the advertising companies make full use of the data so collected and the analysis of this variety of data and activities to create a better recommendation system to offer un-denying discounts and offers.
By making use of Hadoop in telecommunication’s IT department, the service provider can monitor the switches of the clients or the users. The movement of a customer from one network provider to the second one, for the issues like breaking of the voice on call, congestion, disconnection, not delivering of the SMS/MMS service accurately, call drops, lead to the compelling switch from one network provider to the other.
The data so gathered from all those issues is then analyzed using various techniques like Hadoop to fix all that nuisance. Based on that analysis report control measures can be applied like implanting the network tower of particular cellular service in that very area can prove in getting effective results then after.
If one wants to dig deeper into the technicalities of Big Data, then here’s the source of a case study of the New York Stock Exchange. The case study clears one of the most applied Statistics concepts, i.e., ‘Covariance.’
Enterprises Applying Big Data Analysis with Hadoop
- 1. BT
The Cloudera enterprise data hub used by BT effectively uses Apache Hadoop to slice down the call-outs. The analysis of the characteristics of it’s the network, the company finds out the cause, whether the slow speed or a customer issue is responsible for the problem.
The Cloudera hub facilitates a unique view of the customer data reserved in a Hadoop environment. This helped BT to earn a Return on Investment (ROI) ranging from 200 to 250% within a year of deployment
The company has also used the Apache Hadoop framework to assemble new service as “View My Engineer”. The SMS and email alert system lets its customers to track the location of engineers.
- 2. Royal Bank of Scotland (RBS)
The Royal Bank of Scotland has co-allianced with Silicon Valley’s namer Trifacta to have the Hadoop data lake in a particular order, to formulate the insights from the chat conversations that occur between their customers and the bank.
RBS stores around 250,000 chat logs along with its metadata per month.
- 3. CERN
The Large Hadron Collider (HLC) situated in Switzerland is one amongst the most powerful and largest machines, equipped with around 150 million sensors which produces a petabyte of data every second.
To scale such humungous/enormous and complex data Manuel Martin Marquez said that they use Apache Hadoop for its high scalability feature.
- 4. Royal Mail
Due to the excellent strategic technique implementation of the Hadoop framework, it’s being used by ‘Royal Mail’ for “building blocks in place.”
According to Thomas Lee-Warren, the director of the Technology Data Group, hundreds of terabytes of data runs across Hadoop’s nine nodes.
- 5. Marks and Spencer
Who in the world hasn’t heard the ‘Mark and Spencer’ word! This retail brand in 2015 has analyzed the data from various sources adopting Cloudera Enterprise Data Hub Edition system, to have more convenient customer behavior.
The scalable, robust and for bright future-proof strategy, they had Hadoop in their computers.
Apache Hadoop is not a considerable option; instead, it’s a must for the emerging big names. For every public firm, the reason for it's rising is the adoption of Hadoop. The drooling features of Hadoop framework are responsible for its acceptance in the IT department as well as in other sectors.Category : Big Data