Shopping is alluring! How ardently you desire a red top, a pair of blue jeans with some peppy accessories! While following the global trends, you visit an online e-commerce website to buy what you have been dreaming of. However, you are not the only one engaged in such an activity, millions like you are continuously scrolling such sites to own and flaunt the best of things that these websites offer. This is how “you” see the world – colorful, enticing, and vibrant. But, have you ever thought about the other side of the world, wherein your transactions and millions of other transactions create massive amount of largely unmanageable data? Ever did such a thought cross your mind while shopping in Walmart, or in any other supermarket, about where does the data go? Is it small enough to handle or is it large enough to crash the servers? Well, by now you must be visualizing servers with horrendously overflowing data – voluminous, unmanageable, and Big “Bigdata.”
Yes, this is an undeniable reality that Walmart, with millions of customers, generates nearly 2.5 petabytes of unstructured, unsystematic and ungrammatical data in an hour. Similarly, millions of select queries of millions of customers on a shopping website generate terabytes of click stream logs. Huge collection of data is equally a concern for the telecom industry that caters to millions of customers every hour. A sole telecom tower handles millions of incoming calls, outgoing calls, messages, data usage, and call detail records. If a single activity by one customer generates one CDR log, how many logs will be generated if millions of customers perform zillions of activity per day or per hour per se? This is indeed mind-boggling because if terabytes of data is generated at one telecom tower, just imagine the unimaginable amount of data generated at all the towers across the country.
Apart from the retail and telecom industry, banking and finance and healthcare sector also rely heavily upon Bigdata solutions. Big hospitals have to tackle huge data regarding their daily activities such as Operator Information, Medical Records, Drug Information, Emergency work log, In-patient/out patient record, Health Insurance claims, etc. On the other hand, banks act as storehouses of gigabytes and terabytes of data when their servers are bombarded with huge amount of unstructured data in the form of Mutual Funds, EMIs, SIP, Home Loans, Gold Loans, Car Loans, Cheque transactions, or net banking. As a matter of fact, data collected in all such sectors is largely unstructured, huge, raw, and big, that is why the term “bigdata” and Hadoop is an open source framework used to process such a Varied and Voluminous data, with a thrilling Velocity.
The world of bigdata and Hadoop is indeed intriguing because it is not limited to data storage but takes into its fold other aligned activities such as Data Mining, Reporting, and Predictive Analysis. Log storage is essential, but to extract the right kind of data from petabytes of unstructured and big data is definitely an uphill task. A huge number of retail houses depend on data mining which digs out some patterns of customer behaviour, and sales and purchase. Fortune 500 Companies and Walmart, in specific, make use of data mining, and data algorithms to analyse customer purchase pattern, depending on which they launch their new products and recommend certain products to their existing customers. Similarly, bigdata solutions to the banking sector include predictive analysis about spending patterns, gathered specifically from credit/debit card swipes on POS, in order to facilitate their services and availability to their customers. However, one of the smartest ways to collect and analyse the “right” kind of data comes from the world of smart phones. You might have pondered over why do android apps ask for access to your phonebook, messages, mails, and media. The answer is Data mining and Predictive analysis. Retail companies like ebay use bigdata solutions for data storage and data mining. Having an access to your personal details, your images, and messages makes them equipped with lots of significant data that can predict customer behaviour, thereby improving customers shopping experience and also encouraging in-favour conversion rate of customer.
Other renowned companies that use bigdata solutions include IT consulting giants Infosys, Cognizant, and Accenture. These companies rely on bigdata hadoop for Client-specific projects in finance, retails, and telecom. FOX TV Channel uses it for Machine Learning and Log Analysis. Yellow Pages, the telephone directory uses bigdata hadoop for internal search, filtering, and indexing. Poker, the game site, predicts the playing patterns of its customers by analysing their game history. Web hosting services, Rackspace uses it for indexing logs from email hosting system for search, Powerset/Microsoft, the natural language search, uses it for data storage, and last but not the least, ebay, the retail store uses bigdata hadoop for data storage and data mining, and Walmart for log storage and predictive analysis for enhancing customer shopping experience.
It is undoubtedly evident that bigdata solutions have worked wonders in diverse sectors. This remains the primary reason why everyone is keen on undergoing Hadoop training. To have hands-on experience in Hadoop Development and Hadoop Administration, and to undergo extensive training with some real-time industry projects in hadoop, join ETLhive, deemed as the best hadoop training institute in Pune. At ETLhive, our industry-experienced training professionals impart a specifically targeted career-oriented training in hadoop. The hadoop training course at ETLhive is comprehensive and covers all the significant aspects such as Pig, Hive, Sqoop, MapReduce, Flume, Kafka, Oozie, MongoDB, Elastic Search, and Spark and Scala. Come, join, learn, and excel with ETLhive because We Deliver What We Promise!