Data>>>>Oil, has already made headlines and have been common conjuncture of discussion from IT folks to Politicians (Leaders, don’t forget Elections). It has done what IC engines had done Horse-carts, to the many MNCs, irrespective of industry, have completely transformed forever. Before we discuss How Big Data drives tempting value, lets first just understand, What is BIG Data?
The term “big data” refers to data that is so large, fast or complex that it’s difficult or impossible to process using traditional methods. In simpler terms, Big Data is amalgamation of all the processes and tools related to utilizing and managing large data sets. Thanks to Doug Laney who articulated the definition of big data in the early 2000s as the three V’s: Volume, Velocity, Variety and one more extension Variability. These are the characteristics of all types of data.
Big data = Big insights. When it comes to understanding your target audience and customer’s preferences, big data plays a very important role. It even helps you anticipate their needs. The right data needs to be effectively presented and properly analyzed. It can help a business organization achieve various goals. Using Big Data has been crucial for many leading companies to outperform the competition.
This is a market trajectory prediction chart. Thank fully, these prediction are always undermarked and the market outperforms , so consider this as a reference.
Lets see how Amazon used Big-Data to rule E-commerce
The brand frequently taps into big data to make decisions, stimulate purchases and please customers. Here are some reasons Amazon and big data plans often arise in discussions about why companies thrive.
It Implements Dynamic Pricing to Stay Competitive
Before retailers used big data for price changes so often, people generally saw the same prices for stuff from day to day, no matter how many times they visited a website. Now, prices change frequently. One of the reasons is because big data platforms assess a person’s willingness to buy. The company changes product prices about 2.5 million times daily, meaning the cost of an average product shifts every 10 minutes. Thanks to the company’s massive amounts of data, it can analyze things ranging from competitor pricing to available inventory, then make informed choices about how much items should cost.
It Screens Purchases and Return Requests for Signs of Fraud
Due to Amazon’s proactive approach, and big data algorithms tweaked to meet precise needs, the company can also scrutinize suspicious return requests., since it sheer size attracts fraud transaction. For example, if big data shows a person has returned an unusually high percentage of things over the past few months, the company might investigate further. In 2018, some long-time customers reported getting banned for making what Amazon deemed too many returns.
It Encourages People to Buy More With Each Order
Amazon’s product recommendations are probably the big data applications most familiar to everyday users
It Uses Data to Change Physical Stores
When Amazon acquired Whole Foods Market, it immediately started using data to change that brand’s operations, including by lowering prices on popular items. That was the first step in a substantially broader effort to harness big data analytics. Amazon Go, the brand’s future convenience store brand, also heavily relies on data to function.
It Depends on Information to Run Fulfillment Centers
Amazon calls its warehouses “fulfillment centers.” It’s not surprising that the company uses big data there, too. One of the more controversial uses relates to crunching productivity statistics and automatically sending workers warnings about being too slow. But, Amazon also uses data to track which items people buy most often and whether the stock is running low. The brand has a patent for what it calls “anticipatory shipping,” too. That approach would predict what people want to buy before they place their orders.
So for storing, maintaining and feeding their data engines the tools they used is Apache Hadoop.
Hadoop Distributed File System (HDFS)
The basis of the system is to store the data on a cluster of servers. Instead of having to invest in a really large server, the system functions on the hard drives of lots of computers that you can compare to your PC. This system is infinitely scalable.
The cluster of servers is constituted of a client server, a driver node, and worker nodes (they’re also often called master nodes and slave nodes, but that’s not super politically correct). A node is a server by the way, which is basically a computer. The driver node distributes jobs and tracks them across the worker nodes who execute the jobs.
- Computing power. Hadoop’s distributed computing model processes big data fast. The more computing nodes you use, the more processing power you have.
- Fault tolerance. Data and application processing are protected against hardware failure. If a node goes down, jobs are automatically redirected to other nodes to make sure the distributed computing does not fail. Multiple copies of all data are stored automatically.
- Flexibility. Unlike traditional relational databases, you don’t have to preprocess data before storing it. You can store as much data as you want and decide how to use it later. That includes unstructured data like text, images and videos.
- Low cost. The open-source framework is free and uses commodity hardware to store large quantities of data.
Now, after a short briefing, the applications are unending. And still if you’re not convinced that why big data is tempting then try googling “What happens in one second on the internet?”
New detailed use cases in future blogs. Also clapping is free.
