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Amazon - Big Data

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Table of Contents

1.0 Background description of Amazon.com 3 2.0 What is Big Data? 4 2.1 The Business View 5 2.2 Technical View 5 3.0 Amazon.com and Big Data 6 4.0 Identification of Amazon.com’s "Big Data" needs 6 5.0 Big Data problems to be solved and Big Data solutions 6 6.0 What are AWS (Amazon.COM web services) problems and what is the solution for it 8 6.1 Kinesis other advantages 9 7.0 Conclusion 10 8.0 References 11

1.0 Background description of Amazon.com
History

Amazon.com, Inc. (Amazon.com) serves consumers through its retail websites and focus on selection, price, and convenience. The Company offers programs that enables sellers to sell their products on its Websites and their own branded Websites and to fulfill orders through them , and programs that allow authors, musicians, filmmakers, application developers, and others to publish and sell content. The Company operates in two segments: North America and International. The Company serves consumers through its retail websites, and focus on selection, price, and convenience. The Company designs its Websites to enable millions of products to be sold by the Company and by third parties across dozens of product categories. Customers access its Websites directly and through its mobile Websites and apps. It also manufactures and sells Kindle devices. In October 2013, Amazon.com Inc acquired TenMarks Education Inc. Effective February 5, 2014, Amazon.com Inc acquired Double Helix Games LLC.

Amazon.com is a Fortune 500 e-commerce company based in Seattle, WA. Amazon was one of the first big companies to sell goods over the Internet . The company was founded by Jeff Bezos in 1994, and launched in 1995. They started out as an online bookstore and then quickly diversified by adding other items, such as VHS tapes and DVDs, music CDs, software , video games, electronics, MP3s, clothing, furniture, toys and even food items.

In 1999 Time Magazine named Bezos its 1999 Person of the Year. This was largely in recognition of the company's success in popularizing online shopping.
Amazon.com Company Culture

Amazon.com considers itself a completely customer centric company, which is reflected in their company values statement:

• Customer Obsession: We start with the customer and work backwards.
• Innovation: If you don't listen to your customers you will fail. But if you only listen to your customers you will also fail.
• Bias for Action: We live in a time of unheralded revolution and insurmountable opportunity--provided we make every minute count.
• Ownership: Ownership matters when you're building a great company. Owners think long-term, plead passionately for their projects and ideas, and are empowered to respectfully challenge decisions.
• High Hiring Bar: When making a hiring decision we ask ourselves: "Will I admire this person? Will I learn from this person? Is this person a superstar?"
• Frugality: We spend money on things that really matter and believe that frugality breeds resourcefulness, self-sufficiency, and invention!
2.0 What is Big Data? It is not the case that big data has been discovered recently. Data has always existed but the problem is that this information generated from the data has in many cases not been used efficiently, or used at all. Furthermore, there are a lot of applications that have been enabled because of “cloud-services” and unlimited availability of resources, that also have become affordable for customers. The growing interconnected mobile world with smartphones, iPads or the computer in combination with the internet make it possible now for companies to track down customers and to generate more information about them. In general you can describe “big data” as “the voluminous amount of structured, unstructured and semi-structured data a company creates - data that in many cases would take too much time and cost too much money to load into a conventional relational database for analysis.“(SearchBusinessIntelligence.in) To give a better understanding of its value, you can differentiate between what “Big Data” is in business terms of view and what it is from a technical viewpoint.
2.1 The Business View Simply put, “Big Data” is the collection and analysis of huge amounts of data to create a competitive advantage. The idea is to create data, and through the right use you can make the best possible strategic decision. Big data is used in many different sorts of business branches such as retail, the finance sector for risk analysis, or production analysis when you assess and evaluate machine data. But of course also in advertising, “Big Data” plays a big role because with the help of the information generated from customers, companies can target their segments more directly and create services and products according to their needs. Now you have data that you can save and analyze quickly. The question you always have to ask yourself is what kind of data is needed and what is the purpose of it. Businesses, especially the ones in retail have to ask themselves who their customers are, what kind of products they prefer, how they actually use those products, and what happens with them in the social networks. If you take Amazon.com as an example, every time you are going on the recommendations page on their homepage, you create a very valuable amount of proactive data about your preferences, your habits, and buying procedures. Amazon.com can use this data then in order to understand, analyze, and configure customer’s wants even more efficiently in the future.
2.2 Technical View Amounts of data and data analysis have to be scaled in a way that they are innovative in terms of collection, storage, organization, analysis, and transfer of data. So in general, if you have a problem with one of those applications, you have a big data problem. As mentioned before, we have a lot more volume and variety than one decade ago. There are a couple of options for companies to store big data. To take Amazon.com as an example again, there are sets of ways for the storage of data. One of them is the direct connect supported by Amazon’s web service (AWS). With this tool a customer has the possibility to transfer huge amounts of data through dedicated channels from an “on-premises” center to the cloud. If that is not enough, because you have your data stored on physical data mediums, Amazon.com offers an AWS import/export system to transfer that data to AWS. Another option is Queuing Services that helps to send single data. It offers reliable messaging for distributed tasks. The main storage room and target provided for “Big Data” solutions is the simple storage center (S3) that allows to store up to 5 Terabyte of data. Together with those, there are other general open source technologies such as Hadoop for the storage of big data. Hadoop is a Java written framework that makes it possible to conduct intense calculation processes with “Big Data” (Wikipedia) There are many different ways to define “Big Data” but one thing is for sure; “Big Data” is a big deal and it will lead to huge opportunities in the upcoming years.“ (Businessanalytics)
3.0 Amazon.com and Big Data

4.0 Identification of Amazon.com’s “Big Data” Needs Get more information through Customer Product Synchronizations. Amazon.com has a great opportunity to get access to increased customer information/data if they manage to create an even bigger customer awareness using their services fully synchronized on their mobile devices, such as iPads or iPhones. As the world is becoming increasingly interconnected through mobile gadgets, Amazon.com could use this advantage for themselves to learn more about customer preferences and habits. The problem lies in convincing the customers that using web services on a tablet creates a benefit for them. If Amazon.com overcomes this issue they can focus on technical improvements for customer accounts that make it attractive for users to get informed, buy and even look for Amazon.com recommendations on the bus, in the park or at the airport. If you do not use the data generated from those events, you will have a hard time to understand your customer needs in certain daily life situations. Service Synchronization will help to create and later evaluate this data for a better understanding. The solution for Amazon.com in this case is to use real time data sets that tracks down data about information about customer locations, service usage, and potential needs in certain situations that Amazon.com can potentially satisfy in the future.
5.0 Big Data Problems to be Solved and Big Data Solutions Amazon.com handles millions of back-end operations every day, as well as queries from more than half a million third-party sellers. Because of this data gets bigger, and so Amazon.com is positioning itself to help enterprises leverage the overflow of information to create more business value at a lower cost. Hadoop for instance gets a lot of play in discussions of how Amazon.com is making it easier for companies to manage, store, and analyze their data. But is this really sufficient in order for Amazon.com to remain competitive on the market? First of all the big data comes free with the Amazon.com cloud, they offer a library of free datasets that overwhelm the mind. Much of the information is of limited business value, for instance, a few users are likely have a legal reason to browse the dataset, when dealing for example with online payment services such as “SecureCard”. The information is usually and very typically a few years old, so it’s not exactly real-time. The most valuable thing about the Amazon.com big data library is that it gets you to consider where else you might look for free data.

Below you will find a cycle matrix explaining the major issues identified with Amazon.com. As mentioned in the table below, there are always the linked data inputs, which lead to the analytic models, which lead to the decision-support tools, which then finally lead to the business value.

Furthermore, Amazon.com is not available in all European countries, for example it is not available in Switzerland, although there is no plausible explanation for it. So what then generally happens is that customers make an account on the website of a neighboring country and have it sent to a friends address or even a postal address. As such this is not a big issue, and it certainly isn’t for Amazon.com, as this remains revenue for them. But when it comes to analyzing customer behavior based on location and purchasing frequency, this will falsify the results as these customers are in another location. This is not a big deal when we are talking Annemasse and Geneva for example, as the purchasing behavior is relatively identical except maybe the disposable income at hand, but there are known cases of Indians in Germany buying non stop and ship then via DHL and local courier to their home village in India, and this will affect tremendously the result of an analysis. Especially that it makes recommendations given to the user, such as “other users in your area also liked this article” superfluous. The question to this issues “Does Amazon use the location services on big data to make targeted suggestions efficiently?” is left open with a large question mark at the end. In addition, Amazon.com has always been relying on third party suppliers in order to efficiently perform their delivery. This automatically implies that they have to share the big data information from customer accounts, in order for the supplier to perform the delivery. But of course, there are land mines in the path to such market domination. It is said that Amazon.com is making a strong push to offer same-day delivery for many of its products (available in the United States). Such a break through could destroy local retail, because who should go into the city to do the heavy shopping if you can buy today and receive the good tomorrow? Nevertheless, as Amazon.com spends a fortune in the kind of infrastructure necessary to reach this level of service, its own fixed costs will increase, and this is a dangerous trail for Amazon to walk on, if it wishes to remain cheaper than its competition.
6.0 What are AWS (Amazon Web Services) Problems and what is the Solution for it Amazon.com online services are not consolidated meaning that if an item is available in one of their warehouses around the world for example country X and a user have logged in from country Y server the user can't allocate the item because local servers are not interconnected. Like Google, there is one thing that Amazon.com is not lacking of and that is data, since the company is quite old for a technology company they have got this vast data resource that they did not know how to put them all in good use. Amazon Web Services is making a bigger push into monetizing that exponentially growing treasure trove through Kinesis, a new service for processing huge volumes of streaming data in real-time. Basically, Kinesis buffers the data into a storage system with checkpoints to ingest data into the Redshift cloud data warehouse. Users can store and process terabytes of data each hour from hundreds of thousands of sources, including (but not limited to) financial transactions, social media feeds, and location-tracked events. According to AWS, not only can these business customers (and Amazon.com, for that matter) get useful data back instantly, but also be able to write applications, generate alerts, and make other decisions virtually instantly. AWS executives that up until now, most big data processing has been done with a batch-oriented approach with a hodgepodge of open source tools. During Thursday's keynote at AWS reinvent 2013, Amazon.com CTO Werner Vogels also dismissed the buzzphrase "Internet of Things," suggesting that this is a much more serious issue than often realized. Connecting the dots, users can also move this data around within the AWS cloud between the Simple Storage Service (S3), Elastic Map Reduce (EMR) and Redshift. Amazon.com asserted that Kinesis can scale to support applications and data streams of any size while also replicating across multiple availability zones. But these are not the only advantages of Kinesis, Kinesis unlike other algorithms which do their search on big data series wise, it does it parallel wise and thought to that it is going to accelerate the big data searches.
6.1 Kinesis’ other Advantages * Fully Manageable and friendly user Amazon Kinesis integrates with Amazon S3, Amazon Redshift, and Amazon DynamoDB. With Amazon Kinesis, you can reliably ingest, process, and transform all of your data in real-time, before sending it to a business application, or a data store for further analysis. For example, with Amazon Kinesis it is easy for you to write an application that de-duplicates and aggregates your log data, and then archive it to Amazon S3. Another example is an Amazon Kinesis application that extracts key metrics from the data and then, using the Amazon Kinesis-Redshift connector, writes it to your Amazon Redshift data warehouse for querying by business analysts, product managers, or other staff in your organization. Amazon Kinesis also integrates with AWS Identity and Access Management (IAM) to provide fine-grained access control. * Pre-built Application Client Library Amazon Kinesis provides developers with client libraries to build and operate real-time streaming data processing applications. The Amazon Kinesis Client Library enables you to focus on your business logic, letting the client library automatically handle complex issues like adapting to changes in stream volume, load-balancing streaming data, coordinating distributed services, and processing data with fault-tolerance. * CloudWatch Monitoring Built-in Amazon Kinesis displays key operational metrics for your streams in the AWS Management Console. The service also integrates with Amazon CloudWatch so you can see your throughput, utilization, and latency for each Amazon Kinesis stream, and easily track your resource consumption. * Low cost Amazon Kinesis is designed to be cost-efficient for workloads of any scale. You can get started by provisioning a simple stream comprised of a single shard, enabling up to 1 MB per second of reliably-ingested and durably-stored data. With pay-as-you-go pricing, you pay low hourly rates only for the resources you consume. With easy administration and usage-based pricing, Amazon Kinesis can offer significantly lower total cost of ownership (TCO) compared to custom-built alternatives.
7.0 Conclusion Big data represents a new and evolving analytics ecosystem – it is not one technology, but a set of overlapping technologies that combine a range of tools and techniques for managing it efficiently and extracting real value. Amazon.com’s CEO Bezos’s data-driven customer focus mindset has Amazon.com churn out 42 products and services under their belt covering a multitude of areas serving different functions, from Compute, Analytic, Deployment & Management, Application Services, Payments & Billing, Software, Networking, Content Delivery, Database, Storage, Support, Web Traffic, to Workforce. 2013 alone reeled in an approximate US$ 3.2 Bil for Amazon.com on their Amazon Web Services (AWS) products alone. They served customers ranging from NASA to Netflix with dozens of cheap, on-demand computer services via the “cloud.” There is considerable interest at present on the topic of big data and Amazon.com have the tools and facilities for this vision and there is a need to focus on software integration as true value is gained from a hybrid of existing and new data systems and how analytics generated from big data can be leveraged for business benefit. It is important for organizations to design an logical (or extended) data warehouse and advanced analytics ecosystem so as to be able to develop and grow the BI environment. Every company is different thus bringing different needs to be met and we know that one size does not fit all and it is becoming impossible for any given vendor, not even Amazon to satisfy all requirements. Hence integration with existing enterprise systems will be a key vendor differentiator for Amazon.com. The company that win will be the one that can best tackle cost, product and data integration, and/or advanced analytics needs. One thing is for sure and that is their customer focus mentality that will bring them closer to meeting their customer’s needs, but whether or not their Amazon Kinesis will solve it all is an imminent question, with the ability to manage more big data, will there be new tribulations arising? It is a continuously growing and advancing process and Amazon.com will need to consider all factors brought on by their business customers in achieving a customized solutions in an easy and straight forward manner.
8.0 References * Wikipedia: Amazon, from http://en.wikipedia.org/wiki/Amazon.com * Amazon Official Webpage. Amazon Kinesis Product Details. Retrieved on February 13, 2014, from http://aws.amazon.com/kinesis/details/ * Amazon Official Webpage. Overview of Kinesis. Retrieved on February 13, 2014, from http://docs.aws.amazon.com/gettingstarted/latest/emr/getting-started-emr-overview.html * Apache Hadoop. Rerieved on February 14, 2014, from http://de.wikipedia.org/wiki/Apache_Hadoop * Elliott, Timo (July 5, 2013). 7 definitions of big data you should know about. Retrieved on February 15, 2014, from http://timoelliott.com/blog/2013/07/7-definitions-of-big-data-you-should-know-about.html * Guide to big data analytics tools, trends and best practices. Retrieved on February 12, 2014, from http://searchbusinessanalytics.techtarget.com/essentialguide/Guide-to-big-data-analytics-tools-trends-and-best-practices * King Rachel (November 14, 2013). Kinesis: Amazon Web Services's answer to big data? Retrieved on February 14, 2014, from http://www.zdnet.com/kinesis-amazon-web-servicess-answer-to-big-data-7000023223/ * Manyika J., Chui M., Brown B., Bughin J., Dobbs R., Roxburgh C., Hung Byers A. (May 2011). Retrieved on February 12, 2014, from http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation * Wayner Peter, InfoWorld (December 5, 2013). 18 essential Hadoop tools for crunching big data. Retrieved on February 12, 2014, from http://www.infoworld.com/slideshow/131105/18-essential-hadoop-tools-crunching-big-data-232123 * Web- Seminar about Big Data and Amazon by Amazon employee. Retrieved on February 13, 2014, from http://www.youtube.com/watch?v=GcCmEMgh2gQ * Youtube sources: http://www.youtube.com/watch?v=QDyekmgn9dw * http://www.youtube.com/watch?v=6Wp1SihP6FM * http://www.youtube.com/watch?v=ZROcwFis7wI * http://www.youtube.com/watch?v=AXAaCG2QUkE * http://www.youtube.com/watch?v=mK7_5Ag8Z2E * http://www.zdnet.com/amazons-aws-3-8-billion-revenue-in-2013-says-analyst-7000009461/ * http://www.forbes.com/sites/georgeanders/2012/04/04/inside-amazon/

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