Ebay’s big data solution | Computer Science homework help

2- 3 pages EPA Style assignment. 

The deliverable will be an APA style paper consisting of 2-3 pages (excluding cover, TOC & References pages) with the following Table of contents:
+ Abstract
+ Context and Background Information
+ Identification of the Main Issues/ Problems
+ Analysis of the Issues
+ Questions Answers
+ References

Here is the Case Study at  below needs to be analyses and than 2,5 pages needs to write!! with APA style!!   

eBay is the world’s largest online marketplace, enabling the buying and selling of practically anything. Founded in 1995, eBay connects a diverse and passionate community of individual buyers and sellers, as well as small businesses. eBay’s collective impact on e-commerce is staggering: In 2012, the total value of goods sold on eBay was $75.4 billion. eBay currently serves over 112 million active users and 400+ million items for sale.

The Challenge: Supporting Data at an Extreme Scale

One of the keys to eBay’s extraordinary success is its ability to turn the enormous volumes of data it generates into useful insights that its customers can glean directly from the pages they frequent. To accommodate eBay’s explosive data growth—its data centers perform billions of reads and writes each day—and due to the increasing demand to process data at blistering speeds, eBay needed a solution that did not have the typical bottlenecks, scalability issues, and transactional constraints associated with common relational database approaches. The company also needed to perform rapid analysis on a broad assortment of the structured and unstructured data it captured.

The Solution: Integrated Real-Time Data and Analytics

Its Big Data requirements brought eBay to NoSQL technologies, specifically Apache Cassandra and DataStax Enterprise. Along with Cassandra and its high-velocity data capabilities, eBay was also drawn to the integrated Apache Hadoop analytics that come with DataStax Enterprise. The solution incorporates a scale-out architecture that enables eBay to deploy multiple DataStax Enterprise clusters across several different data centers using commodity hardware. The end result is that eBay is now able to more cost effectively process massive amounts of data at very high speeds, at very high velocities, and achieve far more than they were able to with the higher cost proprietary system they had been using. Currently, eBay is managing a sizable portion of its data center needs—250TBs+ of storage—in Apache Cassandra and DataStax Enterprise clusters.

Additional technical factors that played a role in eBay’s decision to deploy DataStax Enterprise so widely include the solution’s linear scalability, high availability with no single point of failure, and outstanding write performance.

Handling Diverse Use Cases

eBay employs DataStax Enterprise for many different use cases. The following examples illustrate some of the ways the company is able to meet its Big Data needs with the extremely fast data handling and analytics capabilities the solution provides. Naturally, eBay experiences huge amounts of write traffic, which the Cassandra implementation in DataStax Enterprise handles more efficiently than any other RDBMS or NoSQL solution. eBay currently sees 6 billion+ writes per day across multiple Cassandra clusters and 5 billion+ reads (mostly offline) per day as well.

One use case supported by DataStax Enterprise involves quantifying the social data eBay displays on its product pages. The Cassandra distribution in DataStax Enterprise stores all the information needed to provide counts for “like,” “own,” and “want” data on eBay product pages. It also provides the same data for the eBay “Your Favorites” page that contains all the items a user likes, owns, or wants, with Cassandra serving up the entire “Your Favorites” page. eBay provides this data through Cassandra’s scalable counters feature.

Load balancing and application availability are important aspects to this particular use case. The DataStax Enterprise solution gave eBay architects the flexibility they needed to design a system that enables any user request to go to any data center, with each data center having a single DataStax Enterprise cluster spanning those centers. This design feature helps balance the incoming user load and eliminates any possible threat to application downtime. In addition to the line of business data powering the Web pages its customers visit, eBay is also able to perform high-speed analysis with the ability to maintain a separate data center running Hadoop nodes of the same DataStax Enterprise ring (see Figure 7.7).


Another use case involves the Hunch (an eBay sister company) “taste graph” for eBay users and items, which provides customer recommendations based on user interests. eBay’s Web site is essentially a graph between all users and the items for sale. All events (bid, buy, sell, and list) are captured by eBay’s systems and stored as a graph in Cassandra. The application sees more than 200 million writes daily and holds more than 40 billion pieces of data.

eBay also uses DataStax Enterprise for many time-series use cases in which processing high-volume, real-time data is a foremost priority. These include mobile notification logging and tracking (every time eBay sends a notification to a mobile phone or device it is logged in Cassandra), fraud detection, SOA request/response payload logging, and RedLaser (another eBay sister company) server logs and analytics.

Across all of these use cases is the common requirement of uptime. eBay is acutely aware of the need to keep their business up and open for business, and DataStax Enterprise plays a key part in that through its support of high availability clusters. “We have to be ready for disaster recovery all the time. It’s really great that Cassandra allows for active-active multiple data centers where we can read and write data anywhere, anytime,” says eBay architect Jay Patel.

Questions for Discussion 

  1. Why did eBay need a Big Data solution?
  2. What were the challenges, the proposed solution, and the obtained results?
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