Big Data: Analysis of Crabby Bills - Case Study Example

BIG DATA al affiliation Part I: Analysis of Crabby Bill’s Case Study a. Introduction This case presents a real life problem faced by numerous small businesses resulting from errors in data making it impossible to manage. Difficulties in data management result from the fact that data increases exponentially as company expands given that old data has to be stored and new data added rapidly. With time, Crabby Bill’s realizes that the data available exceeds the total amount of available space and this prompted for multiple databases. b. Multiple Databases at Crabby Bill’s and the advantages involved Since Crabby, Bill’s is just but a small business, depending on multiple databases, which in the real sense did not communicate with each other (Rainer & Cegielski, 2012). Failed multiple databases connections are not necessary for Crabby due to the difficulty experienced in attaining relational integrity or complexities enforcing across numerous databases, and transactional integrity. There is no compelling reason in this case that call for dataset splitting since Crabby is not even ready to incur the complexity involved and the maintenance efforts needed for the separate databases and multiple database servers (Ramini & Huag, 2010). However, there are advantages linked to multiple databases and these include different organizational objectives like online transaction processing (OLAP) vs online analytical processing (OLTP); distinct companies, tenants, or domains that guaranteed no data commingling between domains thereby demanding segregation of information; and for data archival. c. FileMakerPro technology and relational databases FileMakerPro9 is a wholly characteristic relational database. The technology links easily with other sources of data especially since it is a cross-platform software operating well in Mac Os X and Windows. The relational database functionality makes it possible to create custom reports like scheduling and sales and make calculations (Product Reviewer, 2008). Additionally, FileMakerPro maintains provisional formatting functionality that allows the user to identify visual aspects that require attention with ease while utilizing legacy data. Further, FileMakerPro promotes development of solutions for almost any firm-specific concerns that require attendance like the creation of seating programs for Crabby stores. With FileMakerPro, Crabby Bill’s offers rapid serving up of legacy data, ease of data visibility and extrapolation of required information, and the familiarity of the interface in which the required information is manipulated. Finally, FileMakerPro provides open database connection to SQL and this was a major selection criterion (Product Reviewer, 2008). d. Is Crabby Bill’s only managing challenging structured data? Explain At Crabby Bill’s, the form of data dealt with is mostly structured and it is this reason that promote the use of Structured Query Language or SQl to manage it. Today, Oracle develops SQL programming language to manage and query data in relational database management system. Crabby Bill’s depends on little or no paper-based unstructured systems to supplement inevitable functions in structured systems. e. Integration of data in separate databases into FileMakerPro The design of FileMakerPro renders it as an integrated database tool that solves issues related to isolated data and components of logic interface viewed as ingenious or perverse. According to FileMaker (2014), with constant changes in data and associated information, database flexibility is handy and this requires the use of multiple file solution architecture to reference or incorporate data in a pre-existing system. FileMaker is multi-file solution architecture that improves use of multiple files by allowing database sharing; improves network performance; reduce module-related complexity and overhead; and provide different users with different functionality subject to their preferences (Cologon & Cohen, 2008). Conclusion Using file maker at Crabby Bill’s offers unmatched solutions to managing separate databases. Since the original problem in data management at Crabby was how to deal with numerous databases, FileMakerPro provides more benefits compared to the licensing and other costs incurred. Part II: Executive Briefing/Summary Document 1. Introduction The purpose of this report is to examine the reasons why managing data on your organization experience numerous challenges that pose numerous unexpected costs. The paper also recommends the use of big data to improve organizational data management. 2. Why is go for big data? Big data effectively manage the issues of storage, search, visualization, sharing, and analysis of large datasets, particularly those that are impossible to predict or anticipate. The efficiency of big data research draws attention to the fact that some companies like New York Stock Exchange or NYSE and airline industries collect huge data per hour, terabytes, peta-, and Exabyte not all of which is regarded as high value or high density. With big data, analytics, filtration process separates low value or low-density given revelation to high-density data thereby easily managing data volume. As an analytical technique, Big Data is identified uniquely by value creation. Unlike conservative business, intelligence that performs a simple summation of known worth, big data discovers value via the refinement modeling procedure. The process involves several phases namely hypothesis creation, defining models entailing statistical, semantic, or visual aspects, validation, and definition of new hypotheses. 3. The capabilities necessary for a Big Data Implementation In order to implement Big Data several capabilities are required, and these include storage and management, database, processing, statistical analysis, and data integration (Oracle White Paper, 2012). With the statistical capability, Oracle R and Open source project R enterprise are utilized. Data integration capability requires Oracle Big Data Connectors and integrators, and Oracle Loader for Hadoop. Database capability involves using tools like Apache HBase that offers real time read/write access randomly to NoSQL, Oracle NoSQL, Apache Cassandra, and Apache hives. Big Data architecture storage and management capability involves the use of technology like Hadoop distributed file system (HDFS) which offer scalable storage and replication of data automatically for fault tolerance, Cloudera manager offer end-to-end management for distribution of Apache Hadoop. 4. Big Data Architecture Traditionally, information architecture capabilities was structured and included reference and master data, and transaction data, enterprise integration, data warehouse, and analytic capacities. With the addition of big data capabilities, information architecture changed to include machine generated, text, video, audio and image; distributed file system and principal worth data storage; map minimization; data warehouse and sandboxes; and analytic abilities (Oracle White Paper, 2012). Map minimization differentiates big data from unstructured data by eliminating direct storage of raw data into the warehouse by first processing it. The conditions required for big data analysis involve ensuring the continually changing and unpredictable data is captured, and correlated with existing enterprise structured data. Since big data and traditional business integrity lack integration, the solution is to incorporate the findings of big data and adding them into the current data warehousing platform. The process involved includes identification of relevant data, acquisition, and organization, analyzing data, and making the right decision regarding the best big data architecture that suits the needs of the organization (Oracle White Paper, 2012). 5. Key drivers The fundamental drivers to consider are classified as business and Information technology drivers (Oracle White Paper, 2012). Business drivers include the need for better insights, timeliness and accuracy, and faster turn-around. IT drivers include the need for minimizing costs, lessening data transfer, conventional toolset, supremacy and security, and quicker time-to-market. 6. Best practices With all requirements analyzed, building effective big data architecture involves a. Alignment of specific business objectives with Big data Since big data focuses identifying hidden value, intelligent filtration makes it possible to reveal a low density and elevated volume data and businesses should learn how to apply big data techniques to firm goals. b. Ease skills storage with governance and standards For firms to venture into Big data, there has to be skills availability such that Big Data Technology considerations and options become part of the IT control program. Through necessary standardization, it is possible to manage one’s costs and best leverage one’ c. Knowledge transfer optimization with center of Excellence or CoE CoE are crucial for sharing solution knowledge, artifacts training, communications management, and oversight of projects. d. Alignment of unstructured and structured data for top payoff For best results, organizations have to keep in mind that big data analysis models and processes can be both human and machine (Oracle White Paper, 2012). Consequently, the best results are achieved by correlating novel analytical models with knowledge workforce’ varying data source and types to establish associations and form consequential discoveries. e. Planning the performance sandbox This phase requires that firms identify the best work areas to allow data experimentation with statistical algorithms and accommodation of interactive exploration. f. Aligning with the model for cloud operation With changing requirements for big data architecture, cloud provisioning and security plan plays an integral role. Conclusion With the inevitability of big data for large organizations, machine generated data offer crucial ground for future success despite the challenges involved. References Cologon, R. J., & Cohen, D. R. (2008). FileMaker Pro 9 bible. Indianapolis, IN : Chichester : John Wiley. FileMaker. (2014, March 5). Publishing databases on the web with FileMaker Pro and FileMaker Server. Retrieved from FileMaker: http://help.filemaker.com/app/answers/detail/a_id/7466/~/publishing-databases-on-the-web-with-filemaker-pro-and-filemaker-server Oracle White Paper. (2012, August). Oracle Information Architecture: An Architects Guide to Big Data. Retrieved from Oracle.com: http://www.oracle.com/technetwork/topics/entarch/articles/oea-big-data-guide-1522052.pdf Product Reviewer. (2008, June 18). FileMaker Pro 9 Delivers Access to Legacy Data at Crabby Bill’s. Retrieved from Information Management: http://www.information-management.com/issues/2007_49/10001516-1.html Rainer, K. R., & Cegielski, C. G. (2012). Data and Knowledge Managemen. In K. R. Rainer, & C. G. Cegielski, Introduction to Information Systems: Supporting and Transforming Business (pp. 110-135). United States: JOhn Willey & Sons. Ramini, S., & Huag, F. (2010). Distributed database management systems : a practical approach. Hoboken, N.J.: IEEE Computer Society. Read More