Crabby Bill's Development - Book Report/Review Example

The case of Crabby Bills Part The key points raised in the Crabby Bill’s case concerned data management. They could be summarized as 1) corporation accumulates huge quantity of data that they should be sustained for a long time. 2) Data are scattered in a variety of locations all through the corporation. 3) External information must be incorporated to enforce decision making. 4) Data quality, security, and integrity are essential but also hard to ascertain. Crabby Bills uses FileMaker Pro to ensure customers are served great food at great prices with great service (Rainer & Cegielski, 2011). Crabby Bills developed multiple databases for their data to track various types of data utilized. This includes inventory, sales, scheduling, accounting, financial, record management and other data management within the company. One advantage is that they can quickly check for precise types of information. In addition, they also know what the database to monitor and observe. There is no worry of cross client information contamination or breaches. Exporting a customer’s information is quite easy. The company can manage to serve their customers well due to multiple databases they use (Rainer & Cegielski, 2011). There are disadvantages of the multiple database approach. Few unconnected databases will need the similar or same data inserted a number of times. Approach is also free of the potential of errors that would lead to inconsistencies. Updates and bug fixes challenges are key disadvantages that are associated with multiple databases. They form the real nightmare for the approach. DB management issues or a predicament is another disadvantage. When a company gets to a large figure of customers; managing the entire databases becomes a real bother. This causes a company to locate more DBA time to manage those (Rainer & Cegielski, 2011). File Maker Pro is a relational database since it creates relationships between FileMaker Pro database tables. FileMaker Pro is applicable to track the clients’ names, contacts, and membership condition, as well as to create labels and form correspondence to contact members (Rainer & Cegielski, 2011). Crabby Bill’s does not only manage structured data. Crabby Bills was utilizing FileMaker for other purposes, but when their Chief Financial and Technical Officer learned that with FileMaker Pro had advanced functions that could allow him to access, use and manipulate the fundamental data that was available in their two SQL databases the corporation adopted other functions. This is a proof that the company does not only manage structured data. The company was able to create custom reports like scheduling and sales, do computations, and use existing data from the initial FileMaker databases flawlessly using the FileMaker Pro interface (Rainer & Cegielski, 2011). Data in separate databases such as access or spreadsheets can be integrated into FileMaker. The capability to drag and drop an Excel spreadsheet or access data facilitates integration process. To instantly import these data into FileMaker Pro or open a database in FileMaker Pro through the Quick Start Screen is an integration approach. FileMaker is a fully tailored database resolution that can be utilized for many purposes. It’s a cost-effective and easy-to-use database solution that smoothly manages information (Coronel & Morris, 2014). Part 2 This is an executive summary provides the rationale, purposes, and benefits of making the use of “Big Data” a priority for the organization. Big Data comes with opportunities, also some interruptions, only the well-mixed investigative team can operate seamlessly to resolve Big Data dilemma; the business impending in customer experience optimization, decision making and capacity management. Big Data denotes the full data life sequence management, starting from data storage, to data control, but it is necessary to acknowledge that Big Data is means to the finish, not the end. It is about patterns more than careful elements of data and that is where all changes. What makes Big Data different is the approach and implementation procedure (Rainer & Cegielski, 2011). Big Data improved with complex event processing capacities can give solutions in using memory data networks for analyzing tendencies, patterns and actions in real time. Metadata is a capability that must be reckoned in order to implement Big Data successfully. Metadata propels the exactness of reports, authenticates data transformations, guarantees accuracy of computations and enforces reliable definition of commerce terms across manifold business consumers (Coronel & Morris, 2014). Another capability that is associated with Big Data is Apache Hadoop. Apache Hadoop is one of the key accommodating elements in BigData technologies. It makes simpler the processing of huge quantity of unstructured or structured data in a contemptible manner. It is an open source venture from apache that is constantly changing over the periods.  Enterprises who construct their Big Data based on Cloudera can afford to keep literally the entire data in their association, and maintain it all online for instantaneous interactive questioning, analysis, visualization and business intelligence. Cloudera is an enterprise application corporation that offers Apache Hadoop-oriented software and preparation to data-driven industries. Big Data via NoSQL and Hadoop does indeed have a purpose to play in rating Web data (Coronel & Morris, 2014). NoSql (not only sql) is a kind of DBs that does not mainly rely upon scheme based organization and does not employ SQL for data handling. MapReduce was initiated by Google to generate huge amount of web look for indexes. It is essentially a structure to write software that procedures a large quantity of unstructured or structured data over the network. It takes the query and splits it into components to run it on numerous nodes. By dispersed query processing it creates it easy to sustain big amount of information by separating the data into numerous different apparatus. These capabilities are essential for BigData implementations (Rainer & Cegielski, 2011). When it comes to keeping/storing data, there is no exclusive solution. The four important factors to consider when handling data storage and management include knowing data type, not neglecting unstructured data, understanding compliance needs and establishing a data retention policy. A Database is a compilation of data that is kept in a folder, the data is in a system that can be extracted and manipulated with no trouble (capabilities). Big Data implementation calls for process capability. The Process Capability is a quantifiable property of a procedure to the requirement, expressed as a procedure capability directory or as a process performance guide. It is estimated from the procedure average and standard deviation (STDV), where the STDV is calculated based on the mean range or mean standard deviation (Coronel & Morris, 2014). This demonstrates the statistic capability that must be available in Big Data concept. Data integration is the amalgamation of technical and commerce processes employed to merge data from disparate sources into significant and expensive information. A whole data integration resolution involves discovery, monitoring, cleansing, transforming and delivery of statistics from a range of sources (Rainer & Cegielski, 2011). Big Data Architecture is based on incorporation of the following capabilities, Hadoop, MapReduce, and NoSQL that are referred to as the relational technology beyond the curtain (Coronel & Morris, 2014). Architecture is a comprehensive expression spanning technology resolutions, data scientist, analytics, open source – Hadoop, and profitable products. The best practices that can be associated with Big Data involve practices that bring the data down to its exclusive set and minimize the quantity of data to be managed. An effective data management strategy not only permits Big Data to be enforced far more successfully but also makes it further simply recoverable and available with an enormous ninety percent cost savings. The practices should be able to align BigData with precise business objective, ease skills scarcity with standards and control, optimize acquaintance transfer with a center of superiority, top payoff is aligning structured with unstructured data, plan the sandbox for performance and finally align with the cloud operating model (Rainer & Cegielski, 2011). References Coronel, C. & Morris, S. (2014). Database Systems: Design, Implementation, & Management. (11th ed.). Cengage Learning. Rainer, R. K., & Cegielski, C. G. (2011). Introduction to information systems. Hoboken, N.J: Wiley. Read More