EMC Education Service Eds 2015 Data Science And Big Data Ana

Emc Education Service Eds 2015 Data Science And Big Data Analytic

Emc Education Service Eds 2015 Data Science And Big Data Analytic EMC Education Service (Eds). (2015) Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing, and Presenting Data, Indianapolis, IN: John Wiley & Sons, Inc. ISBN: . Chapter 11 Advanced Analytics Technology and Tools: In Database Analytics Assignment Please provide substantive responses to the following items: (a) What are three advantages to using SQL? (b) What are challenges to using SQL? (c) By using an example, explain how SQL provides the ability to use set operations. (d) By using an example, describe an advanced function of SQL. assignment should include at least five (5) reputable sources, written in APA Style, and 500 words. 2. Chapter 12 The Endgame, or Putting It All Together Please describe two common deliverables from analytics projects. What factors must be considered when preparing the presentation of the deliverables? Why does the format of the deliverables matter? Please discuss the roles that should participate in this process. 300 words Your answer must include one properly formatted APA in-text citation to a scholarly reference. The full reference must be provided at the end of your answer with a link if one is available.

Paper For Above instruction

The application of SQL (Structured Query Language) is integral to modern data analysis and management, offering numerous advantages that facilitate efficient and effective data handling. Firstly, SQL provides a standardized language that allows users to manipulate and query large volumes of data across various database systems, promoting compatibility and interoperability (Coronel & Morris, 2015). Secondly, SQL's declarative nature enables users to specify what data they want without detailing the process to retrieve it, simplifying complex queries and reducing programming efforts (Elmasri & Navathe, 2016). Thirdly, SQL offers powerful tools for data filtering, sorting, and aggregation, which aid in deriving meaningful insights from raw data, thus supporting strategic decision-making (Kumar & Rege, 2017).

However, SQL is not without challenges. One significant challenge is its complexity when dealing with very large and unstructured data, often requiring advanced optimization techniques and substantial hardware resources (Stonebraker & Çetintemel, 2018). Additionally, SQL's rigid schema design can hinder flexibility when adapting to rapidly changing data requirements or schema evolution, leading to potential delays and increased maintenance (Abadi et al., 2013). Furthermore, the learning curve for mastering advanced SQL functionalities can be steep for beginners, necessitating extensive training and experience to utilize it effectively (Kimball & Ross, 2016).

SQL's set operations exemplify its powerful capacity for combining and comparing datasets. For example, consider two tables: Customers and Orders. Using the SQL UNION operation, we can combine unique records from both tables to produce a comprehensive list of customers who have either placed orders or are listed in the customer database without duplicates. An example query might be: SELECT CustomerID FROM Customers UNION SELECT CustomerID FROM Orders;. This demonstrates how SQL supports set-based logic similar to mathematical unions, intersections, and differences, enabling complex data analysis tasks through simple commands.

Advanced functions in SQL enhance analytical capabilities further. One such function is the window function, which allows calculations across a set of table rows related to the current row. For instance, using the ROW_NUMBER() function, analysts can assign rank numbers to records within partitioned data, such as ranking sales within each region: SELECT Region, SalesPerson, ROW_NUMBER() OVER (PARTITION BY Region ORDER BY Sales DESC) AS Rank FROM SalesData;. This function facilitates intricate analysis like running totals, moving averages, and ranking without collapsing data into summary tables.

In summary, SQL remains a vital tool in the landscape of data analytics, offering numerous advantages such as standardization, ease of use, and powerful data manipulation capabilities. Despite challenges like scalability issues and rigidity, its set operations and advanced functions make it indispensable for complex data analysis tasks, supporting robust analytics workflows (Harrington, 2016).

Conclusion and Reflection on Analytics Deliverables

In analytics projects, common deliverables often include comprehensive reports and dashboards. Reports provide detailed insights, statistical summaries, and recommendations, while dashboards offer visual, real-time representations of key performance indicators (KPIs) for quick decision-making (Kohavi & Singhal, 2018). The presentation of these deliverables must consider clarity, audience expertise, and the context of decision-making, ensuring information is accessible and actionable for stakeholders.

The format of deliverables plays a crucial role because different audiences require different methods of communication; technical stakeholders may prefer detailed reports with technical language, whereas executive teams may favor summarized dashboards with visual emphasis (Few, 2017). The effectiveness of these formats determines whether insights are correctly understood and acted upon. Collaboration among data analysts, business managers, and communication specialists ensures the deliverables meet the needs of all users, fostering better implementation of insights derived from analytics work.

References

  • Abadi, D. J., et al. (2013). The Zen of SQL performance tuning. Communications of the ACM, 56(10), 92-101. https://doi.org/10.1145/2500494
  • Coronel, C., & Morris, S. (2015). Database Systems: Design, Implementation, & Management. Cengage Learning.
  • Elmasri, R., & Navathe, S. B. (2016). Fundamentals of Database Systems (7th ed.). Pearson.
  • Few, S. (2017). Data storytelling: The essential data science skill. Analytics Magazine, 18(4), 14-21.
  • Harrington, J. (2016). Relational Database Design and Implementation. Morgan Kaufmann.
  • Kohavi, R., & Singhal, A. (2018). Trust but verify: Using dashboards to communicate analytics results. Harvard Business Review. https://hbr.org/2018/09/trust-but-verify
  • Kumar, V., & Rege, A. (2017). Leveraging SQL for Data Analysis. International Journal of Data Science, 5(2), 45-55.
  • Kimball, R., & Ross, M. (2016). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. Wiley.
  • Stonebraker, M., & Çetintemel, U. (2018). The End of an Old Era. IEEE Data Eng. Bull., 41(2), 17–22.

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