Discuss the features of NoSQL DBMS that ensure high availability but do not guarantee consistency.
> What are some of the advanced techniques that can be applied by a software solution for data quality improvement?
> Explain how an organization’s business rules can be checked as part of a data audit.
> Describe the key steps to improve data quality in an organization.
> Explain four reasons why the quality of data is poor in many organizations.
> Visit an organization that has implemented information systems on a data warehouse, and interview managers to discuss following issues: a. Does increased data collection lead to any information gaps for managers? b. Do they receive information from diver
> Define the eight characteristics of quality data.
> What are the four basic facilities for the backup and recovery of a database?
> What are four reasons why data quality is important to an organization?
> How can fuzzy logic, pattern matching, and expert systems be used to improve data quality?
> How can the data capture process be improved?
> Briefly describe four threats to high data availability and at least one measure that can be taken to counter each of these threats.
> Define each of the following terms: a. database administration b. data administration c. chief data officer d. master data management e. open source DBMS
> Compare and contrast R and Python as computational environments for analytics.
> Briefly describe three types of operations that can easily be performed with OLAP tools.
> Discuss the role of OLAP in the context of descriptive analytics.
> Having reviewed your conceptual models (from Chapters 2 and 3) with the appropriate stakeholders and gaining their approval, you are now ready to move to the next phase of the project, logical design. Your next deliverable is the creation of a relational
> Explain the different tools for querying and analyzing data in traditional data warehouses and marts that enable various forms of descriptive analytics.
> Explain the three different generations of business intelligence and analytics.
> Explain the progression from DSS to analytics through business intelligence.
> Contrast the following terms: a. Data mining; text mining b. ROLAP; MOLAP c. R; Python
> Match the following terms to the appropriate definitions: - text mining - data mining - descriptive analytics - analytics - predictive analytics - prescriptive analytics a. knowledge discovery using a variety of statistical and computational techniques b
> Identify six broad categories of implications of big data analytics and decision making.
> How is data quality and management vital in realizing the full potential of big data and analytics?
> Describe the core idea underlying in-database analytics.
> Describe the core idea underlying in-memory DBMSs.
> Describe the mechanism through which prescriptive analytics is dependent on descriptive and predictive analytics.
> Having reviewed your conceptual models (from Chapters 2 and 3) with the appropriate stakeholders and gaining their approval, you are now ready to move to the next phase of the project, logical design. Your next deliverable is the creation of a relational
> How is KNIME used as a predictive analytics tool?
> Discuss why data mining applications are growing rapidly in business.
> Illustrate the goals of data mining and how they answer fundamental business questions.
> Discuss the different types of dashboards and their role in business performance management.
> How does Apache Spark differ from Hadoop?
> Define each of the following terms: a. data mining b. online analytical processing c. business intelligence d. predictive analytics e. Apache Spark
> What is the difference between a wide-column store and a graph-oriented database?
> What is the trade-off one needs to consider while using a NoSQL database management system?
> What is the difference between the explanatory and exploratory goals of data mining?
> Identify the differences between Hadoop and NoSQL technologies.
> Having reviewed your conceptual models (from Chapters 2 and 3) with the appropriate stakeholders and gaining their approval, you are now ready to move to the next phase of the project, logical design. Your next deliverable is the creation of a relational
> What are the two challenges faced in visualizing big data?
> Identify and briefly describe the five Vs that are often used to define big data.
> Contrast the following terms: a. data lake; data warehouse b. Pig; Hive c. volume; velocity d. NoSQL; SQL
> Match the following terms to the appropriate definitions: - Hive - Big data - Data lake - Pig - Analytics a. data exist in large volumes and variety and need to processed at a very high speed b. a language that is used to extract, load and transform data
> HDase and Cassandra share a common purpose. What is it? What is their relationship to HDFS and Google BigTable?
> Explain the implementation of MapReduce on HDFS clusters.
> How does HDFS aid in coping with hardware failure?
> Describe and explain the two main components of MapReduce
> What is the role of YARN in the management of highly distributed systems?
> List the purposes Hadoop is used for.
> Martin was very impressed with your project plan and has given you the go-ahead for the project. He also indicates to you that he has e-mails from several key staff members that should help with the design of the system. The first is from Alex Martin (ad
> What is the format that can be used to describe database schema besides JSON?
> Define each of the following terms: a. Hadoop b. MapReduce c. HDFS d. NoSQL e. Pig
> Why is it important to consolidate a Web-based customer interaction in a data warehouse?
> List five claimed limitations of independent data marts.
> Explain the need to separate operational and information systems.
> List the issues that one encounters while achieving a single corporate view of data in a firm.
> Briefly describe the factors that have led to the evolution of the data warehouse.
> Why does an information gap still exist despite the surge in data in most firms?
> List the functions performed by a Data Warehouse Administrator and explain how they differ from the typical data administrator and database administrator.
> Martin was very impressed with your project plan and has given you the go-ahead for the project. He also indicates to you that he has e-mails from several key staff members that should help with the design of the system. The first is from Alex Martin (ad
> Explain the reasons why Data Warehousing 2.0 is necessary.
> Explain how the phrase “extract–transform–load” relates to the data reconciliation process.
> List five errors and inconsistencies that are commonly found in operational data.
> List and briefly describe five steps in the data reconciliation process.
> Contrast the following terms: a. transient data; periodic data b. data scrubbing; data transformation c. data warehouse; data mart; operational data store d. reconciled data; derived data e. static extract; incremental extract f. fact table; dimension ta
> List six typical characteristics of reconciled data.
> Explain why it is essential to scrub data before transformation and how they blend together.
> Which three techniques form the building blocks of any data integration approach?
> Describe the current key trends in data warehousing.
> Explain how data integration is not the only data consolidation technique across an enterprise.
> Martin was very impressed with your project plan and has given you the go-ahead for the project. He also indicates to you that he has e-mails from several key staff members that should help with the design of the system. The first is from Alex Martin (ad
> Briefly explain how the dimensions and facts required for a data mart are driven by the context for decision making.
> Why should changes be made to the data warehouse design? What are the changes that need to be accommodated?
> What is the meaning of the phrase “slowly changing dimension”?
> What are the two situations in which factless fact tables may apply?
> Explain through common examples why determining grain is critical.
> Match the following terms and definitions: - periodic data - data mart - star schema - data scrubbing - data transformation - grain - reconciled data - dependent data mart - real-time data warehouse - selection - transient data - snowflake schema a. lost
> List and describe the various situations in which it becomes essential to further normalize dimension tables.
> Explain the components of a star schema with figures and suitable examples.
> Describe the characteristics of a surrogate key as used in a data warehouse or data mart.
> Discuss the role of an enterprise data model and metadata in the architecture of a data warehouse.
> FAME (Forondo Artist Management Excellence) Inc. is an artist management company that represents classical music artists (only soloists) both nationally and internationally. FAME has more than 500 artists under its management and wants to replace its spr
> What are the key differences between data warehousing and big data approaches to analytical data management?
> What type of an impact has the significant decrease in the cost of storage space had on data warehouse and data mart design?
> Why is real-time data warehousing called active data warehousing?
> Explain how the characteristics of data for data warehousing is different from the characteristics of data for operational databases.
> List the different roles played by data marts and data warehouses in a data warehouse environment.
> What is meant by a corporate information factory?
> List the 10 essential rules for dimensional modeling.
> Define each of the following terms: a. data warehouse b. data mart c. reconciled data d. derived data e. enterprise data warehouse f. real-time data warehouse g. star schema h. snowflake schema i. grain j. conformed dimension k. static extract l. increme
> What is the role of a DBA? List various regulations and standards for physical database design and their functions.
> Identify some limitations of normalized data as outlined in the text.
> What is a translation or code table? When it should be implemented, and what are its advantages?
> FAME (Forondo Artist Management Excellence) Inc. is an artist management company that represents classical music artists (only soloists) both nationally and internationally. FAME has more than 500 artists under its management and wants to replace its spr
> What decisions have to be made to develop a field specification?
> What are the key decisions in physical database design?
> Discuss the potential advantages, technical challenges, and disadvantages of using cloud-based database provisioning.
> Describe the differences between the IaaS, PaaS, and SaaS models of cloud-based database management solutions.
> How can views be used as part of data security? What are the limitations of views for data security?
> What are the major inputs into physical database design?
> Briefly describe four components of a disaster recovery plan.
