Chapter Objectives
In this chapter, you will learn to:
• understand the differences between operational and tactical/strategic decision-making;
• define a data warehouse in terms of its key characteristics;
• discern different types of data warehouse schemas;
• understand the key steps of the extraction, transformation, and loading process;
• define a data mart in terms of its key characteristics;
• understand the advantages and disadvantages of virtual data warehouses and virtual data marts;
• define an operational data store;
• discern the differences between data warehouses and data lakes;
• understand the applications of business intelligence by means of query and reporting, pivot tables, and on-line analytical processing.
Opening Scenario
In addition to using its data for day-to-day operational activities, Sober wants to leverage it for both tactical and strategical decision-making. More specifically, the company wants to get a thorough insight in its sales numbers and how these vary on a quarterly basis, per type of service (ride-hailing versus ride-sharing) and per type of car (Sober car or not). By doing so, Sober wants to better understand where it can grow and identify interesting opportunities. The company does not believe it can use its existing relational data model for this since turning the above questions into SQL queries might be too cumbersome. The principals think a new type of data structure is needed to more efficiently answer the above business questions. Considering the limitations of its current model, what would you recommend Sober to do?
Until this point, we have largely focused on storing data in the most optimal way, ensuring their integrity, as much as possible, at all times. A next obvious question is what can we do with these data from a business perspective? In this chapter, we discuss how to take data and extract valuable new business insights from it. We start by zooming into the various levels of corporate decisionmaking and how this relates to data requirements that support these decisions. This will bring us to the concept of a data warehouse, a massive consolidated data store that we formally define and extensively discuss in terms of data model, design, and development. We contrast data warehousing with some newer developments, such as virtualization, data lakes, and indicate synergies.
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