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Encoding Quick Response Code in Software Figure 3-6: The data warehousing concept

Figure 3-6: The data warehousing concept
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SQL-based relational databases were a clear choice for the warehouse data store because of their flexible query processing. A series of new companies was formed to build the data extraction, transformation, and database query tools needed by the data warehouse model. In addition, DBMS vendors started to focus on the kinds of database queries that customers tended to run in the data warehouse. These queries tended to be large and complex such as analyzing tens or hundreds of millions of individual cashregister receipts to look for product purchase patterns. They often involved time-series data for example, analyzing product sales or market share data over time. They also tended to involve statistical summaries of data total sales, average order volume, percent growth, and so on rather than the individual data items themselves.
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To address the specialized needs of data warehousing applications (often called "Online Analytical Processing" or OLAP), specialized databases began to appear. These databases were optimized for OLAP workloads in several different ways. Their performance was tuned for complex, read-only query access. They supported advanced statistical and other data functions, such as built-in time-series processing. They supported precalculation of database statistical data, so that retrieving averages and totals could be dramatically faster. Some of these specialized databases did not use SQL, but many did (leading to the companion term "ROLAP," for Relational Online Analytic Processing). As with so many segments of the database market, SQL's advantages as a standard proved to be a powerful force. Data warehousing has become a one-billion-dollar plus segment of the database market, and SQL-based databases are firmly entrenched as the mainstream technology for building data warehouses.
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This chapter described the development of SQL and its role as a standard language for relational database management: SQL was originally developed by IBM researchers, and IBM's strong support of SQL is a key reason for its success. There are official ANSI/ISO SQL standards and several other SQL standards, each slightly different from the ANSI/ISO standards. Despite the existence of standards, there are many small variations among commercial SQL dialects; no two SQLs are exactly the same. SQL has become the standard database management language across a broad range of computer systems and applications areas, including mainframes, workstations, personal computers, OLTP systems, client/server systems, data warehousing, and the Internet.
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4: Relational Databases
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Database management systems organize and structure data so that it can be retrieved and manipulated by users and application programs. The data structures and access techniques provided by a particular DBMS are called its data model. A data model determines both the "personality" of a DBMS and the applications for which it is particularly well suited. SQL is a database language for relational databases that uses the relational data model. What exactly is a relational database How is data stored in a relational database How do relational databases compare to earlier technologies, such as hierarchical and network databases What are the advantages and disadvantages of the relational model This chapter describes the relational data model supported by SQL and compares it to earlier strategies for database organization.
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Early Data Models
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As database management became popular during the 1970s and 1980s, a handful of popular data models emerged. Each of these early data models had advantages and disadvantages that played key roles in the development of the relational data model. In many ways the relational data model represented an attempt to streamline and simplify the earlier data models. In order to understand the role and contribution of SQL and the relational model, it is useful to briefly examine some data models that preceded the development of SQL.
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