Traditional Data Architectures: Relational Databases

Data Architecture    |    Beginner
  • 12 videos | 34m 48s
  • Includes Assessment
  • Earns a Badge
Rating 4.0 of 72 users Rating 4.0 of 72 users (72)
Databases are essential in working with large amounts of data. Managers, leaders, and decision-makers need to choose the right approach when working on a large data project, distinguishing among multiple database types and their use cases. A relational database is a primary traditional data architecture commonly used by most businesses. Working with relational databases has some key advantages but also poses certain limitations. In this course, learn how critically evaluate and work with relational databases. Explore normalization and denormalization of datasets along with specific use cases of these opposite approaches. Examine two main online information processing systems, Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) systems. Finally, investigate the concepts of data warehousing, data marts, and data mining. Upon completion, you'll be able to identify when and how to use a relational database.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Name and describe common database types used in the industry
    Describe key concepts related to the design of relational databases
    Describe situations when normalization or denormalization is needed and name the key steps of each process
    Name 4 different types of normal forms and compare their use cases
    Describe online transaction processing in the context of relational databases and data warehousing
  • Describe the process of online analytical processing in the context of reporting and forecasting
    Describe common use cases and basic principles of data warehousing
    Describe traditional data warehousing technologies such as virtual data warehousing and enterprise data warehousing
    Describe the concept of data mart and how it can be used for business decision-making through data mining
    Compare vertical and horizontal scaling of databases and their limitations
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 44s
    Meet your instructor. In this course, you’ll learn the key concepts related to relational databases. You’ll also explore what Normalization and Denormalization are, and their key differences. Then, you’ll see how a relational database is normalized by explaining the three main normal forms with a practical example. Finally, you’ll explore online transactional processing, OLTP, and online analytical processing, OLAP. FREE ACCESS
  • 3m 10s
    Learn about different types of databases and that relational databases encompass many characteristics. They organize data in tables, made up of rows and columns, or relations. Rows are also called records or tuples, and columns, fields or attributes. Additionally, a language called SQL, Structured Query Language, was developed to work with relational databases. FREE ACCESS
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    3.  Relational Database Design
    4m 32s
    Discover relational database design. A relational database is represented by tables, which contain rows and columns appropriately arranged. The data can be administered without needing to manually rearrange any of the records. You’ll learn the five main advantages of using relational databases, including ease of use. FREE ACCESS
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    4.  Normalization and Denormalization
    3m 1s
    Explore normalization and denormalization. Normalization is the process of efficiently organizing data within a relational database. The objective of normalization is to reduce duplicate information and avoid anomalies related to relationships between tables and duplicate records. Proper normalization minimizes data duplication across tables and allows data in each table to grow organically and independently from other tables. FREE ACCESS
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    5.  Normal Forms and Their Use Cases
    4m 12s
    Learn about the six normal forms of normalization. The most commonly used are primary, composite, candidate, and foreign keys. A primary key is a single-column value used to identify a database record uniquely. A composite key is a primary key composed of two or more columns used to identify a record uniquely. Candidate keys identify each unique record independently. FREE ACCESS
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    6.  OLTP Information Systems
    3m 8s
    Discover online transaction processing, OLTP. This is a concept related to information systems intended to store transaction-oriented data. OLTP involves a relatively small amount of data and supports database queries, such as insert, update, and delete. To avoid single points of failure, OLTP systems are often decentralized. There are many advantages to using OLTP systems. FREE ACCESS
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    7.  OLAP Information Systems
    2m 23s
    Explore online analytical processing, OLAP. This is an important feature in relational databases, as well as in reporting, data mining, and business intelligence. OLAP is a category of software that allows users to analyze information simultaneously from multiple database systems. This technology enables analysts to extract and view business data from different points of view. FREE ACCESS
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    8.  Common Use Cases of Data Warehousing
    2m 24s
    Discover common uses of data warehousing. This system is used to store and report on data, which typically originates in multiple systems. Then it is moved into the data warehouse for long-term storage and analysis. This storage’s structure allows users from many divisions or departments within an organization to access and analyze data according to their needs. FREE ACCESS
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    9.  Traditional Data Architectures
    3m 15s
    Explore traditional data architectures, consisting of enterprise and virtual data warehouses. An Enterprise Data Warehouse, EDW, is a form of corporate repository that stores and manages all the historical business data. Because of their complex structure and size, enterprise data warehouses are often decomposed into smaller databases, so that end users are more comfortable in querying these smaller databases. FREE ACCESS
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    10.  How Data Mining and Data Marts Are Used
    2m 57s
    Learn how data mining and data marts are used. Data Marts are the key to efficiently transforming information into insights. Data warehouses handle large data sets, but data analysis requires easy-to-find and readily available data. A data mart is a subject-oriented database that is often a partitioned segment of an enterprise data warehouse. FREE ACCESS
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    11.  How to Scale a Database
    2m 59s
    Explore database vertical and horizontal scaling. The number of requests an application can effectively support simultaneously measures its scalability. It reaches its limit when it cannot handle additional requests effectively. Then, the critical hardware resource requires different or more machines, bigger hard drives or less live data. Scaling these resources includes any combination of adjustments to CPU and physical memory. FREE ACCESS
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    12.  Course Summary
    1m 2s
    Review what you’ve learned in this course: relational databases and their advantages; normalization and denormalization, and how they differ; the three most important normal forms in normalization; online transaction processing systems and online analytical processing systems; and data warehouses, marts and scalability. FREE ACCESS

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