Enterprise Database Systems
Traditional Data Architectures
Traditional Data Architectures: Data Warehousing and ETL Systems
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Traditional Data Architectures: Relational Databases
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Traditional Data Architectures: Data Warehousing and ETL Systems
Lesson Objectives
Traditional Data Architectures: Data Warehousing and ETL Systems
- discover the key concepts covered in this course
- describe how a data warehouse is different from a database and how data warehouses are used for business intelligence
- name and define three main tiers of a data warehouse
- compare and contrast various data warehousing schemas, such as Star, Snowflake, etc.
- name use cases of dimension tables and define different types of dimensions and their granularity
- define fact table measures, describe how measures are added and loaded, and outline the steps for implementing a fact table in a data warehouse
- describe how data warehouse keys work, specifying the importance of surrogate keys
- describe extract, transform, and load (ETL) functionality and specify how the movement between transactional OLTP databases and a data warehouse is performed and how to organize and design your extraction, transformation, and loading capabilities to keep your data warehouse up-to-date
- describe the ETL framework and it's three main components - extraction, transformation, and loading
- name and describe the most commonly used ETL tools and software
- specify best practices to be followed when dealing with ETL to perform operations as efficiently as possible
- summarize the key concepts covered in this course
Overview/Description
Data warehouses are actively used for business intelligence and, because they integrate data from multiple sources, are advantageous to simple databases in many instances. Considering modern companies often have ETL-based data warehousing systems, decision-makers need to comprehend how they operate and are appropriately managed. In this course, learn the necessary concepts and processes required to work with and manage projects related to data warehousing. Study data warehousing architectures and schemas and investigate some core data warehouse elements, such as dimension, fact tables, and keys. Furthermore, examine the extract, transform, and load (ETL) approach for working with data warehouses, specifying process flow, tools, and software as well as best practices. When you're done, you'll know how to adopt data warehousing and ETL systems for your business intelligence and data management needs.
Data warehouses are actively used for business intelligence and, because they integrate data from multiple sources, are advantageous to simple databases in many instances. Considering modern companies often have ETL-based data warehousing systems, decision-makers need to comprehend how they operate and are appropriately managed. In this course, learn the necessary concepts and processes required to work with and manage projects related to data warehousing. Study data warehousing architectures and schemas and investigate some core data warehouse elements, such as dimension, fact tables, and keys. Furthermore, examine the extract, transform, and load (ETL) approach for working with data warehouses, specifying process flow, tools, and software as well as best practices. When you're done, you'll know how to adopt data warehousing and ETL systems for your business intelligence and data management needs.
Target
Prerequisites: none
Traditional Data Architectures: Relational Databases
Lesson Objectives
Traditional Data Architectures: Relational Databases
- 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
Overview/Description
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.
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.
Target
Prerequisites: none
