In this 12-video course, learners explore how to define data, its lifecycle, the importance of privacy, and SQL and NoSQL database solutions and key data management concepts as they relate to big data. First, look at the relationship between data, information, and analysis. Learn to recognize personally identifiable information (PII), protected health information (PHI), and common data privacy regulations. Then, study the data lifecycle's six phases. Compare and contrast SQL and NoSQL database solutions and look at using Visual Paradigm to create a relational database ERD (entity-relationship diagram). To implement an SQL solution, Microsoft SQL Server is deployed in the Amazon Web Services (AWS) cloud, and a NoSQL solution by deploying DynamoDB in the AWS cloud. Explore definitions of big data and governance. Learners will examine various types of data architecture, including TOGAF (The Open Group Architecture Framework) enterprise architecture. Finally, learners study data analytics and reporting, how organizations can derive value from data they have. The concluding exercise looks at implementing effective data management solutions.

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.

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.