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Data management involves a variety of processes and practices to collect, organize, store, and deliver high-quality data for data science, business intelligence, performance management, and business operations. Data engineering is an essential discipline that is responsible to design, build, and deploy data management capabilities. The data engineering discipline encompasses three distinct roles: database engineer, data pipeline engineer, and data product engineer.
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This course focuses on the role of database engineer – the technical professional who designs, builds, and deploys databases. The database engineering skill set includes requirements analysis, data modeling, logical and physical database design, and database implementation across a broad spectrum of technologies and topologies. Database technologies include relational, object, graph, document, and more. Infrastructure technologies span on-premises, cloud, multi-cloud, and edge platforms. Topologies include transactional, data warehouse, data mart, and data lake databases.
On completion of this course you will have a solid foundation of knowledge and skills needed for database engineering.
You will learn:
- What is Database Engineering?
- The Database Engineering Lifecycle
- Components of Database Engineering
- Kinds of Database Engineering Projects
- The Database Design Process
- Database Implementation
This course is geared towards:
- Technology students and aspiring database engineers
- Data engineers, data scientists, data analysts, and data architects
- Data warehouse and data lake engineers
Database Engineering Course Outline
Module 0 – About the Course (2 min) Module 1 – Introduction to Database Engineering (25 min)
- What is Data Engineering?
- Data Engineer Types
Module 2 – Database Engineering Lifecycle (78 min)
- Data Modeling as Related to Database Engineering
- Tools
- System Requirements
- Technical Requirements & Physical Modeling
- Physical Implementation
- Security Implementation and Management
- Managing Growth
- Performance Monitoring and Tuning
Module 3 – Components of Database Engineering (52 min)
- Relational Database Models
- Multi-Dimensional Database Models
- NoSQL Data Models
- Object Models
- Graph Models
- Relational Database
- Key-Value Database
- Wide Column Database
- Document Store
- Graph Database
- Object Store
Module 4 – Data Engineering Projects (40 min)
- What are the components of a Data Engineering Project
- What roles are needed for Data Engineering Projects
- Database Engineering Project Examples
Module 5 – Database Design Process (73 min)
- Business Requirements
- Technical Requirements
- Performance & Scalability Requirements
- Security & Compliance Requirements
- Read & Write Patterns & Frequency
- Technical Specifications
- Cost Estimation & ROI Analysis
- Physical Database Design
- Platform Selection
Module 6 – Database Implementation (22 min)
- On-Premises Implementation
- Off-Premises Implementation
Module 7 – Case Study Data Mart Project (32 min) Module 8 – Conclusions & Next Steps (11 min) - Conclusions
- Opportunities
- Next Steps
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This exam tests knowledge and understanding of basic concepts, principles, and terminology of analytics.
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You will be tested in these areas:
- Database engineering definitions and concepts
- The database engineering lifecycle
- Components of database engineering
- Types of database engineering projects
- Elements of the database engineering design process
- Database implementation
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Additional Information
Number of Questions: 25
Time Limit: 50 minutes
Passing Score: 70%
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Once you pass the exam, you will receive a Certificate of Education
documenting that you have demonstrated mastery of the topic. Course
exams count towards eLC certification programs. Visit our
Certification page for more information about our various programs.
We recommend that you take detailed notes and review the course material multiple times before taking this exam.
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