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AWS Certified Database – Specialty (DBS-C01) Certification Guide

You're reading from   AWS Certified Database – Specialty (DBS-C01) Certification Guide A comprehensive guide to becoming an AWS Certified Database specialist

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Product type Paperback
Published in May 2022
Publisher Packt
ISBN-13 9781803243108
Length 472 pages
Edition 1st Edition
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Author (1):
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Kate Gawron Kate Gawron
Author Profile Icon Kate Gawron
Kate Gawron
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Table of Contents (24) Chapters Close

Preface 1. Part 1: Introduction to Databases on AWS
2. Chapter 1: AWS Certified Database – Specialty Overview FREE CHAPTER 3. Chapter 2: Understanding Database Fundamentals 4. Chapter 3: Understanding AWS Infrastructure 5. Part 2: Workload-Specific Database Design
6. Chapter 4: Relational Database Service 7. Chapter 5: Amazon Aurora 8. Chapter 6: Amazon DynamoDB 9. Chapter 7: Redshift and DocumentDB 10. Chapter 8: Neptune, Quantum Ledger Database, and Timestream 11. Chapter 9: Amazon ElastiCache 12. Part 3: Deployment and Migration and Database Security
13. Chapter 10: The AWS Schema Conversion Tool and AWS Database Migration Service 14. Chapter 11: Database Task Automation 15. Chapter 12: AWS Database Security 16. Part 4: Monitoring and Optimization
17. Chapter 13: CloudWatch and Logging 18. Chapter 14: Backup and Restore 19. Chapter 15: Troubleshooting Tools and Techniques 20. Part 5: Assessment
21. Chapter 16: Exam Practice
22. Chapter 17: Answers 23. Other Books You May Enjoy

Working with Neptune

One of the first things to understand about graph databases is how they store data and, specifically, how Neptune stores data. Unlike RDBMS and some NoSQL systems (such as DynamoDB), graph databases do not use Structured Query Language (SQL) for querying. Instead, Neptune supports two different graph query languages: Gremlin and SPARQL Protocol and RDF Query Language (SPARQL). You can only use one language at a time in your database, and each language has its own requirements for how the data will be stored within Neptune and how you can utilize it. If you use Gremlin, the data stored will be using the Property Graph data framework, and if you choose SPARQL, you will be using the Resource Description Framework (RDF). SPARQL looks similar to SQL with SELECT and INSERT statements, but has some major differences with how it handles WHERE clauses and the syntax. Gremlin will appear unfamiliar to database administrators (DBAs) as it uses a structure more similar to...

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