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Exam Ref AZ-304 Microsoft Azure Architect Design Certification and Beyond

You're reading from   Exam Ref AZ-304 Microsoft Azure Architect Design Certification and Beyond Design secure and reliable solutions for the real world in Microsoft Azure

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781800566934
Length 520 pages
Edition 1st Edition
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Author (1):
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Brett Hargreaves Brett Hargreaves
Author Profile Icon Brett Hargreaves
Brett Hargreaves
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Table of Contents (30) Chapters Close

Preface 1. Section 1: Exploring Modern Architecture
2. Chapter 1: Architecture for the Cloud FREE CHAPTER 3. Chapter 2: Principles of Modern Architecture 4. Section 2: Identity and Security
5. Chapter 3: Understanding User Authentication 6. Chapter 4: Managing User Authorization 7. Chapter 5: Ensuring Platform Governance 8. Chapter 6: Building Application Security 9. Section 3: Infrastructure and Storage Components
10. Chapter 7: Designing Compute Solutions 11. Chapter 8: Network Connectivity and Security 12. Chapter 9: Exploring Storage Solutions 13. Chapter 10: Migrating Workloads to Azure 14. Section 4: Applications and Databases
15. Chapter 11: Comparing Application Components 16. Chapter 12: Creating Scalable and Secure Databases 17. Chapter 13: Options for Data Integration 18. Chapter 14: High Availability and Redundancy Concepts 19. Section 5: Operations and Monitoring
20. Chapter 15: Designing for Logging and Monitoring 21. Chapter 16: Developing Business Continuity 22. Chapter 17: Scripted Deployments and DevOps Automation 23. Section 6: Beyond the Exam
24. Chapter 18: Engaging with Real-World Customers 25. Chapter 19: Enterprise Design Considerations 26. Mock Exam
27. Mock Answers
28. Assessments 29. Other Books You May Enjoy

Chapter 13

The first aspect to consider for a data analytics solution is where data will be imported from. The sales data is already being stored in a database that can be interrogated directly. The marketing data is being stored as CSV files, and therefore ADLS Gen2 would make an excellent choice for holding these files.

Next, Azure Data Factory can ingest and combine the data into a single output, which can then be stored back in the ADLS.

Finally, Azure Databricks could be the best option for modeling and analyzing the data using optimized Spark clusters. Azure Databricks also supports the latest version of Spark.

The following diagram shows what this might look like:

Example data pipeline using multiple technologies

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