Revolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines.
You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications.
By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.

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Spis treści

Data Engineering Best Practices. Architect robust and cost-effective data solutions in the cloud era eBook -- spis treści

• 1. Overview of the Business Problem Statement
• 2. A Data Engineer's Journey – Background Challenges
• 3. A Data Engineer's Journey – IT's Vision and Mission
• 4. Architecture Principles
• 5. Architecture Framework – Conceptual Architecture Best Practices
• 6. Architecture Framework – Logical Architecture Best Practices
• 7. Architecture Framework – Physical Architecture Best Practices
• 8. Software Engineering Best Practice Considerations
• 9. Key Considerations for Agile SDLC Best Practices
• 10. Key Considerations for Quality Testing Best Practices
• 11. Key Considerations for IT Operational Service Best Practices
• 12. Key Considerations for Data Service Best Practices
• 13. Key Considerations for Management Best Practices
• 14. Key Considerations for Data Delivery Best Practices
• 15. Other Considerations – Measures, Calculations, Restatements, and Data Science Best Practices
• 16. Machine Learning Pipeline Best Practices and Processes
• 17. Takeaway Summary – Putting It All Together
• 18. Appendix and Use Cases