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

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Data Engineering Best Practices: Architect robust and cost-effective data solutions in the cloud era by Richard J. Schiller, David Larochelle
English | October 11, 2024 | ISBN: 1803244984 | 550 pages | EPUB | 8.76 Mb
Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms

Key FeaturesArchitect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectivenessExplore design patterns and use cases to balance roles, technology choices, and processes for a future-proof designLearn from experts to avoid common pitfalls in data engineering projectsPurchase of the print or Kindle book includes a free PDF eBookBook Description
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.
What you will learnArchitect scalable data solutions within a well-architected frameworkImplement agile software development processes tailored to your organization's needsDesign cloud-based data pipelines for analytics, machine learning, and AI-ready data productsOptimize data engineering capabilities to ensure performance and long-term business valueApply best practices for data security, privacy, and complianceHarness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelinesWho this book is for
If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines.
Table of ContentsOverview of the Business Problem StatementA Data Engineer's Journey - Background ChallengesA Data Engineer's Journey - IT's Vision and MissionArchitecture PrinciplesArchitecture Framework - Conceptual Architecture Best PracticesArchitecture Framework - Logical Architecture Best PracticesArchitecture Framework - Physical Architecture Best PracticesSoftware Engineering Best Practice ConsiderationsKey Considerations for Agile SDLC Best PracticesKey Considerations for Quality Testing Best PracticesKey Considerations for IT Operational Service Best Practices(N.B. Please use the Read Sample option to see further chapters)

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