Data Engineering Best Practices: Architect robust and cost-effective data soluti

Description: Data Engineering Best Practices by David Larochelle, Richard J. Schiller Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platformsKey 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 DescriptionRevolutionize 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.Youll 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, youll 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, youll 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 organizations 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 forIf 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. FORMAT Paperback CONDITION Brand New Author Biography Richard J. Schiller is a chief architect, distinguished engineer, and startup entrepreneur with 40 years ofexperience delivering real-time large-scale data processing systems. He holds an MS in computer engineeringfrom Columbia Universitys School of Engineering and Applied Science and a BA in computer scienceand applied mathematics. He has been involved with two prior successful startups and has coauthoredthree patents. He is a hands-on systems developer and innovator. David Larochelle has been involved in data engineering for startups, Fortune 500 companies, andresearch institutes. He holds a BS in computer science from the College of William & Mary, a Masters incomputer science from the University of Virginia, and a Masters in communication from the Universityof Pennsylvania. Davids career spans over 20 years, and his strong background has enabled him to workin a wide range of organizations, including startups, established companies, and research labs. Table of Contents Table of ContentsOverview of the Business Problem StatementA Data Engineers Journey – Background ChallengesA Data Engineers Journey – ITs 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 PracticesKey Considerations for Data Service Best PracticesKey Considerations for Management Best PracticesKey Considerations for Data Delivery Best PracticesOther Considerations – Measures, Calculations, Restatements, and Data Science Best PracticesMachine Learning Pipeline Best Practices and ProcessesTakeaway Summary – Putting It All TogetherAppendix and Use Cases Details ISBN1803244984 Author Richard J. Schiller Publisher Packt Publishing Limited Year 2024 ISBN-13 9781803244983 Format Paperback Publication Date 2024-10-11 Imprint Packt Publishing Limited Subtitle Architect robust and cost-effective data solutions in the cloud era Place of Publication Birmingham Country of Publication United Kingdom Audience General UK Release Date 2024-10-11 Pages 550 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:161626512;

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