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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 Richard J. Schiller, David Larochelle - okladka książki

Data Engineering Best Practices. Architect robust and cost-effective data solutions in the cloud era Richard J. Schiller, David Larochelle - okladka książki

Serie wydawnicze:
Hands-on
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
550
Dostępne formaty:
     PDF
     ePub

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Do przechowalni

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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O autorach książki

Richard J. Schiller is a chief architect, distinguished engineer, and startup entrepreneur with 40 years of
experience delivering real-time large-scale data processing systems. He holds an MS in computer engineering
from Columbia University's School of Engineering and Applied Science and a BA in computer science
and applied mathematics. He has been involved with two prior successful startups and has coauthored
three patents. He is a hands-on systems developer and innovator.
David Larochelle is a highly experienced data engineer with over 20 years in the industry, having built systems for startups, Fortune 100 companies, and research institutes. He focuses on solving problems by integrating tools from different areas in innovative ways. David holds a BS in Computer Science from the College of William & Mary, a Master's in Computer Science from the University of Virginia, and a Master's in Communication from the University of Pennsylvania.

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