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Data Engineering Best Practices

Data Engineering Best Practices

By : Richard J. Schiller, David Larochelle
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Data Engineering Best Practices

Data Engineering Best Practices

5 (2)
By: Richard J. Schiller, David Larochelle

Overview of this book

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.
Table of Contents (21 chapters)
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Summary

Information Technology has evolved a lot since machine learning and artificial intelligence have replaced natural language processing as effective approaches to handling unstructured human communications. The real world is unstructured, and the task of IT is to bring order to confusion. With cloud computing comes the commodification of resources that were unheard of 25 years ago. With the future advent of quantum computing, the previous pattern of software, processes, and algorithms preceding the advent of hardware is again playing out. When that key processing capability comes online, you can expect the best practices and vision that are aligned with that future to remain effective for a long time.

Since many MLOps life cycle best practices apply lessons learned from DevOps, you can expect these processes to become more unified in time. As with many cutting-edge technologies, you will see a bloom of third-party vendor offerings, open source solutions, and cloud provider...

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