Data Engineering Best Practices Richard Larochelle Schiller David by Richard. Larochelle Schiller (david.), Richard J Schiller, David Larochelle 9781803244983, 1803244984 instant download after payment.

Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms Key Features: - Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness - Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design - Learn from experts to avoid common pitfalls in data engineering projects - Purchase of the print or Kindle book includes a free PDF eBook Book 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 Learn: - Architect scalable data solutions within a well-a