Richard J. Schiller is a Chief Architect, Distinguished Engineer, and startup entrepreneur with 40 years of experience in real-time large-scale data processing systems. He has contributed to two successful startups, Data Treasury and Dataminr, coauthored patents for both, and developed innovative cloud-first architectures at Elsevier and The NPD Group. His career spans diverse domains including financial, social media, health, chemistry, and retail, always maintaining a hands-on approach to IT systems development. Richard holds a Master's degree in Computer Engineering from Columbia University and a BA in Computer Engineering with a minor in Applied Mathematics from Queens College. 

You can read my postings on Medium or learn more from my suggested Reading List 

IT Consulting Services

• Enterprise Architecture: Financial Services Software, Social Media Analytics

• Data Architecture: Knowledge Engineering; Application Architecture: Machine Learning, Artificial Intelligence

• Infrastructure Architecture: AWS/Azure Cloud Platform Design, Agile Design Practices, Cloud Patterns / Blueprints. 

Author of the book entitled: “Data Engineering Best Practices”

Overview

This book is available through Packt Publishing (ISBN: 978-1803244983) on October 11, 2024 via international sales channels.  In it you can 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

• 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. The book 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. 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.

Learning Contents

• Architect scalable data solutions within a well-architected framework

• Implement agile software development processes tailored to your organization's needs

• Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products

• Optimize data engineering capabilities to ensure performance and long-term business value

• Apply best practices for data security, privacy, and compliance

• Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelines

This book is for ... 

Data engineers, ETL developers, or big data engineers who wants to master the principles and techniques of data engineering. 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.