Dmitry Foshin

As a co-author of the Azure Data Factory Cookbook, I contributed to creating a comprehensive, hands-on guide for data engineers and architects working with Microsoft's cloud data integration service. The book provides practical solutions, real-world use cases, and step-by-step recipes for building efficient data pipelines using Azure Data Factory (ADF). I also shared insights from my experience in production environments, helping readers understand how to implement features and why and when to use them. This project reflects my commitment to knowledge sharing and my deep expertise in building scalable, cloud-based data solutions.

As a co-author of the Azure Data Factory Cookbook, 2nd edition, I contributed to delivering a practical, up-to-date guide tailored for modern data engineers and architects working in the Azure ecosystem. This edition goes beyond the fundamentals to cover advanced use cases, including configuring and running data flows with Synapse, integrating ADF with Azure ML Studio, Azure Logic Apps, and Azure Functions.

I authored and technically reviewed chapters focused on building scalable big data pipelines using Databricks and Delta tables, implementing industry-grade best practices, and integrating metadata-driven approaches with Azure Purview. My role involved hands-on development, scenario design, and knowledge sharing from real-world implementations, ensuring the content was actionable and aligned with current enterprise data needs. This project underscores my dedication to empowering data professionals with the tools and insights to build reliable, cloud-native data platforms using Azure technologies.

As a co-author of Jumpstart Snowflake, 2nd edition, I contributed to expanding and refining a comprehensive guide that helps data professionals adopt and master Snowflake, one of the most advanced cloud-native data warehouse platforms. With organizations increasingly moving their analytics to cloud providers like AWS, Azure, and GCP, this book equips readers with the knowledge to build modern, scalable analytics solutions using Snowflake.

I focused on real-world deployment best practices, cloud migration strategies from legacy data warehouses, and integration with tools like Matillion, Tableau, and Databricks. In this new edition, I also helped extend coverage to include cutting-edge features such as Snowpark for complex application development, Apache Iceberg for managing large datasets, and Streamlit for building interactive data apps. My contributions reflect deep hands-on experience and a practical approach to helping readers unlock the full potential of Snowflake in enterprise data platforms.