--- product_id: 447196017 title: "Fundamentals of Data Engineering: Plan and Build Robust Data Systems" price: "₹ 10599" currency: INR in_stock: true reviews_count: 13 url: https://www.desertcart.in/products/447196017-fundamentals-of-data-engineering-plan-and-build-robust-data-systems store_origin: IN region: India --- # Fundamentals of Data Engineering: Plan and Build Robust Data Systems **Price:** ₹ 10599 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** Fundamentals of Data Engineering: Plan and Build Robust Data Systems - **How much does it cost?** ₹ 10599 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.in](https://www.desertcart.in/products/447196017-fundamentals-of-data-engineering-plan-and-build-robust-data-systems) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description From the brand Sharing the knowledge of experts O'Reilly's mission is to change the world by sharing the knowledge of innovators. For over 40 years, we've inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success. Our customers are hungry to build the innovations that propel the world forward. And we help them do just that. Sharing the knowledge of experts O'Reilly's mission is to change the world by sharing the knowledge of innovators. For over 40 years, we've inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success. Our customers are hungry to build the innovations that propel the world forward. And we help them do just that. Your partner in learning AI / Machine Learning Software Development Data & Data Science Review: Great book - Great book. Using it on daily basis. Gives you a comprehensive understanding of data Engineering fundamentals. Review: A Great Overview of the Data Analytics Tech Landscape (for the Uninitiated) - This is a great book for anyone who has a solid understanding of software development and cloud architecture, but doesn't have direct experience building data pipelines or data analytics products. The authors don't get into much technical detail at a tactical level - this is not a book about actually implementing anything whatsoever. Rather, this book offers a really excellent 10,000 foot view of the current state of Data Engineering from multiple angles. Throughout the book they spend a lot of time explaining the "people" side of things (what developers and teams actually do when building Data Eng teams, analytics pipelines, etc.) and how they interact with various other teams and stakeholders (data scientists, analysts, PMs, execs,...). They also cover a vast amount of ground on the architectural side of things. As a developer with years of tech experience, but one which has never directly worked on data pipelines, I really enjoyed how they offered both numerous examples and stories of how projects were built and operated in the _ancient_ "big data" Hadoop era (i.e. 2010-2020, LOL!), and then how quickly the tech and related architectures have changed as significant new technologies came to the fore (i.e. Kafka, BigQuery/Athena, Snowflake/Databricks, etc...). My 2 constructive criticisms of this book are: 1) Some will be frustrated by the lack of tactical content or technical depth. That said, what they sacrifice in depth they make up for in scope. The data analytics space is vast, and evolving at a breakneck pace. They do an admirable job of introducing and summarizing a vast topic, all grounded in practical advice and real-world anecdotes and examples (from their own professional experience). 2) They have 1 surprising blind spot, imho – which is that they don't even offer a passing nod to Domain Driven Design (DDD). Given that they do discuss topics including microservices, data models, schemas, and some aspects of "domains" in the enterprise sense, as well as the need to interact with stakeholders and experts from various other teams (aka "domain experts"), this strikes me as a surprising blind spot. I'd like to see them explore DDD in a future 2nd edition (please!). Final word – If you're an experienced developer or architect with big data or analytics experience, this book may leave you wanting. For anyone else with a solid technical foundation and an interest in the data realm from almost any angle, this is a great read that's well worth your time. ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | 42,057 in Books ( See Top 100 in Books ) 4 in Data Mining (Books) 5 in Beginner's Guide to Databases 5 in Database Applications | | Customer reviews | 4.6 4.6 out of 5 stars (674) | | Dimensions | 17.78 x 2.54 x 23.5 cm | | Edition | 1st | | ISBN-10 | 1098108302 | | ISBN-13 | 978-1098108304 | | Item weight | 1.05 kg | | Language | English | | Print length | 400 pages | | Publication date | 5 July 2022 | | Publisher | O'Reilly Media | ## Images ![Fundamentals of Data Engineering: Plan and Build Robust Data Systems - Image 1](https://m.media-amazon.com/images/I/81+oMD7Lm7L.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Great book *by A***V on 28 February 2026* Great book. Using it on daily basis. Gives you a comprehensive understanding of data Engineering fundamentals. ### ⭐⭐⭐⭐⭐ A Great Overview of the Data Analytics Tech Landscape (for the Uninitiated) *by J***Y on 25 January 2025* This is a great book for anyone who has a solid understanding of software development and cloud architecture, but doesn't have direct experience building data pipelines or data analytics products. The authors don't get into much technical detail at a tactical level - this is not a book about actually implementing anything whatsoever. Rather, this book offers a really excellent 10,000 foot view of the current state of Data Engineering from multiple angles. Throughout the book they spend a lot of time explaining the "people" side of things (what developers and teams actually do when building Data Eng teams, analytics pipelines, etc.) and how they interact with various other teams and stakeholders (data scientists, analysts, PMs, execs,...). They also cover a vast amount of ground on the architectural side of things. As a developer with years of tech experience, but one which has never directly worked on data pipelines, I really enjoyed how they offered both numerous examples and stories of how projects were built and operated in the _ancient_ "big data" Hadoop era (i.e. 2010-2020, LOL!), and then how quickly the tech and related architectures have changed as significant new technologies came to the fore (i.e. Kafka, BigQuery/Athena, Snowflake/Databricks, etc...). My 2 constructive criticisms of this book are: 1) Some will be frustrated by the lack of tactical content or technical depth. That said, what they sacrifice in depth they make up for in scope. The data analytics space is vast, and evolving at a breakneck pace. They do an admirable job of introducing and summarizing a vast topic, all grounded in practical advice and real-world anecdotes and examples (from their own professional experience). 2) They have 1 surprising blind spot, imho – which is that they don't even offer a passing nod to Domain Driven Design (DDD). Given that they do discuss topics including microservices, data models, schemas, and some aspects of "domains" in the enterprise sense, as well as the need to interact with stakeholders and experts from various other teams (aka "domain experts"), this strikes me as a surprising blind spot. I'd like to see them explore DDD in a future 2nd edition (please!). Final word – If you're an experienced developer or architect with big data or analytics experience, this book may leave you wanting. For anyone else with a solid technical foundation and an interest in the data realm from almost any angle, this is a great read that's well worth your time. ### ⭐⭐⭐⭐⭐ Every data engineer needs to read this book *by V***I on 23 February 2025* Every data engineer needs to read this book. The book provides good guidance on the big picture of data engineering, from the source system, storage, ingestion, transformation, serving as well as security, data management, data operation, data architecture, orchestration. And unlike any other technical book this book is a good read! Meaning it's engaging, like in a dialog with the readers. It gets me to think of what is really important. Thank you Joe Reis and Matthew Housley for writing it. ## Frequently Bought Together - Fundamentals of Data Engineering: Plan and Build Robust Data Systems - Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems - Data Pipelines Pocket Reference: Moving and Processing Data for Analytics --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.in/products/447196017-fundamentals-of-data-engineering-plan-and-build-robust-data-systems](https://www.desertcart.in/products/447196017-fundamentals-of-data-engineering-plan-and-build-robust-data-systems) --- *Product available on Desertcart India* *Store origin: IN* *Last updated: 2026-05-21*