Overview: Why System Design Books Still Matter in 2025

System design has become a core skill for software engineers, tech leads, and architects, especially as distributed systems, cloud-native architectures, and AI-driven services dominate the industry in 2025. Among dozens of titles, two books consistently stand out as foundational: “Designing Data‑Intensive Applications” by Martin Kleppmann and “Designing Distributed Systems” by Brendan Burns. This article summarizes the key ideas in each, compares their strengths, and explains how you can use them together to improve your system design skills for interviews and real-world engineering.

You will learn:

  • The main concepts and structure of each book
  • What problems each book helps you solve
  • How relevant the content still is as of December 2025
  • How to choose which one to read first (or how to combine both)

Whether you are preparing for system design interviews or architecting large-scale services, these two books can serve as your long-term reference and mental toolkit.

Book cover of Designing Data-Intensive Applications by Martin Kleppmann
Designing Data‑Intensive Applications is widely regarded as the single most important modern book on system design and data architecture.

1. “Designing Data‑Intensive Applications” (Martin Kleppmann)

First published in 2017 and still highly relevant in 2025, “Designing Data‑Intensive Applications” (DDIA) is often called “the bible of distributed systems and data engineering.” Rather than focusing on one technology stack, Kleppmann explains fundamental principles of how to store, process, replicate, and scale data in modern distributed systems.

1.1. Core Focus and Scope

DDIA focuses on three major themes:

  1. Foundations of data systems – data models, storage engines, and how databases work internally.
  2. Distributed data – replication, partitioning, consensus, and how to keep systems reliable at scale.
  3. Derived data and streaming – batch processing, stream processing, and building data pipelines.

While the examples reference technologies like Kafka, MapReduce, and NoSQL databases, the book is written to be technology-agnostic, so the ideas remain useful even as specific tools evolve in 2025.

1.2. Structure and Key Chapters

DDIA is organized into three parts:

  • Part I – Foundations of Data Systems
    Covers data models (relational, document, graph), storage engines, indexing, and encoding. You learn how databases actually store and retrieve information and why certain queries are fast or slow.
  • Part II – Distributed Data
    Explores replication, partitioning/sharding, transactions, consistency models, and consensus algorithms like Raft and Paxos. This part informs how you design for fault tolerance and scalability.
  • Part III – Derived Data
    Looks at batch processing, stream processing, and dataflow systems—how data pipelines transform raw data into usable, queryable formats for analytics or user-facing features.
DDIA’s greatest strength is that it does not teach you “how to use Database X” but “how to think about any distributed data system,” which keeps it timeless.

1.3. Key Concepts You Will Learn

  • The trade-offs between consistency, availability, and latency
  • How replication strategies affect read/write patterns and failover
  • Different partitioning strategies and their impact on system design
  • What exactly “transactions,” “serializability,” and “isolation” mean in practice
  • How stream processing differs from batch, and when to use each
  • How to design event-driven architectures and data pipelines

1.4. Strengths of DDIA for Modern Engineers

As of 2025, DDIA remains one of the most recommended books for:

  • Backend engineers designing APIs and microservices that rely on databases and queues.
  • Data engineers and platform teams building data lakes, streaming pipelines, and event-based systems.
  • Interview preparation for senior backend or system design roles, because many interview problems are grounded in DDIA concepts.

Its only significant drawback is that it can feel dense for beginners. However, if you take it slowly, chapter by chapter, it can effectively become your personal textbook and reference manual for years.


2. “Designing Distributed Systems” (Brendan Burns)

“Designing Distributed Systems: Patterns and Paradigms for Scalable, Reliable Services” by Brendan Burns (a co‑founder of Kubernetes) takes a more pattern-oriented and practical look at building distributed systems, especially in containerized and cloud-native environments.

Where DDIA is conceptual and data-centric, Burns’s book is pattern-centric and deployment-focused, with examples grounded in Kubernetes and modern cloud platforms. Even in 2025—after many Kubernetes and cloud releases—its core patterns remain valid and widely used.

Designing Distributed Systems focuses on reusable patterns for cloud-native and container-based architectures.

2.1. Core Focus and Scope

The book centers on reusable design patterns for distributed systems, such as:

  • Sidecar pattern
  • Ambassador pattern
  • Leader election
  • Scatter–gather
  • Work queues and event-driven processing

Burns shows how these patterns can be implemented using containers, orchestrators (especially Kubernetes), and typical cloud services. The target audience is software engineers who want to make real systems more reliable and operable, not just pass interviews.

2.2. Structure and Main Sections

The book is typically divided into pattern-focused chapters, including:

  • Foundational patterns – sidecars, ambassadors, and adaptors that help you structure services and common runtime functionality.
  • Workload patterns – batch processing, event-driven workloads, and pipelines for background processing.
  • Data and coordination patterns – leader election, sharding, and distributed coordination techniques.

Each pattern is accompanied by diagrams and code-oriented examples, often assuming you are deploying to Kubernetes or a similar platform. Although tools have matured in 2025, these patterns still map cleanly to today’s service meshes, operators, and managed cloud services.

2.3. Key Concepts You Will Learn

  • How to break functionality into sidecars instead of bloated monolith services
  • Ways to abstract communication with external systems using ambassador services
  • Patterns for running background jobs, queues, and workers in container-based clusters
  • Strategies for achieving fault tolerance and self-healing deployments
  • Using leader election and coordination to avoid conflicts or double-processing

2.4. Strengths of the Book in 2025

In 2025, most large organizations and many startups run their production systems in cloud-native environments. Designing Distributed Systems remains particularly valuable if you:

  • Work with Kubernetes, Docker, or similar container orchestrators
  • Need to design services that are easy to deploy, scale, and monitor
  • Care about operability and resilience as much as you care about data models

Compared with DDIA, this book reads faster and feels more practical, but it does not go as deep into theoretical trade-offs around data consistency, replication, and databases.


3. Comparing the Two: When to Use Each Book

Both books are widely respected, but they address different layers of system design. Understanding the contrast helps you decide which one to prioritize and how to use them together.

3.1. Conceptual vs. Pattern-Oriented

Aspect Designing Data‑Intensive Applications Designing Distributed Systems
Primary focus Data systems, consistency, storage, streaming Service patterns, deployment, cloud-native architectures
Style Theoretical + conceptual, deep explanations Pattern catalog, practical with diagrams
Best for Architects, backend and data engineers Cloud-native developers, DevOps, SREs
Difficulty level Intermediate to advanced Beginner to intermediate

3.2. Which Should You Read First?

Your choice depends on your current role and goals:

  • If you are preparing for system design interviews: Start with Designing Data‑Intensive Applications. Its explanations of consistency, replication, and data models align very closely with typical interview questions.
  • If you are building or operating Kubernetes-based systems: Start with Designing Distributed Systems to quickly adopt proven service patterns and understand how to deploy and run them effectively.
  • If you are early in your career: Read Designing Distributed Systems first for intuition and practical patterns, then tackle DDIA to deepen your theoretical grounding.

In practice, many engineers eventually read both, using DDIA as a long-term reference on data systems and Burns’s book as a catalog of implementation patterns.


4. Are These Books Still Up to Date in 2025?

Even as of December 2025, with advances in serverless computing, AI infrastructure, and managed databases, the core ideas in both books remain highly relevant because they address fundamental trade-offs rather than specific product features.

4.1. Lasting Value of “Designing Data‑Intensive Applications”

  • Concepts like consistency models, replication strategies, and partitioning still underpin modern distributed databases, whether self-hosted or managed.
  • Event streaming, event sourcing, and log-based architectures are even more important as organizations adopt real-time analytics and ML feature stores.
  • Newer tools (e.g., cloud-native data warehouses, serverless databases) still implement the same core principles explained in the book.

4.2. Lasting Value of “Designing Distributed Systems”

  • Sidecar and ambassador patterns are baked into modern service meshes and observability stacks.
  • Work queue and event-driven patterns map directly to serverless functions, message queues, and event buses offered by major cloud providers.
  • Kubernetes remains a dominant orchestration platform; understanding these patterns simplifies working with operators, autoscaling, and multi-cluster deployments.

While some screenshots and specific tools may look slightly dated, the main ideas apply cleanly to the latest ecosystem in 2025.


5. How to Study These System Design Books Effectively

Both books reward active reading. Instead of consuming them passively, treat them as handbooks and project guides.

5.1. Learning Plan for “Designing Data‑Intensive Applications”

  1. Read Part I with a focus on understanding how your current databases actually behave. Compare what you read with the systems you use at work.
  2. In Part II, pause after each chapter to draw diagrams of how you would replicate and shard a real service you know.
  3. In Part III, build a small project that uses a message queue or streaming platform to solidify concepts.
  4. Keep a glossary of terms (e.g., “linearizability,” “idempotence,” “exactly-once”) and revisit it during interview prep.

5.2. Learning Plan for “Designing Distributed Systems”

  1. Start by skimming all patterns, then choose 2–3 that match problems you face today (e.g., logging, metrics, background jobs).
  2. Implement a simple example of the sidecar and ambassador patterns in a small Kubernetes cluster or local environment.
  3. Use the book as a reference when you encounter reliability or scaling issues: check whether a pattern directly addresses your scenario.
  4. Share diagrams and patterns from the book with your team during design reviews to create a shared language for discussing architecture.

6. Conclusion: Building a Strong System Design Foundation

If you are serious about system design in 2025, “Designing Data‑Intensive Applications” and “Designing Distributed Systems” together form a powerful foundation. The first teaches you how data moves, is stored, and is processed in large-scale systems; the second shows you reusable architectural patterns for deploying and operating those systems in the cloud.

By internalizing the principles and patterns from these two books, you will be better equipped to:

  • Design scalable, reliable backends and data platforms
  • Communicate your ideas clearly in system design interviews
  • Make informed trade-offs about consistency, availability, and performance
  • Collaborate effectively with architects, SREs, and platform teams

Treat these books as living references. Revisit them as your responsibilities grow, and you will continue to uncover deeper insights that help you design better systems in an ever-evolving technological landscape.