Book Report: Designing Data-Intensive Applications

2 min readJul 18, 2023

This is a summary of the book — Design Data Intensive Application by Martin Kleppmann. Below are the links to each chapter so as to keep it short and simple to read.

Part I. Foundations of Data Systems

Chapter 1: Reliable, Scalable, and Maintainable Applications

We call an application data-intensive if data is its primary challenge — the quantity of data, the complexity of data…

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Chapter 2: Data Models and Query Languages

Data models are perhaps the most important part of developing software, because they have such a profound effect: not…

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Chapter 3: Storage and Retrieval (Part 1)

On the most fundamental level, a database needs to do two things: when you give it some data, it should store the data…

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Chapter 3: Storage and Retrieval (Part 2)

Transaction Processing or Analytics?

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Chapter 4: Encoding and Evolution (Part 1)

Applications inevitably change over time. Features are added or modified as new products are launched, user…

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Chapter 4: Encoding and Evolution (Part 2)

There are many ways data can flow from one process to another. Who encodes the data, and who decodes it? In the rest of…

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Part II. Distributed Data

Chapter 5: Replication

Replication means keeping a copy of the same data on multiple machines that are connected via a network. There are…

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Chapter 6: Partitioning

In Chapter 5 we discussed replication — that is, having multiple copies of the same data on different nodes. For very…

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Chapter 7: Transactions

In the harsh reality of data systems, many things can go wrong:

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Chapter 8: The Trouble with Distributed Systems

A recurring theme in the last few chapters has been how systems handle things going wrong. For example, we discussed…

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Chapter 9: Consistency and Consensus

In this chapter, we will talk about some examples of algorithms and protocols for building fault-tolerant distributed…

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Part III. Derived Data

Chapter 10: Batch Processing

On a high level, systems that store and process data can be grouped into two broad categories:

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Chapter 11: Stream processing

One big assumption we made throughout Chapter 10 was that the input is bounded — i.e., of a known and finite size — so…

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Chapter 12: The Future of Data Systems

So far, this book has been mostly about describing things as they are at present. In this final chapter, we will shift…

aditilonhari.medium.com

Book Report: Designing Data-Intensive Applications

Part I. Foundations of Data Systems

Chapter 1: Reliable, Scalable, and Maintainable Applications

We call an application data-intensive if data is its primary challenge — the quantity of data, the complexity of data…

Chapter 2: Data Models and Query Languages

Data models are perhaps the most important part of developing software, because they have such a profound effect: not…

Chapter 3: Storage and Retrieval (Part 1)

On the most fundamental level, a database needs to do two things: when you give it some data, it should store the data…

Chapter 3: Storage and Retrieval (Part 2)

Transaction Processing or Analytics?

Chapter 4: Encoding and Evolution (Part 1)

Applications inevitably change over time. Features are added or modified as new products are launched, user…

Chapter 4: Encoding and Evolution (Part 2)

There are many ways data can flow from one process to another. Who encodes the data, and who decodes it? In the rest of…

Part II. Distributed Data

Chapter 5: Replication

Replication means keeping a copy of the same data on multiple machines that are connected via a network. There are…

Chapter 6: Partitioning

In Chapter 5 we discussed replication — that is, having multiple copies of the same data on different nodes. For very…

Chapter 7: Transactions

In the harsh reality of data systems, many things can go wrong:

Chapter 8: The Trouble with Distributed Systems

A recurring theme in the last few chapters has been how systems handle things going wrong. For example, we discussed…

Chapter 9: Consistency and Consensus

In this chapter, we will talk about some examples of algorithms and protocols for building fault-tolerant distributed…

Part III. Derived Data

Chapter 10: Batch Processing

On a high level, systems that store and process data can be grouped into two broad categories:

Chapter 11: Stream processing

One big assumption we made throughout Chapter 10 was that the input is bounded — i.e., of a known and finite size — so…

Chapter 12: The Future of Data Systems

So far, this book has been mostly about describing things as they are at present. In this final chapter, we will shift…

Written by Aditi Lonhari