Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

by Martin Kleppmann

In this chapter we discussed new approaches to designing data systems, and I included my personal opinions and speculations about the future. We started with the observation that there is no one single tool that can efficiently serve all possible use cases, and so applications necessarily need to compose several different pieces of software to accomplish their goals. We discussed how to solve this data integration problem by using batch processing and event streams to let data changes flow between different systems. In this approach, certain systems are designated as systems of record, and other data is derived from them through transformations. In this way we can maintain indexes, materialized views, machine learning models, statistical summaries, and more. By making these derivations and transformations asynchronous and loosely coupled, a problem in one area is prevented from spreading to unrelated parts of the system, increasing the robustness and fault-tolerance of the system as a whole. Expressing dataflows as transformations from one dataset to another also helps evolve applications: if you want to change one of the processing steps, for example to change the structure of an index or cache, you can just rerun the new transformation code on the whole input dataset in order to rederive the output. Similarly, if something goes wrong, you can fix the code and reprocess the data in order to recover. These processes are quite similar to what databases already do internally...