# 12. The Future of Data Systems
> *If a thing be ordained to another as to its end, its last end cannot consist in the preservation of its being. Hence a captain does not intend as a last end, the preservation of the ship entrusted to him, since a ship is ordained to something else as its end, viz. to navigation.*
>
> *(Often quoted as: If the highest aim of a captain was the preserve his ship, he would keep it in port forever.)*
>
> — St. Thomas Aquinas, *Summa Theologica* (1265–1274)
---------------
So far, this book has been mostly about describing things as they *are* at present. In this final chapter, we will shift our perspective toward the future and discuss how things *should be*: I will propose some ideas and approaches that, I believe, may funda‐ mentally improve the ways we design and build applications.
Opinions and speculation about the future are of course subjective, and so I will use the first person in this chapter when writing about my personal opinions. You are welcome to disagree with them and form your own opinions, but I hope that the ideas in this chapter will at least be a starting point for a productive discussion and bring some clarity to concepts that are often confused.
The goal of this book was outlined in [Chapter 1](ch1.md): to explore how to create applications and systems that are *reliable*, *scalable*, and *maintainable*. These themes have run through all of the chapters: for example, we discussed many fault-tolerance algo‐ rithms that help improve reliability, partitioning to improve scalability, and mecha‐ nisms for evolution and abstraction that improve maintainability. In this chapter we will bring all of these ideas together, and build on them to envisage the future. Our goal is to discover how to design applications that are better than the ones of today— robust, correct, evolvable, and ultimately beneficial to humanity.
## ……
## Summary
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 some‐ thing 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, so we recast the idea of dataflow applications as *unbundling* the components of a database, and building an application by composing these loosely coupled components.
Derived state can be updated by observing changes in the underlying data. Moreover, the derived state itself can further be observed by downstream consumers. We can even take this dataflow all the way through to the end-user device that is displaying the data, and thus build user interfaces that dynamically update to reflect data changes and continue to work offline.
Next, we discussed how to ensure that all of this processing remains correct in the presence of faults. We saw that strong integrity guarantees can be implemented scala‐ bly with asynchronous event processing, by using end-to-end operation identifiers to make operations idempotent and by checking constraints asynchronously. Clients can either wait until the check has passed, or go ahead without waiting but risk hav‐ ing to apologize about a constraint violation. This approach is much more scalable and robust than the traditional approach of using distributed transactions, and fits with how many business processes work in practice.
By structuring applications around dataflow and checking constraints asynchro‐ nously, we can avoid most coordination and create systems that maintain integrity but still perform well, even in geographically distributed scenarios and in the pres‐ ence of faults. We then talked a little about using audits to verify the integrity of data and detect corruption.
Finally, we took a step back and examined some ethical aspects of building data- intensive applications. We saw that although data can be used to do good, it can also do significant harm: making justifying decisions that seriously affect people’s lives and are difficult to appeal against, leading to discrimination and exploitation, nor‐ malizing surveillance, and exposing intimate information. We also run the risk of data breaches, and we may find that a well-intentioned use of data has unintended consequences.
As software and data are having such a large impact on the world, we engineers must remember that we carry a responsibility to work toward the kind of world that we want to live in: a world that treats people with humanity and respect. I hope that we can work together toward that goal.
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1. J. M. Porup: “[‘Internet of Things’ Security Is Hilariously Broken and Getting Worse](http://arstechnica.com/security/2016/01/how-to-search-the-internet-of-things-for-photos-of-sleeping-babies/),” *arstechnica.com*, January 23, 2016.
1. Bruce Schneier: *Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World*. W. W. Norton, 2015. ISBN: 978-0-393-35217-7
1. The Grugq: “[Nothing to Hide](https://grugq.tumblr.com/post/142799983558/nothing-to-hide),” *grugq.tumblr.com*, April 15, 2016.
1. Tony Beltramelli: “[Deep-Spying: Spying Using Smartwatch and Deep Learning](https://arxiv.org/abs/1512.05616),” Masters Thesis, IT University of Copenhagen, December 2015. Available at *arxiv.org/abs/1512.05616*
1. Shoshana Zuboff: “[Big Other: Surveillance Capitalism and the Prospects of an Information Civilization](http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2594754),” *Journal of Information Technology*, volume 30, number 1, pages 75–89, April 2015.[doi:10.1057/jit.2015.5](http://dx.doi.org/10.1057/jit.2015.5)
1. Carina C. Zona: “[Consequences of an Insightful Algorithm](https://www.youtube.com/watch?v=YRI40A4tyWU),” at *GOTO Berlin*, November 2016.
1. Bruce Schneier: “[Data Is a Toxic Asset, So Why Not Throw It Out?](https://www.schneier.com/essays/archives/2016/03/data_is_a_toxic_asse.html),” *schneier.com*, March 1, 2016.
1. John E. Dunn: “[The UK’s 15 Most Infamous Data Breaches](http://www.techworld.com/security/uks-most-infamous-data-breaches-2016-3604586/),” *techworld.com*, November 18, 2016.
1. Cory Scott: “[Data is not toxic - which implies no benefit - but rather hazardous material, where we must balance need vs. want](https://twitter.com/cory_scott/status/706586399483437056),” *twitter.com*, March 6, 2016.
1. Bruce Schneier: “[Mission Creep: When Everything Is Terrorism](https://www.schneier.com/essays/archives/2013/07/mission_creep_when_e.html),” *schneier.com*, July 16, 2013.
1. Lena Ulbricht and Maximilian von Grafenstein: “[Big Data: Big Power Shifts?](http://policyreview.info/articles/analysis/big-data-big-power-shifts),” *Internet Policy Review*, volume 5, number 1, March 2016. [doi:10.14763/2016.1.406](http://dx.doi.org/10.14763/2016.1.406)
1. Ellen P. Goodman and Julia Powles: “[Facebook and Google: Most Powerful and Secretive Empires We've Ever Known](https://www.theguardian.com/technology/2016/sep/28/google-facebook-powerful-secretive-empire-transparency),” *theguardian.com*, September 28, 2016.
1. [Directive 95/46/EC on the protection of individuals with regard to the processing of personal data and on the free movement of such data](http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:31995L0046), Official Journal of the European Communities No. L 281/31,
*eur-lex.europa.eu*, November 1995.
1. Brendan Van Alsenoy: “[Regulating Data Protection: The Allocation of Responsibility and Risk Among Actors Involved in Personal Data Processing](https://lirias.kuleuven.be/handle/123456789/545027),” Thesis, KU Leuven Centre for IT and IP Law, August 2016.
1. Michiel Rhoen: “[Beyond Consent: Improving Data Protection Through Consumer Protection Law](http://policyreview.info/articles/analysis/beyond-consent-improving-data-protection-through-consumer-protection-law),” *Internet Policy Review*, volume 5, number 1, March 2016. [doi:10.14763/2016.1.404](http://dx.doi.org/10.14763/2016.1.404)
1. Jessica Leber: “[Your Data Footprint Is Affecting Your Life in Ways You Can’t Even Imagine](https://www.fastcoexist.com/3057514/your-data-footprint-is-affecting-your-life-in-ways-you-cant-even-imagine),” *fastcoexist.com*, March 15, 2016.
1. Maciej Cegłowski: “[Haunted by Data](http://idlewords.com/talks/haunted_by_data.htm),” *idlewords.com*, October 2015.
1. Sam Thielman: “[You Are Not What You Read: Librarians Purge User Data to Protect Privacy](https://www.theguardian.com/us-news/2016/jan/13/us-library-records-purged-data-privacy),” *theguardian.com*, January 13, 2016.
1. Conor Friedersdorf: “[Edward Snowden’s Other Motive for Leaking](http://www.theatlantic.com/politics/archive/2014/05/edward-snowdens-other-motive-for-leaking/370068/),” *theatlantic.com*, May 13, 2014.
1. Phillip Rogaway: “[The Moral Character of Cryptographic Work](http://web.cs.ucdavis.edu/~rogaway/papers/moral-fn.pdf),” Cryptology ePrint 2015/1162, December 2015.