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Managing the Complexity of Processing Financial Data at Scale - An Experience Report

  • Sebastian FrischbierEmail author
  • Mario Paic
  • Alexander Echler
  • Christian Roth
Conference paper

Abstract

Financial markets are extremely data-driven and regulated. Participants rely on notifications about significant events and background information that meet their requirements regarding timeliness, accuracy, and completeness. As one of Europe’s leading providers of financial data and regulatory solutions vwd processes a daily average of 18 billion notifications from 500+ data sources for 30 million symbols. Our large-scale geo-distributed systems handle daily peak rates of 1+ million notifications/sec. In this paper we give practical insights about the different types of complexity we face regarding the data we process, the systems we operate, and the regulatory constraints we must comply with. We describe the volume, variety, velocity, and veracity of the data we process, the infrastructure we operate, and the architecture we apply. We illustrate the load patterns created by trading and how the markets’ attention to the Brexit vote and similar events stressed our systems.

Keywords

Financial data Big data Event-driven architecture Enterprise architecture Quality of information Infrastructure 

References

  1. 1.
    BaFin: Minimum requirements for risk management for banks. https://www.bafin.de/dok/11681598 (2018). Accessed 09 May 2019
  2. 2.
    Coenen, M., Wagner, C., Echler, A., Frischbier, S.: Benchmarking financial data feed systems. In: DEBS 2019, pp. 252–253. ACM (2019). https://doi.org/10.1145/3328905.3332506
  3. 3.
  4. 4.
    EBA: Final report on EBA guidelines on outsourcing arrangements. https://eba.europa.eu/documents/10180/2551996/EBA+revised+Guidelines+on+outsourcing+arrangements. Accessed 12 Feb 2019
  5. 5.
    Frischbier, S., Buchmann, A., Pütz, D.: FIT for SOA? Introducing the FIT-metric to optimize the availability of service oriented architectures. In: CSDM 2012, pp. 93–104. Springer (2012). https://doi.org/10.1007/978-3-642-25203-7_6CrossRefGoogle Scholar
  6. 6.
    Frischbier, S., Paic, M., Echler, A., Roth, C.: A real-world distributed infrastructure for processing financial data at scale. In: DEBS 2019, pp. 254–255. ACM (2019). https://doi.org/10.1145/3328905.3332513
  7. 7.
    Frischbier, S., Pietzuch, P., Buchmann, A.: Managing expectations: runtime negotiation of information quality requirements in event-based systems. In: ICSOC 2014, pp. 199–213. Springer (2014). https://doi.org/10.1007/978-3-662-45391-9_14CrossRefGoogle Scholar
  8. 8.
    Hinze, A., Sachs, K., Buchmann, A.: Event-based applications and enabling technologies. In: DEBS 2009. ACM (2009). https://doi.org/10.1145/1619258.1619260
  9. 9.
    Saha, B., Srivastava, D.: Data quality: the other face of big data. In: ICDE 2014, pp. 1294–1297. IEEE, March 2014. https://doi.org/10.1109/ICDE.2014.6816764

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sebastian Frischbier
    • 1
    Email author
  • Mario Paic
    • 1
  • Alexander Echler
    • 1
  • Christian Roth
    • 1
  1. 1.vwd: Vereinigte Wirtschaftsdienste GmbHFrankfurtGermany

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