Sep 23–26, 2019
Please log in

Fast data with the KISSS stack

Bas Geerdink (Aizonic)
5:25pm6:05pm Wednesday, September 25, 2019
Location: 1A 15/16
Average rating: *****
(5.00, 5 ratings)

Who is this presentation for?

  • Software and solution architects




Streaming analytics (or fast data processing) is becoming an increasingly popular subject in enterprise organizations. Customers want real-time experiences, such as notifications and advice based on their online behavior and other users’ actions. A typical streaming analytics solution follows a “pipes and filters” pattern that consists of three main steps: detecting patterns on raw event data (complex event processing), evaluating outcomes with the aid of business rules and machine learning algorithms, and deciding on the next action. At the core of this architecture is the execution of predictive models that operate on enormous amounts of never-ending data streams.

Bas Geerdink presents an architecture for streaming analytics solutions that covers many use cases that follow this pattern: actionable insights, fraud detection, log parsing, traffic analysis, factory data, the IoT, and others. He explores a few architecture challenges that arise when dealing with streaming data, such as latency issues, event time versus server time, and exactly once processing. The solution is open source and available on GitHub: build on the KISSS stack.

Prerequisite knowledge

  • A basic understanding of big data and fast data applications and application and solution architecture
  • Experience with a reference architecture

What you'll learn

  • Learn to set up a streaming analytics (fast data) solution, basic concepts in this field, and the KISSS stack
Photo of Bas Geerdink

Bas Geerdink


Bas Geerdink is an independent technology lead, focusing on AI and big data. He has worked in several industries on state-of-the-art data platforms and streaming analytics solutions, in the cloud and on prem. Bas has a background in software development, design, and architecture with broad technical experience from C++ to Prolog to Scala. His academic background is in artificial intelligence and informatics. Bas’s research on reference architectures for big data solutions was published at the IEEE conference ICITST 2013. He occasionally teaches programming courses and is a regular speaker at conferences and informal meetings.

Comments on this page are now closed.


Picture of Bas Geerdink
Bas Geerdink | Technology Lead
10/09/2019 11:08pm EDT

The slides are available online at the following link:

Anushka Jadhav | sr software engineer
10/09/2019 4:40pm EDT

Hi, can you please post the slides for this talk

  • Cloudera
  • O'Reilly
  • Google Cloud
  • IBM
  • Cisco
  • Dataiku
  • Intel
  • Io-Tahoe
  • MemSQL
  • Microsoft Azure
  • Oracle Cloud Infrastructure
  • SAS
  • Arcadia Data
  • BMC Software
  • Hazelcast
  • SAP
  • Amazon Web Services
  • Anaconda
  • Esri
  •, Inc.
  • Kyligence
  • Pitney Bowes
  • Talend
  • Google Cloud
  • Confluent
  • DataStax
  • Dremio
  • Immuta
  • Impetus Technologies Inc.
  • Keyence
  • Kyvos Insights
  • StreamSets
  • Striim
  • Syncsort
  • SK holdings C&C

    Contact us

    For conference registration information and customer service

    For more information on community discounts and trade opportunities with O’Reilly conferences

    For information on exhibiting or sponsoring a conference

    For media/analyst press inquires