Confluent Workshop: Big Data becomes Real-Time Data
Immer mehr Unternehmen nutzen verteilte Streaming-Plattformen, auf denen nicht nur Publizieren und Abonnieren, sondern auch Speicherung und Prozessierung von Daten möglich sind. Unternehmen aller Branchen verlassen sich jetzt auf Echtzeitdaten, um die Kundenerfahrungen zu personalisieren oder betrügerisches Verhalten zu erkennen.
Confluent, Anbieter einer Streaming-Plattform auf der Basis von Apache Kafka®, und inovex haben im November 2018 zu einem Workshop nach Hamburg und Köln eingeladen, um verschiedene Anwendungsfälle der Streaming-Technologie in großen und mittelständischen Unternehmen zu demonstrieren. Sven Hauck (dmTECH) und Dr. Dominik Benz (inovex) berichteten dabei in ihren Vorträgen über ihre Erfahrungen direkt aus der Praxis:
„Experiences from building a streaming-ready hybrid data platform for Customer Analytics at DM“
Sven Hauck (dmTECH)
At DM as one of Germany's biggest drugstores, all aspects of the customers' needs play a crucial role. In order to identify and address these needs in a more precise and timely manner, the department of customer experience analytics (CXA) at DM is extending its platform by components which allow for large-scale and event-driven analytics approach by combining multiple online and offline touchpoint data sources. A key requirement for the new components is to integrate seamlessly with existing enterprise and cloud infrastructure, like an on-premise data warehouse and a cloud-based SAS CI360 solution. In this talk, we highlight the architectural challenges and decisions, and describe our current solution based on Apache Hadoop, Spark, Nifi and the Confluent Platform. Hereby we pay special attention to the steps taken towards productive usage in real-world usecases, especially regarding development, deployment, monitoring and high availability. We close the talk by highlighting current and future customer-facing applications which are enabled by the new platform.
„Stream me up, Scotty: Experiences of integrating event-driven approaches into analytic data platforms“
Dr. Dominik Benz (inovex)
The requirements of many modern data platforms develop along two directions: (1) Low latency, i.e. the shift from batch-oriented to event-driven processes, which facilitate much more timely and reactive insights; and (2) complex analytics, i.e. the ability to efficiently apply analytic functions or models to the incoming data streams. However, many companies don't start from scratch, and already have well-established data infrastructure and processes with various degrees of affinity and compatibility to these novel paradigms. Based on extensive experience of building data platforms with customers, we describe in this talk some key challenges and aspects of introducing streaming-based approaches in real-world productive environments. These include e.g. integrating existing batch-oriented APIs and building realtime analytical visualizations. For selected cases, architectural options are discussed, and the final solution is explained, including technologies like Apache Nifi, Airflow, Phoenix, Druid and the Confluent Platform. We close the talk by describing non-technical aspects like building up an event-driven mindset among analysts.
Datum: 19.11.2018 & 21.11.2018
Speaker: Dr. Dominik Benz
Dominik Benz works as Head of Machine Learning Engineering at inovex GmbH. His research background lies in the field of Data Mining from the Social Web, where he obtained a PhD at the Chair of Knowledge and Data Engineering, University of Kassel. Since 2012, he was involved at inovex in engineering and architecting analytic data platforms in various projects for major companies. He is most experienced in tools around the Hadoop ecosystem, and has hands-on experience in productionizing analytical applications, with a special focus on streaming and realtime approaches.
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