Die Umsetzung einer erfolgreichen Datenstrategie ist nicht einfach, aber die Einschränkungen, die durch zentralisierte Punkt-zu-Punkt-Batch-Architekturen entstehen, machen es noch schwieriger. Daten-Streaming-Plattformen heben diese Beschränkungen auf, indem sie Echtzeit-Konnektivität zwischen SaaS-, Analyse- und Datenbanksystemen ermöglicht, die es mehr Nutzern erlaubt, mehr Daten mit weniger Redundanz und weniger Abhängigkeit von zentralisierten Teams zu nutzen. Was wollen wir an diesem…

Discover the power of combining Amazon Web Services (AWS) and Confluent for real-time analytics and data streaming. This talk explores the synergistic capabilities of both platforms in building scalable, reliable, and high-performance streaming architectures.Learn how AWS data services and Confluent's Apache Kafka-based platform for managing event-driven architectures combine to deliver a solution for your business. Gain practical insights into integration strategies, data transformation, stream…

Have you ever wondered how a modern event analytics pipeline is different from a classical ETL setup? In this talk you will learn how to design and set up an analytics pipeline in just a few minutes, using Kafka for delivery, ksqlDB for preprocessing, and Apache Druid for real time OLAP.

Voraussetzungen: Basic knowledge of what Kafka and Data Streaming are

Integration architectures are the key to modern architectures in every case. It is valid for green-field project, but especially in evolving existing architectures to modern architectures. To bring legacy systems in the cloud highly decoupled systems are necessary. To do so, event-driven architectures are necessary, because decoupling can easily be achieved via events. The talk shows how an event-driven architecture can help to evolve an existing architecture to the state of the art.

Data in motion itself requires a rethinking of how to handle data and what an IT architecture should look like. This opens new ways of analyzing data in near real-time. We’ll have a look at the possibilities of data analytics in motion.

• Which use cases can be covered?
• What technologies can be used for real-time analytics?
• What differences are there to batch analytics?

Prerequisites: none
Level: Basic

We will learn about the latest MLOPs strategies and how to approach application deployment for production using various cloud-native technologies.
We will illustrate how the infrastructure, model and pipeline templates built by Data Reply reduce the time needed to get a QA application into production, allowing manufacturers to reap the significant benefits these machine learning applications can offer.