Author: Italo Nesi

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This blog explores how to integrate Confluent Tableflow with Trino and use Jupyter Notebooks to query Apache Iceberg tables. Learn how to set up Kafka topics, enable Tableflow, run Trino with Docker, connect via the REST catalog, and visualize data using Pandas. Unlock real-time and historical an...

In this final part of the blog series, we bring it all together by exploring data streaming platforms (DSPs), event-driven architecture (EDA), and real-time data processing to scale AI-powered solutions across your organization.

In Part 2 of the series, we take things a step further by enhancing GenAI with the tools it needs to deliver smarter, more relevant responses. We introduce retrieval-augmented generation (RAG) and vector databases (VectorDBs), key technologies that provide LLMs with the context they need.

This blog series explores how technologies like generative AI, RAG, VectorDBs, and DSPs can work together to provide the freshest and most actionable data. Part 1 lays the foundation for understanding how data fuels AI, and why having the right data at the right time is essential for success.

Learn about the bits and bytes of what happens behind the scenes in the Apache Kafka producer and consumer clients when communicating with the Schema Registry and serializing and deserializing messages.

When developing streaming applications, one crucial aspect that often goes unnoticed is the default partitioning behavior of Java and non-Java producers. This disparity can result in data mismatches and inconsistencies, posing challenges for developers.