GenAI Demo with Kafka, Flink, LangChain and OpenAI
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GenAI Demo with Kafka, Flink, LangChain and OpenAI

Generative AI (GenAI) enables automation and innovation across industries. This blog post explores a simple but powerful architecture and demo for the combination of Python, and LangChain with OpenAI LLM, Apache Kafka for event streaming and data integration, and Apache Flink for stream processing. The use case shows how data streaming and GenAI help to correlate data from Salesforce CRM, searching for lead information in public datasets like Google and LinkedIn, and recommending ice-breaker conversations for sales reps.
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Top 5 Trends for Data Streaming with Kafka and Flink in 2024

Do you wonder about my predicted TOP 5 data streaming trends with Apache Kafka and Flink in 2024 to set data in motion? Discover new technology trends and best practices for event-driven architectures, including data sharing, data contracts, serverless stream processing, multi-cloud architectures, and GenAI.
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Apache Kafka + Vector Database + LLM = Real-Time GenAI

Generative AI (GenAI) enables advanced AI use cases and innovation but also changes how the enterprise architecture looks like. Large Language Models (LLM), Vector Databases, and Retrieval Augmentation Generation (RAG) require new data integration patterns. Data streaming with Apache Kafka and Apache Flink processes incoming data sets in real-time at scale, connects various platforms, and enables decoupled data products.
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Data Streaming with Apache Kafka as Data Fabric for GenAI
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Apache Kafka as Mission Critical Data Fabric for GenAI

Apache Kafka serves thousands of enterprises as the mission-critical and scalable real-time data fabric for machine learning infrastructures. The evolution of Generative AI (GenAI) with large language models (LLM) like ChatGPT changed how people think about intelligent software and automation. This blog post explains the relation between data streaming and GenAI and shows the enormous opportunities and some early adopters of GenAI beyond the buzz.
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