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Building Realtime AI Applications with Apache Flink

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Building Realtime AI Applications with Apache Flink

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https://www.meetup.com/futureofdata-newyork/events/298660453/

Building Real-Time Applications for Credit Card Spending Analysis with APIs
By Matthias Broecheler
Looking to build realtime, data-driven applications at scale? Join us as we explore how to harness Apache Flink's power to process large data volumes efficiently, and expose the results through responsive APIs. In a live demo, we'll construct a credit card transaction analytics microservice, enabling users to monitor their spending and review transaction history in realtime.

To enhance customer accessibility, we'll also craft a realtime ChatBot using large language models (LLMs) that interacts with the microservice. We'll introduce you to DataSQRL, a tool that simplifies the creation of realtime data applications with Flink by managing the tedious data integration work. With DataSQRL, ingesting your streaming data, processing it in realtime, and exposing the results through a responsive API or a customer-facing ChatBot becomes a breeze.

Come and discover how the combination of Apache Flink and LLMs can effortlessly transform your data into realtime data products.

Unlocking Financial Data with Real-Time Pipelines
(Flink Analytics on Stocks with SQL )
By Timothy Spann
Financial institutions thrive on accurate and timely data to drive critical decision-making processes, risk assessments, and regulatory compliance. However, managing and processing vast amounts of financial data in real-time can be a daunting task. To overcome this challenge, modern data engineering solutions have emerged, combining powerful technologies like Apache Flink, Apache NiFi, Apache Kafka, and Iceberg to create efficient and reliable real-time data pipelines. In this talk, we will explore how this technology stack can unlock the full potential of financial data, enabling organizations to make data-driven decisions swiftly and with confidence.
Introduction: Financial institutions operate in a fast-paced environment where real-time access to accurate and reliable data is crucial. Traditional batch processing falls short when it comes to handling rapidly changing financial markets and responding to customer demands promptly. In this talk, we will delve into the power of real-time data pipelines, utilizing the strengths of Apache Flink, Apache NiFi, Apache Kafka, and Iceberg, to unlock the potential of financial data. I will be utilizing NiFi 2.0 with Python and Vector Databases.

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