Systems | Development | Analytics | API | Testing

Confluent is thrilled to be named Microsoft’s 2024 OSS on Azure Global Partner of the Year. As a three-time Partner of the Year award winner, this recognition reflects our commitment to delivering outstanding open source-based applications and infrastructure solutions on Microsoft Azure.

In today’s hyper-connected world, systems are more intertwined and complex than ever. Myriad data sources including applications, databases, network and IoT devices continuously generate vast amounts of data, capturing every event and interaction. Imagine harnessing this data–login logs, firewall logs, IPS logs, web logins–aggregating it, and analyzing it to create a holistic view of your entire infrastructure.

In this post, we demonstrate how a robust and scalable generative artificial intelligence (GenAI) chatbot is built using Amazon Bedrock and Confluent Cloud. We walk through the architecture and implementation of this generative AI chatbot, and see how it uses Confluent's real-time event streaming capabilities along with Amazon's infrastructure to continually stay up to date with the latest advances from the AI landscape.

In the space of APIs for consuming up-to-date data (say, events or state available within an hour of occurring) many API paradigms exist. There are file- or object-based paradigms, e.g., S3 access. There’s database access, e.g., direct Snowflake access. Last, we have decoupled client-server APIs, e.g., REST APIs, gRPC, webhooks, and streaming APIs.

As government agencies work to improve both customer experience and operational efficiency, two tools have become critical: cloud services and data. Confluent and Amazon Web Services (AWS) have collaborated to make the move to and management of cloud easier while also enabling data streaming for real-time insights and action. We’ll be at the AWS Public Sector Summit in Washington, DC on June 26-27 to talk about and demo how our solutions work together.

Buy 10 sandwiches, get 1 free. Classic punch cards (and fishing for them in your wallet or occasionally misplacing one) have become a thing of the past, as today's digital landscape demands more innovative solutions. Today’s customer loyalty programs are increasingly sophisticated—evolving, proliferating, and diversifying across every industry from retail, travel, and hospitality to healthcare (e.g., a discount for paying within 30 days of a hospital visit).

In part one of this series, we walked through how to use Streamlit, Apache Kafka, and Apache Flink to create a live data-driven user interface for a market data application to select a stock (e.g., SPY) and discussed the structure of the app at a high level. First, data with information on stock bid prices is moved via an Alpaca websocket, then, it’s produced to a Kafka topic in Confluent Cloud where it is also processed with Flink SQL.

Businesses that are best able to leverage data have a significant competitive advantage. This is especially true in financial services, an industry in which leading organizations are in constant competition to develop the most responsive, personalized customer experiences. Often, however, legacy infrastructure, data silos, and batch systems introduce significant technical hurdles.