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By Vidya Peri
For most go-to-market (GTM) teams, understanding what’s really happening with a customer right now is harder than it should be. Usage data lives in one system, renewals in another, support escalations somewhere else—and field notes are scattered across tools and docs. By the time someone pieces together a full picture, it’s already out of date. As we began using our own data platform internally, this fragmentation became impossible to ignore.
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By Jay Kreps
People often imagine that to provide a cloud service for a piece of open source software is a simple matter of packaging up the open source and putting it in Kubernetes. We knew when we set out to build Confluent Cloud that a true cloud-native offering of Apache Kafka as a service would be much, much more than that.
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By Kartik Kaushik
Traditional Customer 360 architectures were perfectly adequate for the era of quarterly reports and static marketing segments. They successfully pooled data from CRMs, transaction logs, and support platforms to build a unified profile. But for GenAI-powered applications? Yesterday's architecture is a massive bottleneck. Here is why legacy systems are breaking down under the demands of modern AI, and how the architecture is forcing a shift to real-time data.
A streaming transformation is a continuous operation that processes events as they arrive, applies logic in real time, and emits transformed results immediately—without waiting for batch jobs to complete. In Apache Flink, a streaming transformation runs continuously, reacting to each event from a stream. This enables real-time data transformation directly on live data.
Autonomous / agentic event-driven systems are a class of AI-native architectures where software agents continuously sense events, reason over shared state, take actions, and learn from outcomes—all in real time and without human-in-the-loop orchestration. At an architectural level, these systems combine event streaming, stateful processing, and agentic decision layers to form closed-loop AI systems capable of operating independently at scale.
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By Bijoy Choudhury
Enterprise knowledge management RAG (Retrieval-Augmented Generation) is a production-grade AI architecture designed to connect Large Language Models (LLMs) securely to a continuous, real-time flow of proprietary corporate data. Unlike basic RAG implementations that rely on static document uploads and batch-processed vector databases, an enterprise RAG architecture utilizes event streaming to ingest document updates, regenerate embeddings, and synchronize context in real time.
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By Laasya Krupa B
As a cloud engineer, I’ve seen organizations rush to implement Generative AI, only to hit a brick wall when the Chief Information Security Officer (CISO) asks about data residency or PII leakage. In the public sector and regulated industries like healthcare or finance, moving fast and breaking things isn't an option.
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By Confluent
New capabilities remove barriers to production-ready AI applications with agent-powered workflows, automated data protection, and private cloud connectivity.
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By Bijoy Choudhury
Agentic fleet management is a real-time, event-driven architecture where distributed AI agents continuously process streaming data to make autonomous operational decisions and execute them through closed-loop feedback systems. At its core, agentic systems enable: Unlike traditional systems that react to events after the fact, agentic architectures operate as adaptive, self-optimizing systems.
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By Erick Lee
Your AI coding assistant just learned to speak Confluent. Developers live in their editors. The best platform tools meet them there—and increasingly, that means their AI assistants meet them there too. AI coding tools are already reshaping how developers build, debug, and operate software, but most of them are generalists. They can write an Apache Kafka producer, but they won't know your Schema Registry subjects.
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By Confluent
Confluent CEO Jay Kreps takes the stage alongside industry leaders at data streaming’s biggest event. Together, they’ll show why free-flowing, real-time data has become the key to unleashing the full potential of intelligent systems across every business. From live demos to real-world use cases to industry-changing product announcements, this year’s keynote is essential viewing for anyone looking to maximize the potential of their AI. Which is pretty much everyone. Don’t miss it.
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By Confluent
Wix rewired 85% of its data volume onto Confluent Freight Clusters—and the result was lower costs and elastic scalability that handles Black Friday–scale spikes without manual intervention. Josef Goldstein explains why it felt like a magical solution.
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By Confluent
Wix processes 40 billion events a day across use cases that range from minutes to milliseconds. Josef Goldstein explains why the entire upstream architecture has to be built around your most latency-sensitive lane—or none of it works.
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By Confluent
Real-time data and AI are converging—and companies that have already solved the data pipeline problem are pulling ahead fast. Wix processes over 40 billion interactions every day across hundreds of millions of websites, and the architecture behind that scale didn't happen by accident. It was built, lane by lane, around the principle that your upstream data must be at least as fast as your fastest use case.
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By Confluent
KCP automatically generates custom Terraform modules, allowing you to provision your entire target infrastructure and networking in just a few minutes for Kafka migrations.
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By Confluent
Multi-agent systems aren't new architecture—they're microservices evolved. Varun Jasti of AWS explains why Apache Kafka is the natural backbone for agent-to-agent communication at scale.
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By Confluent
Varun Jasti of AWS explains why real-time data—not better models—is the true unlock for enterprise AI. Most enterprises don't need to build AI models from scratch—they need to put AI to work. That requires a data foundation that is real-time, reliable, and ready to serve intelligent systems at scale.
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By Confluent
Most companies aren't trying to build AI—they're trying to use it. Varun Jasti of AWS breaks down why accessible data, not model sophistication, determines whether AI creates real business value.
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By Confluent
The average grocery store has 65 to 80% inventory accuracy. One in 10 products is out of stock at any moment. For an industry operating on razor-thin margins and competing against digital-native challengers, that data gap is existential. In this episode, Kevin Johnson, CEO of Focal Systems, sits down with Joseph to explore how his team is using computer vision, data streaming, and stateful stream processing to close that gap at scale.
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By Confluent
What happens when a security intelligence company decides that data contracts aren't optional, they're the foundation? For SecurityScorecard, that decision changed everything: how teams share data, how pipelines are built, and how quickly a new engineer can ship production-grade work on day one.
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By Confluent
Traditional messaging middleware like Message Queues (MQs), Enterprise Service Buses (ESBs), and Extract, Transform and Load (ETL) tools have been widely used for decades to handle message distribution and inter-service communication across distributed applications. However, they can no longer keep up with the needs of modern applications across hybrid and multi cloud environments for asynchronicity, heterogeneous datasets and high volume throughput.
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By Confluent
Why a data mesh? Predicated on delivering data as a first-class product, data mesh focuses on making it easy to publish and access important data across your organization. An event-driven data mesh combines the scale and performance of data in motion with product-focused rigor and self-service capabilities, putting data at the front and center of both operational and analytical use-cases.
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By Confluent
When it comes to fraud detection in financial services, streaming data with Confluent enables you to build the right intelligence-as early as possible-for precise and predictive responses. Learn how Confluent's event-driven architecture and streaming pipelines deliver a continuous flow of data, aggregated from wherever it resides in your enterprise, to whichever application or team needs to see it. Enrich each interaction, each transaction, and each anomaly with real-time context so your fraud detection systems have the intelligence to get ahead.
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By Confluent
Many forces affect software today: larger datasets, geographical disparities, complex company structures, and the growing need to be fast and nimble in the face of change. Proven approaches such as service-oriented (SOA) and event-driven architectures (EDA) are joined by newer techniques such as microservices, reactive architectures, DevOps, and stream processing. Many of these patterns are successful by themselves, but as this practical ebook demonstrates, they provide a more holistic and compelling approach when applied together.
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By Confluent
Data pipelines do much of the heavy lifting in organizations for integrating, transforming, and preparing data for subsequent use in data warehouses for analytical use cases. Despite being critical to the data value stream, data pipelines fundamentally haven't evolved in the last few decades. These legacy pipelines are holding organizations back from really getting value out of their data as real-time streaming becomes essential.
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By Confluent
In today's fast-paced business world, relying on outdated data can prove to be an expensive mistake. To maintain a competitive edge, it's crucial to have accurate real-time data that reflects the status quo of your business processes. With real-time data streaming, you can make informed decisions and drive value at a moment's notice. So, why would you settle for being simply data-driven when you can take your business to the next level with real-time data insights??
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By Confluent
Shoe retail titan NewLimits relies on a jumble of homegrown ETL pipelines and batch-based data systems. As a result, sluggish and inefficient data transfers are frustrating internal teams and holding back the company's development velocity and data quality.
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By Confluent
Data pipelines do much of the heavy lifting in organizations for integrating and transforming and preparing the data for subsequent use in downstream systems for operational use cases. Despite being critical to the data value stream, data pipelines fundamentally haven't evolved in the last few decades. These legacy pipelines are holding organizations back from really getting value out of their data as real-time streaming becomes essential.
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Connect and process all of your data in real time with a cloud-native and complete data streaming platform available everywhere you need it.
Data streaming enables businesses to continuously process their data in real time for improved workflows, more automation, and superior, digital customer experiences. Confluent helps you operationalize and scale all your data streaming projects so you never lose focus on your core business.
Confluent Is So Much More Than Kafka:
- Cloud Native: 10x Apache Kafka® service powered by the Kora Engine.
- Complete: A complete, enterprise-grade data streaming platform.
- Everywhere: Availability everywhere your data and applications reside.
Apache Kafka® Reinvented for the Data Streaming Era