|
By Saurabh Ghelani
As regulatory frameworks such as the General Data Protection Regulation (GDPR), Digital Operational Resilience Act (DORA), and Network and Information Security Directive 2 (NIS2) converge with the US Clarifying Lawful Overseas Use of Data Act (CLOUD Act), contractual assurances are no longer a sufficient defense. For senior leadership, digital sovereignty has evolved from a compliance checkbox into a core architectural requirement.
|
By Alberto González
Every customer interaction generates signals that matter—a failed checkout, repeated form errors, a frustrated support call, a confusing AI agent exchange, or an unresolved email thread. Individually, these are isolated events. Connected, they reveal customer intent, friction points, operational risk, and opportunities for action.
|
By Nusair Haq
We’re introducing enhanced visibility for streaming workload performance on Confluent Cloud, making it easier for developers and operators to understand, troubleshoot, and optimize real-time applications. As Apache Kafka has become the backbone of data streaming, many teams rely on Confluent Cloud for its scale, elasticity, and reduced operational burden.
|
By Yashwanth Dasari
Organizations are under pressure to feed data lakes and lakehouses with fresher data while keeping a tight lid on cloud spend. The problem is that most ingestion stacks weren’t designed for the real-time, high-volume workloads that power modern analytics and artificial intelligence (AI). They rely on layers of connectors, ETL jobs, and maintenance processes that quietly inflate both infrastructure and operational costs. Confluent’s Tableflow was built to change that equation.
|
By Manveer Chawla
Adding large language models (LLMs) and artificial intelligence (AI) to real-time event streams comes down to one thing: picking the right boundary between data transport and model compute. Where you run inference determines your system's resilience, latency, and cost. This article is for data engineers, streaming architects, and developers who want to add AI capabilities to their Apache Kafka event backbone without destabilizing production consumer groups or blowing through API rate limits.
|
By Manveer Chawla
AI has evolved fast. We've gone from static, predictive models to dynamic, interactive agents. But most organizations still run data pipelines that haven't kept up. Consider what’s happening in modern AI architecture. Teams deploy high-performance engines like large language models (LLMs) and real-time fraud detectors, then feed them data that's hours or days old.
|
By David Marsh
In a previous blog post in this series, we introduced Kafka Copy Paste (KCP), an open source CLI tool that automates the discovery, provisioning, and data migration steps of moving your Apache Kafka environment to Confluent Cloud. We walked through how KCP and Cluster Linking work together to reduce a process that traditionally took weeks to a matter of hours. At the end of that post, we hinted that automated client migration was coming soon. That day has arrived.
|
By Confluent
New Confluent research reveals that 9 in 10 leaders say decisions are speeding up, with AI raising the pressure for instant calls and rushed decisions at the top of UK businesses.
|
By Julian Payne
Today, we're excited to announce the release of Confluent Platform 8.2, which builds on Apache Kafka 4.2! This release extends and simplifies what you can do with Apache Kafka and Apache Flink, whether that’s handling task queues natively with Apache Kafka 4.2, processing streams easily with Flink SQL, or managing cluster migration, upgrades, or disaster recovery without the usual operational pain. The release highlights are below, and additional details about the features are in the release notes.
|
By Saul Sparber
In the explosive new landscape of generative AI (GenAI), the difference between a proof of concept and a production-grade system is scale. For artificial intelligence (AI) infrastructure startup Agent Taskflow Inc. (ATF), this wasn't just a future goal; it was a foundational requirement. Founded in 2023, ATF provides a platform for rapid AI agent bootstrapping, multi-agent orchestration, and comprehensive observability.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
By Confluent
45 million vehicles, 90 markets, 12+ iconic brands, each with its own data silos, standards, and infrastructures. In this episode, Chetan Alatagi, Solution Architect reveals how they transitioned from fragmented legacy ETL silos to a Unified Data Ecosystem—a global data streaming highway that turns vehicle telemetry into real-time value.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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??
|
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.
|
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.
- May 2026 (10)
- April 2026 (8)
- March 2026 (12)
- February 2026 (17)
- January 2026 (13)
- December 2025 (15)
- November 2025 (19)
- October 2025 (36)
- September 2025 (21)
- August 2025 (16)
- July 2025 (26)
- June 2025 (19)
- May 2025 (15)
- April 2025 (22)
- March 2025 (26)
- February 2025 (25)
- January 2025 (14)
- December 2024 (24)
- November 2024 (10)
- October 2024 (24)
- September 2024 (27)
- August 2024 (15)
- July 2024 (9)
- June 2024 (22)
- May 2024 (18)
- April 2024 (7)
- March 2024 (18)
- February 2024 (13)
- January 2024 (6)
- December 2023 (9)
- November 2023 (10)
- October 2023 (14)
- September 2023 (28)
- August 2023 (8)
- July 2023 (2)
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