|
By Saurabh Ghelani
As organizations have transitioned from batch processing to real-time streaming architectures, a critical governance gap has emerged. Legacy data governance tools designed for databases, warehouses, and file systems assume that information is stationary and focus on protecting, classifying, and auditing data at rest.
|
By Saurabh Ghelani
Banking customers expect financial transactions to be completed quickly. Fraud analysis must execute in milliseconds, so traditional batch processing systems are inherently too slow. To safeguard transactions, institutions must shift to proactive, in-flight prevention. Confluent enables this shift by using Apache Kafka and Apache Flink to continuously correlate transactional and behavioral signals, blocking malicious activity before a transaction settles.
|
By Manveer Chawla
The problem. Predictive ML pipelines that maintain separate batch and streaming code paths for the same features carry training-serving skew, the gap between the features a model was trained on and the features it sees at inference time. Skew silently degrades model accuracy and doubles infrastructure cost. The recommendation. Adopt a unified streaming (kappa) architecture.
|
By Manveer Chawla
Hyper-personalization in 2026 is the ability to act on a user's current intent within the current session, using signals from across the journey. Batch customer data platforms (CDPs) can't do this. They can't capture intent as it forms, can't hold session state, and can't activate inside the intent window.
|
By Zander Matheson
dbt is the most commonly used tool by data engineers to define SQL transformations (as models), write tests, generate documentation, and deploy through CI/CD and now it’s available with Confluent Cloud too! The magic of dbt is that it brings the engineering rigor to modern data work and data engineering, regardless of the underlying compute source - Snowflake, BigQuery, Databricks, Redshift or Confluent. You can find out more about the launch in our Q2 Confluent Cloud Launch post and the keynote.
|
By Confluent
Confluent's 2026 Data Streaming Report finds the biggest barrier to AI growth isn't investment, but the infrastructure needed to support it.
|
By Manveer Chawla
Moving a retrieval-augmented generation (RAG) prototype from a Python notebook into production isn't an API orchestration challenge. It's a distributed systems problem. For engineering managers and data platform leads, the build-versus-buy decision on streaming infrastructure will dictate your artificial intelligence (AI) feature velocity for the next three to five years. This guide assumes you've already prototyped a RAG pipeline.
|
By Manveer Chawla
The EU AI Act's general provisions are already in force, and high-risk AI system obligations apply from August 2026. The National Institute of Standards and Technology (NIST) AI Risk Management Framework and its Generative AI Profile set the baseline for what auditors expect, framing governance around four functions: identify, measure, manage, and monitor. Deploying artificial intelligence (AI) agents in regulated environments isn't a sandbox experiment anymore. It's a strict governance challenge.
Your team just shipped a microservices refactor. Services are smaller, deployments are faster, and boundaries are clearer. Then, during a design review, someone inevitably suggests: “We should use Kafka.”That suggestion might be the exact architectural breakthrough you need—or it could quietly introduce months of unnecessary operational complexity.This article serves as a practical decision framework.
|
By Confluent
Unable to access data-driven insights in time, 82% of UK business leaders are being forced to choose between fast and informed decisions.
|
By Confluent
Use Real-Time Context Engine and Claude, or any MCP-compatible client, to explore operational data using natural language in real time. That includes everything from simple lookups to multi-step investigative questions like: Confluent’s Real-Time Context Engine gives AI agents live access to operational context as events happen across the business. Instead of relying on stale snapshots, agents can query and reason over continuously updated tables in real time.
|
By Confluent
Every enterprise has a data monster. And a way to take control. From AI to analytics, Confluent supercharges innovation across every organization with reusable, reliable, real-time data. So don’t let your data monster hold you back. Unlock value and unleash business impact with freeflowing, real-time data on The World’s Data Streaming Platform.
|
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.
|
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
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
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
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.
- June 2026 (12)
- May 2026 (22)
- 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