# Confluent Published articles for Confluent. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Confluent Uses Third-Party Risk Assessments to Support Vendor Due Diligence DevFeed: [How Confluent Uses Third-Party Risk Assessments to Support Vendor Due Diligence]() Original publisher: [Read original article]() Author: Bethany Carter Published: 2026-09-15T16:40:06Z Content type: article Language: en Sources: [Confluent: Data in motion]() Topics: [Security](), [Business Security](), [vulnerability management](), [Cloud]() Tags: [apra](), [automated](), [cloud](), [compliance](), [confluent](), [confluent-cloud](), [data-protection](), [gdpr](), [identity](), [iso](), [nist](), [security](), [standards](), [third-party](), [trust-center](), [vulnerability-management]() ### AI overview Confluent explains how its Trust Center provides third-party risk assessment reports to support vendor security, resilience, compliance, procurement, and customer due diligence. The article describes assessments including ProcessUnity Global Risk Exchange and control mapping to customer frameworks. ### Source excerpt Confluent's Trust Center simplifies vendor risk reviews with CyberGRX, CyberVadis, SIG, CAIQ, and TruSight/KY3P assessments. ## Aiven, Confluent, Redpanda, StreamNative and Ververica Form Streamhouse Working Group DevFeed: [Aiven, Confluent, Redpanda, StreamNative and Ververica Form Streamhouse Working Group]() Original publisher: [Read original article]() Author: Streamhouse Working Group Published: 2026-09-15T07:00:00Z Content type: release Language: en Sources: [Confluent: Data in motion]() Topics: [data-architecture](), [real-time](), [data](), [Streaming](), [Artificial Intelligence]() Tags: [agents](), [ai](), [architectures](), [confluent](), [data](), [distributed](), [news](), [real-time](), [streaming]() ### AI overview Aiven, Confluent, Redpanda, StreamNative, and Ververica announced the Streamhouse Working Group and published a vendor-neutral definition of Streamhouse. The proposed data architecture is designed to keep business context continuously available to production applications, analytics, and AI agents, with real-time, production-native, and decentralized attributes. ### Source excerpt New industry initiative establishes an open category for data architectures that power real-time applications and AI agents ## KCP: How to Migrate to Confluent Cloud in Days, Not Weeks DevFeed: [KCP: How to Migrate to Confluent Cloud in Days, Not Weeks]() Original publisher: [Read original article]() Author: Ahmed Saef Zamzam Published: 2026-09-14T07:00:00Z Content type: tutorial Language: en Sources: [Confluent: Data in motion]() Topics: [Kafka](), [migration](), [Confluent Cloud](), [Replication](), [Infrastructure as code](), [Provisioning](), [Access Control](), [Structured-data]() Tags: [access-control](), [confluent](), [confluent-cloud](), [data-replication](), [infrastructure-as-code-iac](), [kafka](), [migration](), [open-source](), [provisioning](), [replication](), [schema]() ### AI overview The article explains how Confluent's open source KCP tool automates migration from Amazon MSK to Confluent Cloud. KCP supports discovery, infrastructure provisioning, ACL and schema mapping, and migration, while Cluster Linking provides offset-preserving data replication. Support for self-managed Kafka migrations is described as coming soon. ### Source excerpt Use Kafka Copy Paste (KCP) to automate Kafka migration with infrastructure generation, ACL and schema mapping, and offset-preserving data replication. ## Hemut: Building the Internet of Freight on Real-Time Data DevFeed: [Hemut: Building the Internet of Freight on Real-Time Data]() Original publisher: [Read original article]() Author: Tim Graczewski Published: 2026-08-27T20:23:15Z Content type: article Language: en Sources: [Confluent: Data in motion]() Topics: [data](), [real-time](), [Confluent Cloud](), [Streaming](), [Artificial Intelligence](), [Network](), [Software](), [Cloud](), [Operating system]() Tags: [2025](), [2026](), [ai](), [business](), [ceo](), [cloud](), [confluent](), [confluent-cloud](), [cost](), [data](), [efficiency](), [erp](), [network](), [production](), [real-time](), [real-time-data-streaming](), [software](), [streaming](), [supply-chain](), [work]() ### AI overview Hemut uses Confluent Cloud and real-time data streaming to provide an AI-native operating system for trucking carriers and brokers. Its platform combines ERP and TMS capabilities to automate tasks, improve fleet efficiency, reduce operating costs, and support the company's vision of an Internet of Freight. ### Source excerpt Hemut uses Confluent, real-time data streaming, and AI to automate trucking operations, improve fleet efficiency, and build the Internet of Freight. ## New in Confluent Cloud and WarpStream: Evolving the Data Streaming Platform for AI, Scale, and Control DevFeed: [New in Confluent Cloud and WarpStream: Evolving the Data Streaming Platform for AI, Scale, and Control]() Original publisher: [Read original article]() Author: Mike Agnich Published: 2026-08-18T14:00:11Z Content type: article Language: en Sources: [Confluent: Data in motion]() Topics: [Streaming](), [Kafka](), [Cloud](), [event driven](), [Transactions]() Tags: [confluent](), [confluent-cloud](), [debug](), [kafka](), [networking](), [security](), [stream-processing](), [streaming](), [transactions]() ### AI overview Confluent describes more than 70 new features for its data streaming platform, including new connectors, networking improvements, AI capabilities for event-driven agents, real-time machine learning in streaming pipelines, and AI-assisted development tools. The article also discusses broader Kafka workload support, including stream processing, transactions, exactly-once processing, queues, debugging, and Python client support for Queues for Kafka. ### Source excerpt Accelerate enterprise streaming and AI workloads with new product updates across Kora, connectors, Flink, Tableflow, AI anomaly detection and forecasting, security enhancements, and Warpstream. ## New in Confluent Intelligence and AI Tools: Making Agents Native to the Stream, Expanded Model Support, New Agent Skills, and Copilot DevFeed: [New in Confluent Intelligence and AI Tools: Making Agents Native to the Stream, Expanded Model Support, New Agent Skills, and Copilot]() Original publisher: [Read original article]() Author: Confluent Staff Published: 2026-08-18T14:00:10Z Content type: article Language: en Sources: [Confluent: Data in motion]() Topics: [Artificial Intelligence](), [apache-flink](), [Kafka](), [streaming-data-processing](), [real-time](), [MCP Server](), [Agent Skills](), [MCP](), [Inference](), [Google](), [ibm]() Tags: [ai](), [ai-ready-data](), [ai-tools](), [apache-flink](), [confluent](), [confluent-cloud](), [google](), [ibm](), [kafka](), [mcp](), [mcp-server](), [real-time](), [skills](), [streaming-data-processing](), [support](), [tools]() ### AI overview Confluent announces updates to Confluent Intelligence and related AI tools for building production AI systems on Apache Kafka and Apache Flink. The release adds expanded time-series model support, a generally available Real-Time Context Engine, updates to the fully managed MCP Server, Agent Skills for AI coding assistants, and Confluent Copilot. The features are intended to provide agents and applications with fresh business context and governed access to live data and infrastructure. ### Source excerpt Explore new AI features and AI tools: support for IBM Granite Time Series models and TimesFM models (EA), enhanced Real-Time Context Engine experience, new Agent Skills, Confluent Copilot ## How Neuron Systems Served 2.3 Million Fans Across 104 World Cup Matches with AI on Confluent DevFeed: [How Neuron Systems Served 2.3 Million Fans Across 104 World Cup Matches with AI on Confluent]() Original publisher: [Read original article]() Author: Shalini Ananda, PhD Published: 2026-08-14T06:48:05Z Content type: article Language: en Sources: [Confluent: Data in motion]() Topics: [Artificial Intelligence](), [Kafka](), [Streaming](), [real-time](), [observability](), [systems]() Tags: [agents](), [ai](), [architecture](), [confluent](), [kafka](), [observability](), [production](), [real-time](), [scale](), [solutions](), [streaming]() ### AI overview Neuron Systems used six agents and Confluent's Data Streaming Platform to provide multilingual live commentary for all 104 FIFA World Cup 2026 matches. The platform processed 42.1 million production events, maintained a median glass-to-glass latency of 42 milliseconds, and supported millions of fans while keeping commentary synchronized. ### Source excerpt Every one of the 42.1 million production events flowed through Confluent's Data Streaming Platform and that single decision is what let a small team serve a global tournament without the commentary ever falling out of sync. ## Announcing Confluent Platform 8.3: Powerful Apache Flink® SQL operations, Easier KRaft Migrations, Expanded Monitoring and more. DevFeed: [Announcing Confluent Platform 8.3: Powerful Apache Flink® SQL operations, Easier KRaft Migrations, Expanded Monitoring and more.]() Original publisher: [Read original article]() Author: Premika Srinivasan Published: 2026-07-29T15:00:10Z Content type: release Language: en Sources: [Confluent: Data in motion]() Topics: [apache-flink](), [Kafka](), [Monitoring](), [data-governance](), [Model Context Protocol](), [Claude Code](), [codex]() Tags: [apache-flink](), [apache-kafka](), [claude-code](), [codex](), [confluent](), [confluent-platform](), [data-governance](), [mcp](), [mcp-server](), [monitoring]() ### AI overview Confluent Platform 8.3.0, built on Apache Kafka 4.3.0, adds simplified Apache Flink SQL operations, expanded monitoring through Unified Stream Manager, easier KRaft migration, and structured governance for data in motion. The release also introduces an MCP server for Confluent Platform for Apache Flink, allowing AI agents such as Claude Code and Codex to inspect and manage Flink resources through existing security controls. ### Source excerpt Announcing Confluent Platform 8.3: Powerful Apache Flink® SQL operations, Easier KRaft Migrations, Expanded Monitoring and more ## Announcing Upvest as Confluent's 2026 EMEA Data Streaming Startup of the Year DevFeed: [Announcing Upvest as Confluent's 2026 EMEA Data Streaming Startup of the Year]() Original publisher: [Read original article]() Author: Tim Graczewski Published: 2026-07-16T00:07:01Z Content type: article Language: en Sources: [Confluent: Data in motion]() Topics: [Streaming](), [real-time](), [Resilience](), [event driven](), [Scalability](), [Cloud](), [data-governance](), [Microservices](), [Architecture & Design](), [Disaster Recovery]() Tags: [2026](), [cloud](), [cloud-native](), [compliance](), [confluent](), [confluent-cloud](), [data](), [data-governance](), [disaster-recovery](), [emea](), [event-driven](), [fintech](), [infrastructure](), [microservices](), [real-time](), [resilience](), [revolut](), [scalability](), [startup](), [streaming]() ### AI overview Confluent recognizes Berlin-based Upvest as its inaugural EMEA Data Streaming Startup of the Year. The article describes Upvest's real-time, event-driven fintech infrastructure, which supports embedded investments, client onboarding, data governance, disaster recovery, scalability, and regulatory resilience for major financial institutions. ### Source excerpt Confluent for Startups provides an easy on-ramp to Confluent Cloud for early stage startups with great data streaming use cases. ## IBM Buying Confluent Reflects Enterprise Preference for Safety DevFeed: [IBM Buying Confluent Reflects Enterprise Preference for Safety]() Original publisher: [Read original article]() Author: Javi Santana Published: 2026-02-02T00:00:00Z Content type: opinion Language: en Sources: [Tinybird]() Topics: [ibm]() Tags: [confluent](), [enterprise](), [ibm](), [the-data-base]() ### AI overview The article argues that IBM buying Confluent is a move to provide enterprises with a safe, established choice. ### Source excerpt IBM buying Confluent is just another move to secure a safe place for enterprise. Nobody ever got fired for buying IBM--or Confluent. ## Streamfest Day 2: Smarter streaming in the cloud and the future of Kafka DevFeed: [Streamfest Day 2: Smarter streaming in the cloud and the future of Kafka]() Original publisher: [Read original article]() Author: Jenny Medeiros Published: 2025-11-11T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Streaming](), [Cloud](), [data](), [Kafka](), [cloud-infrastructure](), [Latency](), [benchmarking]() Tags: [2025](), [ai](), [analytics](), [applications](), [case-studies](), [cloud](), [confluent](), [cost](), [data](), [developers](), [engineering](), [event](), [kafka](), [latency](), [networking](), [object-storage](), [performance](), [product-updates](), [streaming](), [virtual-event]() ### AI overview A recap of the second day of Redpanda Streamfest 2025, covering data streaming for real-time applications, analytics, and AI. It highlights Redpanda Cloud Topics, a storage tier that uses object storage to help optimize workloads for latency, cost, or performance, along with a planned Redpanda Shadowing migration path from Confluent. ### Source excerpt Redpanda Streamfest is a two-day virtual event for developers to master data streaming for the AI era. Here's what happened on day two. ## Streamfest Day 1: AI, governance, and enterprise agents DevFeed: [Streamfest Day 1: AI, governance, and enterprise agents]() Original publisher: [Read original article]() Author: Jenny Medeiros Published: 2025-11-11T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Streaming](), [Artificial Intelligence](), [ai-governance](), [migration](), [Databricks Unity Catalog integration](), [databricks]() Tags: [2025](), [acquisition](), [agents](), [ai](), [ai-governance](), [catalog](), [chat](), [confluent](), [databricks](), [databricks-unity-catalog-integration](), [developers](), [enterprise](), [event](), [migration](), [product](), [streaming](), [virtual-event]() ### AI overview Redpanda Streamfest is a two-day virtual event focused on data streaming for the AI era. Day one highlights sessions on AI, governance, enterprise agents, product capabilities, customer use cases, and migration from Confluent to Redpanda. ### Source excerpt Redpanda Streamfest is a two-day virtual event for developers to master data streaming for the AI era. Here's what happened on day one. ## Track real-time ad analytics with Snowflake (the easy way) DevFeed: [Track real-time ad analytics with Snowflake (the easy way)]() Original publisher: [Read original article]() Author: Gaurav Thalpati Published: 2025-07-08T00:00:00Z Content type: tutorial Language: en Sources: [Redpanda]() Topics: [real-time](), [data](), [Streaming](), [Learning](), [Docker](), [Containers](), [GitHub](), [Command-line interface](), [Artificial Intelligence]() Tags: [ai](), [analysis](), [analytics](), [build](), [confluent](), [containers](), [developer](), [digital-advertising-analytics-platform](), [docker](), [github](), [implement-ad-analytics-with-snowflake](), [kafka](), [metrics](), [pipelines](), [platform](), [real-time](), [real-time-ad-analytics-snowflake](), [real-time-advertising-data-analysis](), [redpanda-snowflake-integration](), [snowflake-ad-analytics-tutorial](), [snowflake-and-redpanda-for-ad-analytics](), [snowflake-connector-for-ad-analytics](), [snowflake-digital-ad-tracking](), [snowflake-real-time-data-pipeline](), [streaming](), [tech](), [track-digital-ad-engagement-metrics](), [tutorial](), [use-cases]() ### AI overview This tutorial explains how to build a real-time digital advertising analytics pipeline with Redpanda and Snowflake. It uses impression and click data to calculate engagement metrics such as click-through rate, with a no-code, configuration-driven Snowflake connector and Snowpipe Streaming. The tutorial also covers running a self-managed Redpanda cluster with Docker for learning and testing. ### Source excerpt Learn how to build scalable, high-performance pipelines for digital advertising use cases with Snowflake and Redpanda -- a developer-first real-time data platform. ## Achieving $2.25 million in savings: ROI analysis of migrating to Temporal Cloud DevFeed: [Achieving $2.25 million in savings: ROI analysis of migrating to Temporal Cloud]() Original publisher: [Read original article]() Author: Mason Egger Published: 2024-12-12T10:22:00Z Content type: comparison Language: en Sources: [Temporal Blog]() Topics: [maintenance](), [Scalability](), [reliability](), [Confluent Cloud](), [Cloud]() Tags: [cloud](), [confluent](), [confluent-cloud](), [cost](), [cost-savings](), [how-to](), [maintenance](), [reliability](), [scalability]() ### AI overview This ROI report compares a client's homegrown Inventory Management System with Temporal Cloud. Based on a single client and estimates for a typical large customer, it projects $2.25 million in annual savings across infrastructure, maintenance and incident management, and human capital costs. It also describes potential improvements in system stability, scalability, feature development, and data accuracy. ### Source excerpt Discover how Temporal Cloud saves $2.25M annually by cutting infrastructure, maintenance, and labor costs while boosting scalability and reliability. ## Build a Real-time Materialized View from Postgres Changes using Confluent's ksqlDB DevFeed: [Build a Real-time Materialized View from Postgres Changes using Confluent's ksqlDB]() Original publisher: [Read original article]() Author: Evan Shortiss Published: 2024-02-28T17:33:42Z Content type: tutorial Language: en Sources: [Blog -- Neon Docs]() Topics: [Kafka](), [Replication](), [Streaming](), [real-time](), [Messaging](), [Microservice](), [data](), [Databases](), [Cloud](), [Architecture & Design](), [Publish-subscribe pattern](), [ide]() Tags: [apache-kafka](), [architectures](), [cloud](), [community](), [components](), [confluent](), [confluent-cloud](), [data](), [events](), [messaging](), [microservices](), [performance](), [postgres](), [real-time](), [replication](), [sql](), [streaming]() ### AI overview This tutorial explains how to stream changes from a Neon Postgres database to Apache Kafka and use ksqlDB on Confluent Cloud to build a real-time materialized view. It contrasts this approach with refreshing a Postgres materialized view after each write, highlighting performance, history retention, and reliable downstream notifications. ### Source excerpt Neon's support for Postgres' logical replication features opens up a variety of interesting use cases, including for real-time streaming architectures based on change data capture. We previously demonstrated how to use Debezium to fan-out changes from Postgres by using Redis as a... ## Tinybird connects with Confluent for real-time analytics at scale DevFeed: [Tinybird connects with Confluent for real-time analytics at scale]() Original publisher: [Read original article]() Author: Tinybird Published: 2023-07-18T00:00:00Z Content type: release Language: en Sources: [Tinybird]() Topics: [real-time](), [Kafka](), [data]() Tags: [analytics](), [apis](), [confluent](), [data](), [integration](), [kafka](), [real-time](), [scale](), [stream](), [tinybird-news]() ### AI overview Tinybird now connects directly with Confluent, allowing users to stream Kafka data and build real-time APIs without custom integration code. ### Source excerpt Tinybird now connects with Confluent directly. Stream your Kafka data and build real-time APIs without custom integration code. ## Real-time streaming analytics with Confluent and Tinybird DevFeed: [Real-time streaming analytics with Confluent and Tinybird]() Original publisher: [Read original article]() Author: Alejandro Martín Published: 2023-04-18T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [real-time](), [Streaming](), [data]() Tags: [apis](), [batch](), [confluent](), [data](), [product-updates](), [real-time](), [streaming]() ### AI overview The article presents streaming data from Confluent into Tinybird as an alternative to waiting for batch jobs, with the goal of building fast APIs for real-time use. ### Source excerpt Confluent users: stop waiting for batch jobs. Stream your data into Tinybird and build fast APIs on top of it. Real time, for real. ## Integrating Confluent Schema Registry with Apache Spark applications DevFeed: [Integrating Confluent Schema Registry with Apache Spark applications]() Original publisher: [Read original article]() Author: Dominik Liebler Published: 2022-01-24T08:04:19Z Content type: tutorial Language: en Sources: [YAZIO Engineering - Medium]() Topics: [Kafka](), [Apache Spark](), [Streaming](), [data lake](), [parquet](), [Kotlin](), [Kubernetes](), [ceph](), [JSON Schema]() Tags: [apache-spark](), [backpressure](), [ceph](), [confluent](), [data-engineering](), [data-lake](), [data-pipeline](), [json](), [kafka](), [kotlin](), [kubernetes](), [parquet](), [payload](), [pipelines](), [schema](), [schemaregistry](), [serialization](), [spark](), [streaming]() ### AI overview This engineering article explains YAZIO's data pipeline from mobile and web applications through Kafka and Spark Structured Streaming into a Ceph-based data lake. It discusses why schemas matter and describes replacing JSON with Apache Avro and Confluent Schema Registry to reduce message size while keeping schema information externally stored and cached. ### Source excerpt At YAZIO, we believe in making decisions backed by data to help people live healthier lives through better nutrition. For each new and existing feature we want to evaluate how well it performs and how our users interact with it. In order to do so, we need a lot of data and we need to handle backpressure in our systems. To cope with that we use a Kafka cluster managed by Strimzi operators running in Kubernetes. The data itself is being ingested from our mobile and web apps via HTTP or TCP endpoints serialized into JSON and stored in Kafka by a small application written in Kotlin/JVM. Overview of our data pipeline architecture At the other end of the pipeline, different Spark Structured Streaming applications (also written in Kotlin) dump this information into our data lake residing in a Ceph bucket. They read data from Kafka, deserialize it, transform some of the fields and write Parquet files into the data lake using a new schema. Why schemas? Schemas play an important role in data pipelines because they give meaning and context to data. In a world without schemas we would still do random interpretations about the context and meaning of data every now and then when using it. As you might have guessed already this would lead to a lot of bugs and misunderstandings. Photo by EJ Strat https://unsplash.com/photos/VjWi56AWQ9k Similar to a legal contract that binds you to certain limits, a schema binds the data to certain limits and meaning which narrow down the need of interpretation. Choice of serialization formats At the time of writing, Confluent Schema Registry supports these three serialization formats: Apache Avro Protocol Buffers (protobuf) JSON Schema From those choices, only two really provide more than just validation of the data that is ingested and transmitted through our data pipelines. Avro and Protobuf also allow us to shrink the sizes of our topics because only the payload is contained in a message, while the repeating schema will not be stored. In the cas