{"slug": "whats-new-in-q426-confluent-data-streaming-platform-the-most-complete-performant", "title": "What’s New in Q4’26 Confluent Data Streaming Platform: The Most Complete, Performant, and Open Foundation for Streamhouse", "summary": "Confluent released its Q4'26 Data Streaming Platform update, adding Query as a fifth DSP pillar alongside Stream, Connect, Process, and Govern, with Lightning Tables at its center to make Kafka streams queryable in milliseconds without a separate serving database or ETL. The release also makes Managed Model Inference generally available for zero-configuration inference from Flink SQL against LLMs and embedding models in Amazon Bedrock and Vertex AI, and adds Schema Registry SQL-style logical schemas, multi-tenancy for Confluent Private Cloud, Confluent Gateway SKUs across Platform, Private Cloud and Cloud, and Tableflow support for Google Cloud and BigQuery with automated BigLake Metastore sync.", "body_md": "The last item sells in store. The website still takes orders. The AI assistant recommends same-day pickup. One product. Three versions of reality.\n\nA [Streamhouse](https://www.confluent.io/blog/streamhouse-your-business-always-current/) keeps agents, applications, and analytics working from the same current business context. Confluent’s Data Streaming Platform (DSP) provides the most complete, performant, and open foundation for building a Streamhouse – and this quarter, that foundation gets even more powerful. Query joins Stream, Connect, Process, and Govern as the fifth DSP pillar, helping teams continuously capture, process, govern, and serve trusted, always-fresh state of the business wherever decisions happen. At the center of Query is Lightning Tables, which turn Kafka streams into a real-time state of the business, so agents and applications can query what’s true now in milliseconds – no extra database or ETL.  \n\nThat same foundation brings AI closer to live business operations. With Managed Model Inference now generally available, teams can get started faster with zero configuration and more easily build AI on streaming data. Together with Confluent Gateway and Tableflow enhancements, the innovation extends across every DSP pillar to make building a Streamhouse easier, more resilient, and cost-effective. Here’s the latest across AI, Confluent Cloud (CC), Confluent Platform (CP), and WarpStream in Q4’26.\n\nSix innovations lead this release, fulfilling DSP’s promise as the complete, performant, and open foundation a Streamhouse needs.\n\n[**Lightning Tables:**](https://www.confluent.io/product/lightning-tables/) Lightning Tables is a fully managed, read-only serving layer that makes Kafka data queryable with low latency and high concurrency – giving agents, applications, and analytics engines fresh business context without a separate serving database.  \n\n[**Managed Model Inference:**](https://docs.confluent.io/cloud/current/ai/ai-model-inference.html) Zero-configuration capability for calling supported AI and ML models from Flink SQL, enabling real-time inference without managing credentials, endpoints, or serving infrastructure.  \n\n**Schema Registry Updates:** Schema Registry is evolving into the foundational trust layer for your entire Streamhouse, ensuring Kafka, Flink, and the lakehouse all speak the exact same language. With new SQL-style logical schemas and proactive compatibility checks, it guarantees end-to-end pipeline safety by catching and preventing downstream breaks long before your code ever ships. \n\n**Multi-tenancy for Confluent Private Cloud (CPC)****:** Consolidate Kafka clusters, reduce TCO, and onboard in minutes, all on your private infrastructure. Safely host many isolated Logical Kafka Clusters (LKC) on one Physical Kafka Cluster (PKC). Each LKC tenant has its own bootstrap URL, topics, consumer groups, RBAC, quotas and observability.  \n\n[**Confluent Gateway:**](https://docs.confluent.io/platform/current/gateway/overview.html) Simplify migrations, upgrades, and disaster recovery, gain secure partner access, and ensure consistent governance like encryption. Access Confluent Gateway SKU across Confluent Platform, Confluent Private Cloud and Confluent Cloud. \n\n[**Tableflow updates:**](https://docs.confluent.io/cloud/current/topics/tableflow/overview.html) Tableflow now supports Google Cloud and BigQuery, with automated BigLake Metastore sync for querying live Kafka data in minutes without pipeline code.  \n\nConfluent Intelligence helps teams build AI on live streaming data while AI tools help you build faster and bring Confluent capabilities into the AI assistants already in use. This quarter’s updates strengthen the production foundation for real-time AI.\n\n**Managed Model Inference****:** Zero configuration allows you to run inference from Flink SQL against supported models hosted by cloud service providers (CSPs), with no credentials or configuration to set up. This is now generally available for LLMs and embedding models in Amazon Bedrock, Vertex AI, and Azure OpenAI. Managed inference abstracts away serving layers, credentials, and hardware management. \n\n**Real-Time Context Engine****:** Generally available, it serves fresh context to AI agents through MCP as an access pattern for Lightning Tables. Updates includes BYOK using the customer-managed key used by the Kafka cluster, OAuth via Confluent Cloud sign-in or enterprise SSO, and metrics API visibility into storage bytes, bytes scanned, and CCU minutes, and support for backward-compatible schema changes. \n\n**Managed MCP servers****:** Generally available for use with AI assistants, Confluent's MCP servers teams explore environments and clusters, inspect topics and schemas, debug, and query. New Flink SQL tools can create, list, read, and delete statements; debug statements; retrieve metrics; and look up table structure. OAuth provides one Confluent Cloud sign-in - with no API key - across global and regional MCP servers and the Real-Time Context Engine. These AI tools are free for Confluent Cloud users working with assistants such as Claude, Cursor, and Copilot.  \n\n**Confluent Skills****:** Generally available as complementary coding-assistant plugins - not MCP tools - these guided workflows help developers design, build, and debug Kafka Streams applications. The Confluent Tuning Advisor inspects Kafka configurations and recommends tuning changes without applying them. \n\n**Confluent Assistant****:** Directly in Confluent Cloud—no MCP or third-party AI assistant needed—this helps you troubleshoot fully managed connectors that have entered a `FAILED` state with a runtime error. It diagnoses the root cause, recommends a solution, and with explicit user consent, applies the fix and verifies the connector returns to a `RUNNING` state. Sign up for early access [here](https://events.confluent.io/early-access-flink-features).  \n\n**Confluent Intelligence on-prem AI models serving in CP Flink (Open Preview)** enables you to run specialized AI models on-prem in Confluent Platform for Apache Flink (CP Flink) without data ever leaving your environment. Teams can enable production-grade, ultra-lightweight specialized AI models from a curated catalog in Confluent Manager for Apache Flink (CMF), starting with three IBM Granite Time Series Foundation models (TTM-R3, FlowState, and PatchTST-FM). These models need no pre-training and run on CPU alone, so they can be applied immediately to any time series.  Enable use cases such as real-time forecasting and anomaly detection with ease. Invoke them through two new Flink SQL functions, `AI_FORECAST` and `AI_DETECT_ANOMALIES`, or in natural language through the SQL Assistant notebook. Future releases will expand the model catalog. \n\n**Confluent Assistant (NL2SQL) (Open Preview)** brings a conversational, notebook-style experience to Confluent Platform for Apache Flink (CP Flink), that allows you to query real-time Flink data using natural language; no SQL experience required. Users can ask business questions about real-time streaming data in plain English, and SQL Assistant generates the corresponding Flink SQL, while keeping the underlying SQL visible and controllable. The query and results appear together in the conversation, giving users an approachable way to explore live data. Users can ask follow-up questions, continue an investigation with context from earlier exchanges, and ask the SQL Assistant to explain the SQL or results. Experienced SQL users can also review and edit generated statements or write their own SQL in the same notebook-like interface. Every statement remains visible and runnable through the existing Flink SQL execution path. \n\nStream is the foundation of the Streamhouse, serving as a durable multi-consumer record of every business change that applications, agents, and analytics systems can all read at their own pace. A single order event might update inventory, trigger fulfillment, inform fraud controls, and feed an AI agent, and the producer shouldn’t have to coordinate any of them. A Streamhouse feeds live applications and AI agents, so the stream beneath it must be easy to adopt, control, monitor, and quick to recover. This quarter, Confluent delivers on every one. On Confluent Cloud, you can recover from outages seamlessly, migrate one topic at a time to reduce multi-team coordination, and find platform capabilities faster. On private infrastructure, multi-tenancy in Confluent Private Cloud lets platform teams run Kafka as a shared service without choosing between isolation and efficiency.\n\n[__Automatic Failover__](https://docs.confluent.io/cloud/current/multi-cloud/cluster-linking/dr-automatic-failover-tutorial.html) is a fully managed active/passive [__disaster recovery (DR)__](https://docs.confluent.io/cloud/current/multi-cloud/cluster-linking/dr-failover.html) capability for Kafka in Confluent Cloud. By pairing primary and secondary clusters, it allows operators to easily trigger automatic failover for mission-critical workloads. Clients are automatically redirected without endpoint changes or workload restarts, while replication seamlessly reverses for failback, eliminating complex operational toil and delivering low RTO/RPO. \n\n[__Topic-based migration__](https://docs.confluent.io/cloud/current/cp-component/gateway/gateway-topic-based-routing.html) gives platform teams a controlled, incremental path for moving Kafka workloads to Confluent. Supported by a single gateway endpoint and KCP orchestration, it removes application team coordination from the critical path. This allows teams to migrate and validate workloads in manageable batches based on business priorities, drastically minimizing the blast radius and overall migration risk. \n\nConfluent is introducing the next generation of non-Java [__Kafka clients__](https://docs.confluent.io/kafka-clients/overview.html) that will maintain feature parity with the Java client shipped in Apache Kafka. These clients are built on a shared Rust core derived from the Java reference implementation, delivering new Kafka capabilities within weeks of Java availability. They offer idiomatic APIs, improved performance, and reliability. \n\nThe redesigned Confluent Cloud navigation helps users find resources faster by making core objects immediately accessible, and full Data Streaming Platform capabilities easier to discover. It includes an environment switcher that defaults to your last-used workspace and cleanly separates admin controls in the main menu from personal preferences in the user profile. This streamlined, scalable structure reduces clicks and ensures a clear home for future platform expansions.\n\nThe Apache Software Foundation is releasing [__Apache Kafka 4.4__](https://kafka.apache.org/44/), delivering [__30 community-driven improvements__](https://www.confluent.io/blog/introducing-apache-kafka-4-4-0/) to Apache Kafka. The release is led by a change that cuts Kafka Streams recovery from minutes to seconds, and by two of the most requested additions to Queues for Kafka. \n\n[__Multi-Tenancy for Confluent Private Cloud__](https://docs.confluent.io/platform/current/private-cloud/multi-tenancy/quick-start.html) enables teams to safely host many isolated logical clusters (LKCs) on one physical Kafka cluster (PKC). Each LKC tenant is a logical slice of the PKC and has its own bootstrap URL, topics, consumer groups, RBAC, quotas, and observability. With multi-tenancy, your teams can consolidate Kafka clusters, lower TCO and onboard in minutes, all on your private infrastructure. For the tenant, there is no visibility into other tenants' data or the underlying tenancy mechanism, and it feels like a dedicated cluster. Broker-enforced quotas help protect tenants from noisy neighbors.  \n\n__Multi-Tenancy for CPC__**:** Control Center is evolving to make private Kafka environments even easier to operate. C3 now natively supports CPC multi-tenancy.  \n\n[__Dark Mode__](https://docs.confluent.io/control-center/current/release-notes.html#c3-release-notes): Control center now offers light, dark or system themes, and you can switch between them at any time. Dark mode is easier on the eyes during long, and the default light experience is unchanged.  \n\n__Bring your own Prometheus__**:** Control Center can now connect to your existing Prometheus deployment, so it no longer has to install and operate its own bundled Prometheus. Teams that already run a scalable, highly available Prometheus can point Control Center at it. That lets them monitor large Kafka deployments on the Prometheus setup you already trust, at no extra cost. It works for both Confluent for Kubernetes and Ansible deployments. \n\n[__Advanced Security Add-On__](https://docs.confluent.io/cloud/current/security/advanced-security.html) (LA): Advanced Security is an organization-level Confluent Cloud add-on that brings together all advanced enterprise security controls into one package, across all products (e.g., Kafka, Flink, Tableflow) and independent of cluster tier. Rather than managing security capabilities individually, customers get a more consistent security posture across their Confluent Cloud organization with less operational overhead. Features currently included in Advanced Security Add-on include: SCIM, BYOK, Higher IAM-related limits, Access Transparency in audit logs. \n\n[__Access Transparency:__](https://docs.confluent.io/cloud/current/monitoring/audit-logging/access-transparency-overview.html)  Access Transparency lets security and compliance teams track when and why Confluent personnel access Kafka message content on dedicated clusters across AWS, Azure, and GCP. These detailed events, including timestamps, affected clusters, and access reasons, are easily reviewable through your existing logging integrations and the Audit Log Monitoring UI. \n\nStream processing turns raw data into actionable business insights to feed real-time context. Apache Flink® handles this workload upstream (Shift Left) closer to the source, so applications and AI agents read unified results while reducing ETL costs by 40–60%. This release introduces enterprise security, performance, and flexibility enhancements in Confluent Cloud for Apache Flink, along with AI capabilities in open preview for Confluent Platform.\n\nIntegrate customer-managed encryption keys (AWS KMS, Azure Key Vault, GCP KMS) to encrypt Flink state and transient data at rest, enforcing strict data sovereignty and key revocation policies across processing clusters.\n\nFlink SQL can now seamlessly process Kafka records encrypted with Client-Side Field-Level Encryption and Payload Encryption. Governed by RBAC, only authorized principals with read permissions on Key Encryption Keys (KEKs) access cleartext.\n\nThis new hybrid execution mode automatically runs historical catch-up queries using high-throughput batch operators before transitioning to low-latency streaming. This accelerates backfills by 1.3x–1.5x while lowering CFU consumption.\n\nThe Flink dbt adapter now manages Materialized Tables, orchestrates Tableflow Iceberg syncs, executes point-in-time Snapshot Queries, and authenticates via OAuth, enabling data engineering teams to build real-time pipelines using familiar declarative workflows.\n\nBy decoupling state storage from compute via cloud object storage, Flink removes the 500 GB per-statement limit. Stateful joins and aggregations can now scale to multi-terabyte datasets while reducing recovery and rescaling times to seconds.\n\nConfluent now offers a financially backed uptime and availability SLA for Flink statement execution, guaranteeing processing availability and reliability for mission-critical streaming applications.\n\nThe newest pillar, Query, is the serving layer: It makes the current state of the business directly usable, in whatever form each consumer needs. Lightning Tables give applications low-latency SQL access to continuously maintained data, while the Real-Time Context Engine serves live business context to AI agents over MCP. Tableflow turns streams into Apache Iceberg or Delta Lake tables for lakehouse engines; Snapshot Queries run ad hoc SQL across live and historical data, and Streaming Queries push results out as they change. Because these are different ways of serving the same stream of changes, teams can support applications, agents, and analytics without building a separate data pipeline for each.\n\n[Lightning Tables](https://www.confluent.io/product/lightning-tables/) is a managed, read-only serving layer that durably materializes Kafka data into queryable tables. It gives applications and agents low-latency access to current business state - without separate ETL pipelines, databases, or caches - and is generally available on AWS. \n\nData freshness at scale: Changes are available for serving within hundreds of milliseconds; queries return in 10–100 ms at 1,000+ queries per second.\n\nMultiple ways to query: Use SQL or API access directly, or expose fresh context to AI agents through the Real-Time Context Engine via MCP.\n\nProduction controls: Lightning Tables supports BYOK, backward-compatible schema evolution, usage and performance metrics.\n\nFor example, a logistics company can combine vehicle telemetry, weather, traffic, and parcel data to answer, “What’s happening across our drivers and vehicles?” against a current view of operations – rather than stitching together separate, potentially stale systems.\n\nThat gives teams one continuously updated view of business state to serve applications, agents, and analytics.\n\nTableflow on Google Cloud Platform (GCP) offers general availability (GA) and complete feature parity with AWS and Azure across all GCP regions supporting Confluent Cloud Flink. Tableflow materializes Kafka topics into open table formats—Apache Iceberg and Delta Lake—eliminating custom pipeline engineering and schema overhead.\n\nArchitecturally, organizations can store tables using Confluent Managed Storage (CMS) or Bring Your Own Storage (BYOS) with customer-owned Google Cloud Storage (GCS) buckets in single-region or dual-region configurations aligned with the cluster region. This release introduces native catalog synchronization with Google BigLake Metastore (BLMS), alongside Databricks Unity Catalog and Snowflake Open Catalog. As data streams, Tableflow writes Iceberg and Delta tables to GCS and asynchronously registers them in BLMS, ensuring immediate discoverability. Standard SQL queries in Google BigQuery execute directly against streaming data without manual DDL or table registration.\n\nEnterprise capabilities include automated table compaction, snapshot retention, orphaned file cleanup, and schema evolution featuring engine-compatible metadata naming. Security is fortified via credential-free GCP Provider Integration with chained service account impersonation, customer-managed encryption keys via BYOK (Dedicated SKU), and Private Google Access (PGA) for private Kafka clusters.\n\nTableflow supports an automated five-minute default freshness target when materializing Kafka topics into Apache Iceberg and Delta Lake tables. This 66% latency reduction accelerates time-sensitive analytics, fraud detection, and operational dashboards without requiring pipeline re-architecture.\n\nRole-Based Access Control for Tableflow now features two predefined roles: Tableflow Admin for cluster-level management and Tableflow Developer for Kafka topics. This configuration streamlines permission management, prevents over-privileged assignments, and enforces least-privilege compliance across teams.\n\nSecure network access to Tableflow’s Iceberg REST Catalog off the public internet using AWS PrivateLink and Azure Private Link. Analytics engines can discover and query live streaming tables over private network connections, meeting enterprise networking and compliance standards.\n\nTransform Debezium Change Data Capture (CDC) streams directly into analytics-ready Iceberg or Delta tables without custom decoding layers. By natively processing event envelopes—handling inserts, updates, deletes, and schema evolution—Tableflow simplifies CDC-to-lakehouse architectures and reduces operational overhead.\n\nTableflow Upsert supports up to 100 billion rows and 100,000 events per second. Advanced index checkpointing reduces object storage PUT/POST requests by over 50%, significantly lowering cloud provider request fees to under 1% of total operational costs.\n\nA Streamhouse architecture begins by connecting core operational databases, enterprise applications, SaaS platforms, datalakes and other legacy systems. Because these diverse systems cannot be consolidated onto a single platform, the Streamhouse must seamlessly integrate across existing environments. The Connect ecosystem now offers over 110 fully managed connectors. Recent enhancements expand these capabilities in three key areas: extending reach to self-managed and on-premises environments, deepening private network integrations, and accelerating incident resolution.\n\nConfluent introduces **eight new fully managed connectors**—the [BigQuery JDBC Source Connector](https://docs.confluent.io/cloud/current/connectors/cc-gcp-bigquery-source.html#google-bigquery-source-jdbc-connector-for-ccloud), [Marketo Sink Connector](https://docs.confluent.io/cloud/current/connectors/cc-marketo-sink.html#marketo-sink-connector-for-ccloud), [Azure Data Explorer (Kusto) Sink Connector](https://docs.confluent.io/cloud/current/connectors/cc-azure-data-explorer-kusto-sink.html#az-data-explorer-kusto-database-sink-connector-for-ccloud), [DataStax Sink Connector](https://docs.confluent.io/cloud/current/connectors/cc-datastax-sink.html#datastax-sink-connector-for-ccloud), [Webhook Source Connector](https://docs.confluent.io/cloud/current/connectors/cc-webhook-source.html) (LA), [Microsoft Dynamics 365 CRM Source Connector](https://docs.confluent.io/cloud/current/connectors/cc-dynamics-365-crm-source.html#microsoft-dynamics-365-crm-source-connector-for-ccloud), [Stripe Source Connector](https://docs.confluent.io/cloud/current/connectors/cc-stripe-source/cc-stripe-source.html#stripe-source-connector-for-ccloud), and [ChromaDB Sink Connector](https://docs.confluent.io/cloud/current/connectors/cc-chromadb-sink.html#chromadb-sink-connector-for-ccloud)—further expanding our enterprise integration ecosystem. \n\n[__Hybrid Connectors monitored through Unified Stream Manager__](https://docs.confluent.io/cloud/current/usm/monitor/connectors-cloud-kafka.html) (USM) integrate self-managed workloads into a centralized control plane. Monitoring is now Generally Available (GA), providing comprehensive health metrics and throughput tracking across hybrid environments. Additionally, Management features are now in Early Access (EA), enabling operators to pause, resume, restart, and configure eligible self-managed connectors directly within the Confluent Cloud Console. \n\nAdditionally, register for early access to [__Hybrid Connector management with USM__](https://docs.google.com/forms/d/e/1FAIpQLSeGeAGENxgEfiDGanei9DZFBIJkLVZq43lKz9--tAWl-sOLkA/viewform). \n\nThe [Troubleshooting Agent](https://docs.confluent.io/cloud/current/connectors/reference/cflt-troubleshoot-assistant.html) for fully managed connectors diagnoses and resolves integration failures within the Confluent Cloud Console. By combining automated log analysis with human-approved configuration adjustments and automated rollbacks, it significantly reduces mean time to resolution and maintains pipeline availability. \n\nCustom Single Message Transforms (SMTs) in fully managed connectors now support private outbound requests via AWS Egress PrivateLink. This enables secure integrations with services such as AWS KMS and HashiCorp Vault without exposing traffic to the public internet.\n\nPrivateLink egress support is now extended to custom connectors on Microsoft Azure. This provides secure, private outbound connectivity to Azure resources and on-premises environments, ensuring multi-cloud networking parity with AWS.\n\nA Streamhouse is only as trustworthy as the data inside it. Stream Governance defines schema, quality and policy once at the source, so every consumer reads the same contract. This quarter, Schema Registry evolves to be the trust layer for the entire Streamhouse on Confluent, Confluent Gateway is now available across Confluent Platform, Confluent Cloud and Confluent Private Cloud, and Unified Stream Manager gives you one view of your deployments, wherever they run.\n\nThe [Schema Registry](https://docs.confluent.io/cloud/current/sr/schemas-manage.html) is evolving. It’s not just about storing schemas anymore; it's going to be the trust layer for everything built on Kafka and across the Streamhouse.  Schema Registry now delivers one logical schema directly integrated with topics and tables, with support for SQL-style definitions. By guaranteeing that Kafka, Flink, and the lakehouse use the same language and completely agree on what data means, the updates will enable the safe creation and evolution of schemas across the entire Streamhouse on Confluent. Every schema change is now proactively checked for safety across the stream, stream processing and the lakehouse before it ships. The first phase of launches delivers on end-to-end pipeline safety through three major pillars: \n\n**Bind the Topic to its Schema with** __Explicit Topic-Subject Association:__ A first-class, platform-enforced binding between a Kafka topic and its Schema Registry subject, replaces implicit, guess-work naming convention. The platform actively prevents the deletion of any schema a topic still depends on, permanently eliminating pipeline breaks and Sev-1 incidents caused by accidental deletions. This explicit relationship gives downstream engines (like Flink and Iceberg), data catalogs, and developers absolute confidence in their data contracts without having to trace application code or decode naming strategies. \n\n**Ensure One Schema Language with** __Logical Types and Inline Data Quality Rules__**:** Author schemas using native SQL-style types (like STRUCT and VARCHAR) to match the exact vocabulary of Flink and the lakehouse, rather than switching between serialization syntaxes (Avro, Protobuf, or JSON). The Schema Registry seamlessly manages the underlying formats to prevent client disruption. Declare data quality rules directly alongside field definitions using simple CHECK syntax to eliminate separate rule blocks and application-level code. This guarantees the structural integrity AI agents require to consume data safely and effectively. \n\n**Evolve without Breaking Downstream Engines with** __Logical Compatibility Policy:__ Evaluate every schema evolution against downstream realities at register time. The Logical Compatibility Policy proactively rejects schema updates that would silently break Flink jobs, Tableflow, or Lightning Tables, even if they pass standard Kafka wire checks. Developers see exactly why a change was rejected and which engine it would affect right in their pull request, long before the code ever ships. \n\nWe are thrilled to announce the general availability of the Streamhouse Accelerator (formerly known as the DSP Starter Pack). Designed to help teams innovate faster without financial friction, this program provides eligible customers with a flexible, highly discounted budget built to accelerate experimentation and drive seamless workload adoption across Flink, Connect, Tableflow, Stream Governance, and Lightning Tables.By eliminating upfront cost barriers for early-stage data streaming initiatives, the Streamhouse Accelerator delivers immediate financial predictability and high ROI starting at a $25,000 commitment for $50,000 in total DSP list value (a 50% initial discount). Reach out to our account team for more details and to get started!\n\n[__Confluent Gateway__](https://docs.confluent.io/platform/current/gateway/gateway-deploy-overview.html) is an enterprise-grade, stateless, self-managed, Kafka-protocol-aware gateway that sits between client applications and Kafka clusters, decoupling client applications from the underlying Kafka infrastructure. Gateway helps you simplify migrations, upgrades, and disaster recovery, gain secure partner access, and ensure consistent governance like encryption. You can now access Confluent Gateway across Confluent Platform, Confluent Private Cloud, and Confluent Cloud.  \n\nConfluent Gateway’s centralized governance and compliance capability enable you to access:\n\n**Schema ID Enforcement and Deep Schema Validation:** Help ensure compliant, well-formed data flows through your pipelines. \n\n**Field-Level Encryption (GWFLE) and Payload Encryption (GWPE):** Give teams more precise control over how sensitive data is protected in motion. \n\n**Centralized Governance Enforcement:** Enables platform operators to define policies once in Confluent Schema Registry and enforce them authoritatively through Gateway across client applications. \n\nUSM support for GCP via the USM agent and Google Cloud Private Service Connect will be available later this month. USM on GCP allows you to monitor, manage and govern Confluent Platform and Confluent Private Cloud deployments on GCP alongside on-prem, private cloud and other public cloud deployments in one operational view. This extends USM’s governance, observability, and operational control across hybrid environments while keeping Kafka data and client workloads in your private environment. Since only essential metadata, telemetry, and Schema Registry information are shared with Confluent Cloud through Google Cloud Private Service Connect, teams gain centralized visibility and management without compromising data privacy.\n\nJust like Confluent DSP is the foundation for running a Streamhouse-ready platform, so is WarpStream – it covers every capability (capture, transport, transform, govern, and serve), and it all runs in your own cloud account, on your own object storage. [__Learn more here__](https://www.warpstream.com/streamhouse). \n\n[__WarpStream Orbit__](https://www.warpstream.com/orbit-apache-kafka-migration-and-replication) already allows you to automatically migrate per topic from any Kafka-compatible source to WarpStream with zero downtime and no restarts. Now, you can replicate from more than one source cluster (avoiding one project per cluster) and different groups of Agents can reach the source cluster through different entry points, meaning migrations across multiple VPCs, regions, or accounts just work by default and don’t require workarounds. \n\n[__WarpStream Data Governance__](https://www.warpstream.com/data-governance-schema-registry-and-validation) is a suite of tools that span schema registry, validation, and linking. The latest updates add Protobuf to schema validation, so malformed records can be rejected before they land; native Confluent Protobuf types, which makes Confluent-to-WarpStream migrations easier as schemas built for Confluent work as is; and retry for brand-new schemas, so there are few false rejections when an app register a schema and writes it immediately. \n\n[__WarpStream Tableflow__](https://www.warpstream.com/tableflow) is an Iceberg-native database that materializes tables from any Kafka topic and automates ingestion, compaction, and maintenance. On the query performance front, new features include dictionary encoding (files are smaller and queries are faster on columns with repetitive values), better-shaped files (more rows are supported per Parquet file, so query engines can skip what they don’t need), and metadata is parallel-friendly (engines can split reads more efficiently). Turning to setup and security, Tableflow now allows each storage bucket to have its own access orle, giving security teams fine-grained, least-privilege access, and works out table locations automatically for Google’s Lakehouse runtime catalog, reducing manual configuration for Google Cloud users. \n\nConfluent helps you build the most complete Streamhouse, keeping a continuously updated view of what’s happening across the business. By connecting data across systems, processing and governing it as it changes, and serving directly to agents, applications, and lakehouses, a Streamhouse helps teams act sooner.\n\nTo get started:\n\n__Sign up for Confluent__\n\n__Sign up for WarpStream__\n\n__Register for the Streamhouse webinar__\n\n__Start building with the Streamhouse Quickstart__\n\n*The preceding outlines our general product direction and is not a commitment to deliver any material, code, or functionality. The development, release, timing, and pricing of any features or functionality described may change. Customers should make their purchase decisions based on services, features, and functions that are currently available.*\n\n*Confluent and associated marks are trademarks or registered trademarks of Confluent, Inc.*\n\n*Apache**®**, Apache Kafka**®**, Kafka**®**, Apache Flink**®**, Flink**®**, Apache Iceberg** TM* *, Iceberg**TM* *, Apache Parquet**TM* *, and Parquet**TM* *are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. No endorsement by the Apache Software Foundation is implied by using these marks. All other trademarks are the property of their respective owners.*\n\nThe Streamhouse sits between the operational and analytical estates, addressing the inefficiencies inherent in each. In this post, we will compare the Streamhouse to the lakehouse: what each one does, where they differ and how they complement each other.\n\nTo make accurate decisions at machine speed, we need to maintain the state of the business as it changes. It needs to be always current, decentralized across systems that already run the business, and dependable for software to act on it. The architecture that makes this possible is the Streamhouse.", "url": "https://wpnews.pro/news/whats-new-in-q426-confluent-data-streaming-platform-the-most-complete-performant", "canonical_source": "https://www.confluent.io/blog/2026-q4-confluent-bundle-launch/", "published_at": "2026-11-04 15:55:22+00:00", "updated_at": "2026-11-04 16:51:41.172741+00:00", "lang": "en", "topics": ["ai-infrastructure", "large-language-models", "ai-agents", "ai-tools"], "entities": ["Confluent", "Confluent Data Streaming Platform", "Lightning Tables", "Managed Model Inference", "Confluent Gateway", "Tableflow", "Schema Registry", "Amazon Bedrock"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/whats-new-in-q426-confluent-data-streaming-platform-the-most-complete-performant", "markdown": "https://wpnews.pro/news/whats-new-in-q426-confluent-data-streaming-platform-the-most-complete-performant.md", "text": "https://wpnews.pro/news/whats-new-in-q426-confluent-data-streaming-platform-the-most-complete-performant.txt", "jsonld": "https://wpnews.pro/news/whats-new-in-q426-confluent-data-streaming-platform-the-most-complete-performant.jsonld"}}