Ververica Unified Streaming Data Platform Product Updates

Product Updates are organized by release date and let you know what’s new, what’s improved, and what’s fixed in that release.

Every entry within a Product Update follows a consistent structure:

• What's in this update - a self-explanatory heading that describes the individual update.
• What it means to you - a description that explains the impact of the changes and why it matters.
• Where it lives - identifies what part or component of the tech layer the change applies to.
• Where you can use it - identifies the supported deployments to make it easy to know if the feature is available in your environment.
• Where to learn more – links to relevant documentation, examples, or tutorials.

tip

This structure helps you quickly see what changed, why it matters, and where it applies, without needing to understand internal versioning or engineering details. For detailed engineering information, refer to the ChangeLogs.

Release: February 18, 2026

New Features

Deploy BYOC on Microsoft Azure

Ververica Cloud: Bring Your Own Cloud (BYOC) is now available on Microsoft Azure. This deployment option allows you to run the VERA-powered stream processing platform entirely within your Azure environment. Ververica manages platform operations through a secure control plane connection.

To get started, deploy your cluster in your Azure environment, subscribe through the Azure Marketplace, link the subscription to your Ververica Cloud account, and connect your cluster to begin deploying stream processing jobs.

Benefits:

• Zero-Trust Security: Your data stays in your Azure environment. All stream processing data, state checkpoints, and results remain in your Azure Blob Storage with outbound-only control plane access.
• Azure Spend Optimization: Apply your Azure consumption commitments and negotiated discounts to stream processing infrastructure. All billing is consolidated through the Azure Marketplace.
• Native Azure Integration: Connect directly to Azure services such as Event Hubs, Blob Storage, PostgreSQL, and MySQL within your network.
• Flexible Pricing: Choose from Pay-As-You-Go, Monthly, Annual, or Multi-Year commitment plans through the Azure Marketplace.

BYOC on Azure is powered by VERA, delivering up to 2x faster processing than open-source Flink with native support for lakehouse architectures using Apache Paimon.

Supported Deployments: BYOC

Learn More: Get Started with BYOC on Azure

Release: February 9, 2026

Compute Engine: VERA 4.3

Built-on: Apache Flink® 1.20

Overview

VERA 4.3 introduces major enhancements across AI capabilities, SQL and data management, and data ingestion. This release significantly expands Flink SQL functionality, adds vector search support, and improves AI model configuration and robustness.

Key updates focus on features that impact users directly, including:

• Vector Search: Real-time similarity search using the VECTOR_SEARCH function and Milvus integration.
• AI Model Context Handling: Configurable strategies for handling requests that exceed an AI model’s maximum context window.
• Materialized Tables: Automatically managed stateful representations of data for efficient stream processing.
• Unified Batch and Streaming: Native batch execution on Ververica Cloud for a single, unified platform.
• Enhanced SQL Functions: A significantly expanded library of built-in Flink SQL functions for string, JSON, and regex processing.

New Features

This release introduces capabilities that directly impact how you work with the Ververica Unified Streaming Data Platform, improving efficiency, performance, and usability across AI, batch, and streaming workloads.

Perform Vector Search

The new VECTOR_SEARCH function enables real-time similarity searches directly within your SQL queries. It uses Milvus as the underlying vector database to support AI-driven use cases like semantic search, recommendation engines, and retrieval-augmented generation (RAG).

Benefits:

• Simplifies the integration of vector databases into streaming pipelines.
• Enables low-latency semantic retrieval without external application logic.
• Supports complex AI workflows within a familiar SQL interface.

Supported Deployments: BYOC, Managed Cloud

Learn More: VECTOR_SEARCH

Configure AI Model Context Handling

This release adds configurable strategies for handling requests that exceed an AI model’s maximum context window. You can now define how the system behaves when an input is too large, choosing between truncating the data or discarding the record.

Benefits:

• Improves the reliability of AI pipelines by preventing overflow errors.
• Provides better observability through optional logging of truncated or skipped records.
• Allows for finer control over data costs and model performance.

Supported Deployments: BYOC, Managed Cloud

Learn More: Model DDL

Expand SQL Function Library

VERA 4.3 greatly expands the built-in Flink SQL function library, adding powerful new tools for string manipulation, JSON processing, and regular expressions. This includes the JSON_ARRAYAGG function for aggregating rows into JSON arrays and REGEXP_EXTRACT_ALL for advanced pattern matching.

Benefits:

• Reduces the need for custom User-Defined Functions (UDFs).
• Simplifies complex data transformations directly in SQL.
• Improves compatibility with other SQL dialects and processing engines.

Supported Deployments: BYOC, Managed Cloud

Learn More: JSON_ARRAYAGG

Use the VARIANT Data Type

The VARIANT data type provides a flexible container for any supported data type. It is specifically designed to handle semi-structured data or evolving schemas where the exact structure of a column might change over time.

Benefits:

• Simplifies schema management for diverse data sources.
• Enables easier ingestion of semi-structured JSON or NoSQL data.
• Provides a middle ground between rigid schemas and schema-less processing.

Supported Deployments: BYOC, Managed Cloud

Learn More: VARIANT

Use Materialized Tables

Materialized tables are stateful representations of data that are persisted and automatically managed by Flink. They enable efficient stream processing for complex scenarios like temporal joins and windowed aggregations by maintaining intermediate data structures.

Benefits:

• Provides low-latency, fault-tolerant stream computations.
• Supports incremental updates that calculate only changed data.
• Simplifies the development of stateful streaming applications.

Supported Deployments: BYOC, Managed Cloud

Learn More: Materialized Tables

Orchestrate with Workflows

Workflows is a visual orchestration feature that allows you to build, schedule, and manage both batch and streaming deployments. It provides an integrated experience for managing the complete lifecycle of a streaming lakehouse.

Benefits:

• Simplifies the management of complex, multi-stage data pipelines.
• Supports both manual and periodic scheduling using cron expressions.
• Provides a clear graphical view of dependencies and execution status.

Supported Deployments: BYOC, Managed Cloud

Learn More: Workflow Scheduler

Execute Native Batch Jobs

Ververica Cloud now supports native batch execution, allowing you to run large-scale, finite jobs using SQL, Python, or JARs. This unification eliminates the need for separate batch engines and ensures consistent semantics across your entire data platform.

Benefits:

• Reduces operational overhead by using a single engine for all workloads.
• Ensures consistent business logic between real-time and historical processing.
• Simplifies infrastructure by removing the need for a separate Spark cluster for batch.

Supported Deployments: BYOC, Managed Cloud

Learn More: Batch Mode

Manage Multi-Environment CLI Contexts

The vvctl CLI now supports context commands, similar to kubectl. This allows you to define multiple environments (servers, users, and workspaces) and switch between them instantly without manually editing configuration files.

Benefits:

• Reduces the risk of accidental actions against production environments.
• Simplifies onboarding for users managing multiple workspaces.
• Improves security by standardizing authentication and environment handling.

Supported Deployments: BYOC, Managed Cloud

Learn More: CLI Reference

Connectors & Catalogs

This release expands the ecosystem with new catalogs and enhanced format support, focusing on lakehouse integration and MySQL CDC fidelity.

New Integrations

• Delta Lake Connector: Provides a centrally managed metadata layer for writing Flink data into Delta Lake tables, supporting consistent governance and exactly-once semantics. Learn More
• Apache Iceberg Catalog: Enables users to configure Iceberg backends once and reference tables consistently across all deployments. Learn More
• Databricks Unity Catalog: Integrates with the Databricks governance layer to allow discovery and management of lakehouse tables using fully qualified names. Learn More

Updates / Enhancements

• Canal-JSON Format: Now supports parsing native MySQL types when using the MYSQL_TYPE strategy. This improves the correctness of MySQL CDC data ingestion through Kafka by maintaining high schema fidelity. Learn More

Release: November 7, 2025

Compute Engine: VERA 4.1

Built-on: Apache Flink® 1.20

note

Apache Flink 1.20 provides the underlying execution environment for both streaming and batch jobs, handling scheduling, state management, fault tolerance, and distributed processing. On top of this, the VERA 4.1 Compute Engine — Ververica’s proprietary distribution and management layer for Flink — simplifies operations and adds management tools, connectors, and integrations, while Flink continues to perform the actual computation.

Overview

In this release, the Ververica Unified Streaming Data Platform delivers new capabilities and major features across batch and streaming workloads.

Key updates focus on features that impact users directly, including:

• AI-powered integrations that result in real-time insights with LLM and Retrieval-Augmented Generation (RAG) on streaming data.
• Resource management improvements that allow fine-grained scheduling, autopilot tuning, and dynamic parameter updates for efficient, reliable workloads.
• YAML CDC capabilities allow declaratively defining CDC jobs with YAML configuration to simplify job management, making them human readable and understandable to anyone on the team (engineers, data analysts, DevOps).

New Features

This release introduces capabilities that directly impact how you work with the Ververica Unified Streaming Data Platform, improving efficiency, performance, and usability across batch and streaming workloads.

Summary

Feature	Description	Self-Managed (On-Prem)	BYOC	Managed Cloud
Real-Time RAG & AI	Real-time AI and LLM integration	✅	✅	✅
YAML CDC	Declarative CDC configuration	✅	✅	✅
Autopilot	Intelligent and scheduled job tuning	✅	✅	✅
Dynamic Parameter Update	Hot updates without restart	✅	✅	✅
Custom Partitioner	Advanced dimension-table joins	✅	✅	✅
Named Parameters in UDFs	Flexible SQL function calls	✅	✅	✅
New Connectors & Catalogs	New and enhanced connectors (Oracle, PostgreSQL, Milvus, Fluss, Paimon) and catalog integrations for seamless data access and schema management	✅	✅	✅
Resource Queue Management	Stability, fairness, and optimized resource utilization across mixed batch and streaming workloads running under the same workspace	❌	✅	✅
Batch Mode	Unified execution for batch workloads	❌	✅	✅

Integrate with OpenAI Models

Ververica Unified Streaming Data Platform integrates seamlessly with OpenAI models to bring advanced language intelligence into your data pipelines. Empowers real-time Retrieval-Augmented Generation (RAG) directly on streaming data, enabling dynamic, context-aware insights as events happen. Builds intelligent, adaptive pipelines that combine live data processing with AI-driven inference for smarter, faster decision-making.

Benefits:

• Performs data processing, model inference, and information retrieval within a single unified platform-significantly enhancing efficiency and simplifying workflows
• Supports calling AI remote models for text inference and real-time RAG embedding construction within Flink SQL without additional programming and configuration.

Supported Deployments: BYOC, Managed Cloud, Self-Managed (On-Prem)

Learn More: Real-Time AI and RAG with Ververica

Manage CDC Jobs

You can declaratively define CDC jobs with YAML configuration, so anyone on the team (engineers, data analysts, DevOps) can understand or update a job without needing to be a Flink expert. The feature helps reduce misconfigurations and saves time when scaling CDC across many tables or tenants, encourages clean architecture and division of responsibilities.

Benefits:

• Simplifies job management (human-readable, standardized)
• Reuses configuration across environments
• Allows Git-based version control and CI/CD integration
• Isolates wnvironments with templating (e.g., Helm)
• Supports validation and linting tools
• Enables GitOps workflows for CDC jobs

Supported Deployments: BYOC, Managed Cloud, Self-Managed (On-Prem)

Learn More: Data Ingestion

Autopilot 2.0: Automatic Resource Tuning

Autopilot is an automatic tuning feature that helps simplify adjusting resources to achieve optimal utilization. You can select the appropriate tuning mode:

• Intelligent Tuning: In this mode, the system automatically reduces resource allocation when usage is low and increases it when usage rises above a certain threshold..
• Scheduled Tuning: defines how resources are allocated based on specific time periods (e.g., peak/off-peak). It can include multiple sets of time-resource mappings.

Benefits:

• Optimizes job throughput and latency
• Improves efficient resource utilisation
• Provides global tuning for performance and cost efficiency

Supported Deployments: BYOC, Managed Cloud, Self-Managed (On-Prem)

Learn More: Tune Performance

Update Parameters Dynamically (Hot Update)

Dynamic update of the parameter configuration of Apache Flink deployment can make parameter configurations take effect more quickly. This helps reduce business downtime caused by deployment startup and cancellation and facilitates dynamic scaling of TaskManagers and checkpoint-based troubleshooting.

Benefits:

• Reduces downtime
• Simplifies job maintenance

Supported Deployments: BYOC, Managed Cloud, Self-Managed (On-Prem)

Learn More: Dynamic parameter updates

Customize Cache Policies

Most connectors allow you to specify the cache policy for the JOIN operations on dimension tables. Different connectors support different cache policies. For more information, see the related connector documentation.

Benefits: Optimizes lookup joins on small or frequently queried dimension tables.

Supported Deployments: BYOC, Managed Cloud, Self-Managed (On-Prem)

Learn More: Dimension Table Joins

Use Named Parameters in UDFs

You can use named arguments for SQL UDFs. In SQL, parameter values must be passed to a function in the exact order in which the parameters are defined, and optional parameters cannot be omitted. When a function has many parameters, maintaining the correct order can be difficult. To improve usability and avoid errors, you can use named parameters to specify only the parameters you need.

Benefits:

• Improves readability and safety
• Simplifies development and maintainability of the code.

Supported Deployments: BYOC, Managed Cloud, Self-Managed (On-Prem)

Learn More: Named Parameters in User-Defined Functions (UDFs)

Manage Resources Across Mixed Workloads

This release addresses performance and resource allocation challenges in streaming lakehouse scenarios with a fine-grained resource scheduling system. This feature ensures stability, fairness, and optimized resource utilization across mixed batch and streaming workloads running under the same workspace.

Benefits:

• Resource Isolation. Separate tasks and users into distinct queues to prevent interference, avoiding performance drops or failures caused by competing workloads.
• Resource Limitation. Define resource quotas for queues to ensure fair sharing and prevent single workloads from consuming excessive compute or memory.
• Resource Scheduling. Automated scheduling of task execution and allocation based on queue priorities and workload demand.

Supported Deployments: BYOC, Managed Cloud

Learn More: Resource Allocation

Run Large-Scale Batch Jobs

You can now run large-scale, finite batch jobs efficiently using SQL, Python, or JARs — all within a unified Flink execution environment. Integrates seamlessly with catalogs for consistent schema and metadata management.

Benefits:

• Supports end-to-end orchestration through resource queues, ensuring smooth coordination across complex data pipelines.
• Reduces operational overhead
• Provides consistent schema and metadata handling
• Is ideal for analytics, historical backfills, and hybrid workloads that demand both performance and reliability.

Supported Deployments: BYOC, Managed Cloud

Learn More: Batch Mode

Connectors & Catalogs

This release expands ecosystem connectivity with new and enhanced connectors and catalogs, making it easier to integrate Flink jobs with enterprise databases, streaming systems, and AI workloads.

New Integrations

• Oracle Connector & Catalog: Integrated Oracle Database support for both catalog and connector components. The Oracle connector enables reading from and writing to Oracle databases in both streaming and batch modes. Built as an extension of the JDBC connector, it inherits key configuration options and capabilities while adding Oracle-specific optimizations and encryption support.
• Milvus Connector: Added a new connector for Milvus, enabling Flink SQL jobs to store vector and scalar data with upsert support for AI and similarity search workloads.
• Fluss Connector & Catalog: Introduced Fluss integration for Apache Flink. The Fluss Catalog provides SQL-level support with a full mapping between Fluss and Flink tables, enabling seamless metadata management. It supports both source and sink functionality with ordered, durable event streams, configurable parallelism, offset management, and strong delivery guarantees.
• Protobuf Format: Introduced support for Protobuf serialization format as requested by enterprise customers.

Updates / Enhancements

• Kafka Catalog: Added integration with Confluent Schema Registry and AWS Glue Schema Registry for managing and validating Avro schemas. These registries ensure producers and consumers share consistent data structures, support schema evolution, and enable compatibility checks. Flink now supports direct interaction with both registries through the Kafka Schema Registry Catalog, allowing schema and subject metadata management from Flink SQL.
• PostgreSQL Connector & Catalog: Bundled PostgreSQL JDBC connector and driver, enabling Flink SQL jobs on Ververica to connect to PostgreSQL databases. Supports three access modes: the Postgres connector (for reading, writing, and lookups using simple JDBC-style configs), the Postgres catalog (for instant access to existing tables with zero-DDL), and CDC (for continuous streaming). No extra JARs needed, Ververica includes the required drivers and supports key features like parallel reads, sink buffering, and lookup caching.

Learn More:

• Connectors
• Catalogs
• Formats

Security Fixes

This release addresses several critical CVEs and removes vulnerable dependencies to enhance platform security.

• Denial of Service via poisoned logback receiver data.
• Deserialization of untrusted data may allow remote code execution.
• Deserialization vulnerability in Jackson libraries.
• Stack-based buffer overflow due to deeply nested JSON input.
• DoS from excessive memory usage during decompression.
• Updated dependencies to reduce dependency surface.

Bug Fixes

This release resolves the following issues:

• Classloading and deployment issues for both catalog-level and deployment-level UDFs. You can now reliably create and deploy User-Defined Functions (UDFs) without errors caused by classloading conflicts. Previously, a UDF might fail to load or behave inconsistently across different environments (catalog-level vs deployment-level). This ensures smoother, more predictable execution of custom code.

• Paimon Connector: Compatibility with Azure BlobStore. If you store or retrieve data in Azure BlobStore via the Paimon connector, you can now do so without errors or incompatibility issues. This improves stability and reliability when integrating with Azure cloud storage, reducing data pipeline failures.