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The Death of Dual Pipelines.

Why run two separate data platforms to represent one truth?

With Ververica's newest release, you can finally eliminate the pipeline architecture that creates duplication, drift, and operational risk in your cloud deployments. One managed platform for all your real-time data processing needs.

Improvements: 

Increase Your Developer Efficiency 

10x faster from blank SQL editor to production deployment

Achieve Operational Excellence

90% reduction in time spent diagnosing failed jobs

Improve State Operations with the VERA Engine

97% faster snapshots, 32% faster scale-ups, 47% faster scale-downs

Build a More Reliable Platform

Real-time health visibility across every workspace and deployment

Meet Compliance Goals

30 seconds to generate complete column-level lineage for GDPR, HIPAA, CCPA, BCBS

Optimize Cost

40-60% cost reduction while maintaining Service Level Objectives (SLOs)

VVP 3-0 Upgrade Animation_2

Ververica transforms your streaming data platform from necessary infrastructure into a competitive advantage.

Learn More

Check out the new feature details in the announcement blog

Read the Release Announcement

New Features and Improvements

Eliminate pipeline duplication, reduce operational complexity, and rebuild trust in your data.

Fundamentally change how you build and operate your data pipeline. Stop managing separate streaming and batch systems. Replace fragile streaming ETL workflows with declarative SQL. Define what you want, and let Ververica's Unified Streaming Data Platform handle the execution.

Available Now: 

Materialized Tables

Define tables once using SQL. The platform maintains them over time. 

Freshness-Driven Execution

Declare how up-to-date data must be and let the platform choose the execution strategy. 

Built-In Workflow Scheduling

Bounded refreshes are planned and scheduled automatically. 

Resource Queue Management

Always-on workloads are protected and batch work runs safely. 

Unified Streaming and Batch Semantics

One platform, one set of rules. 

VERA diagram

Ready for one pipeline? Read the release notes.

squirrel

VERA Engine

One Powerful Engine for Batch and Streaming

Meet VERA 

VERA is the heart of Ververica’s Streaming Data Platform, the engine that operationalizes streaming data and optimizes open source Apache Flink®.

VERA allows you to connect, process, analyze, and govern your data in one ultra-high-performance streaming data solution with exactly-once semantics built in. Created to solve both batch and real-time streaming use cases, VERA makes it easy for you to harness insights from your data at any volume and scale.

Key Features:

Gemini State Backend: With 97% faster snapshots, state migrations that once took 20 minutes now take 30 seconds. 

Tiered Storage: With hot data in memory/SSD, and cold data in object storage, you'll never hit disk limits. 

Key/Value Separation for Joins: Access up to 2x faster streaming joins with low match rates. 

Dynamic Complex Event Processing (CEP): Update fraud detection rules in your database table, and running jobs pick up the changes automatically with no job restart. Now you can react to threats in minutes, not days.

CDAS/CTAS (Create Table As Select): Move data with one SQL statement: CREATE TABLE target AS SELECT * FROM source and Ververica handles the rest: automatic schema inference, offset tracking, delivery guarantees, and seamless schema evolution.

Unified Storage: Build one durable, cost-effective data layer on a lakehouse architecture. A single source of truth with no data duplication.

Streamhouse

Streamhouse

Unified Streaming and Analytics

Streamhouse 

The Problem: Your legacy data architecture is faced with an impossible choice: real-time streaming OR cost-effective analytics. You run duplicate pipelines, streaming in Apache Flink, and batch ETL to warehouses. Two systems means double the maintenance, and a permanent lag between real-time and historical data.

The Solution: Streamhouse unifies streaming and batch with a lakehouse architecture. Stream your data directly into open lakehouse table formats such as Apache Paimon or Iceberg stored in S3, GCS, or Azure Data Lake. Query in streaming mode (for millisecond latency) or batch mode (for warehouse-scale analytics). Same table. Same SQL. Zero duplication. Easier management. 

Key Features:

Stream Directly to the Data Lake: With ACID transactions, automatic compaction, and native change data capture (CDC) support.

Query Both Ways: Real-time dashboards and deep historical analytics on the same table.

Multi-Engine Compatibility: Flink writes streams, while Spark, Presto, Trino, and your BI tools all operate on the same tables. No duplication, no data silos. 

Automatic Schema Evolution: Add columns or change types with no downtime or redeployment.

Time Travel and Audit Trails: Query tables as of a specific timestamp or snapshot ID. Easily restore previous versions for debugging, auditing, or recovery.

Unified Storage: Build one durable, cost-effective data layer. A single source of truth with no data duplication.

Elasticity

NEW! Connectors and Catalogs

New Connectors and Catalogs

Meet the new connectors that link your data sources and destinations, and catalogs that organize and govern your data assets with metadata and lineage tracking available with this release.

Explore the full connector and catalog libraries. 

Now Available:

Delta Lake Connector:

 

This connector provides a first-class, Unity Catalog–backed metadata layer for writing Flink batch and streaming data into Delta Lake tables, so you can manage Delta tables using fully qualified names (catalog.schema.table) with consistent governance across engines. By integrating natively with Flink and supporting exactly-once semantics for both batch and streaming workloads, the Catalog simplifies table management, eliminates filesystem-level configuration, and lays the foundation for reliable append and CDC-friendly write patterns, delivering a production-grade, lakehouse-aligned experience for data engineers.

Databricks Unity Catalog Integration:

 

Use the centralized metadata, governance, and access-control layer that allows Apache Flink jobs to discover, read from, and write to lakehouse tables (such as Delta Lake) using fully qualified names 9like catalog.schema.table) instead of managing table paths and metadata manually.

Unity Catalog acts as the authoritative metastore that provides consistent table definitions, permissions, and lineage, enabling governed, production-grade batch and streaming pipelines that integrate cleanly with the Databricks lakehouse ecosystem.

Apache Iceberg Catalog Support:

 

The Iceberg Catalog provides a first-class, centrally managed Apache Iceberg metadata layer into Ververica, enabling users to configure Iceberg backends once and reference tables consistently as catalog.database.table across all Flink SQL and job deployments. By aligning with the upstream Iceberg Flink catalog integration and integrating it into the native Ververica catalog experience, it eliminates repeated per-table configuration, improves discoverability and governance, and ensures consistent, reliable Iceberg table access across teams, environments, and deployment modes.

One Powerful Engine for Batch and Streaming

Meet VERA 

VERA is the heart of Ververica’s Streaming Data Platform, the engine that operationalizes streaming data and optimizes open source Apache Flink®.

VERA allows you to connect, process, analyze, and govern your data in one ultra-high-performance streaming data solution with exactly-once semantics built in. Created to solve both batch and real-time streaming use cases, VERA makes it easy for you to harness insights from your data at any volume and scale.

Key Features:

Gemini State Backend: With 97% faster snapshots, state migrations that once took 20 minutes now take 30 seconds. 

Tiered Storage: With hot data in memory/SSD, and cold data in object storage, you'll never hit disk limits. 

Key/Value Separation for Joins: Access up to 2x faster streaming joins with low match rates. 

Dynamic Complex Event Processing (CEP): Update fraud detection rules in your database table, and running jobs pick up the changes automatically with no job restart. Now you can react to threats in minutes, not days.

CDAS/CTAS (Create Table As Select): Move data with one SQL statement: CREATE TABLE target AS SELECT * FROM source and Ververica handles the rest: automatic schema inference, offset tracking, delivery guarantees, and seamless schema evolution.

Unified Storage: Build one durable, cost-effective data layer on a lakehouse architecture. A single source of truth with no data duplication.

Unified Streaming and Analytics

Streamhouse 

The Problem: Your legacy data architecture is faced with an impossible choice: real-time streaming OR cost-effective analytics. You run duplicate pipelines, streaming in Apache Flink, and batch ETL to warehouses. Two systems means double the maintenance, and a permanent lag between real-time and historical data.

The Solution: Streamhouse unifies streaming and batch with a lakehouse architecture. Stream your data directly into open lakehouse table formats such as Apache Paimon or Iceberg stored in S3, GCS, or Azure Data Lake. Query in streaming mode (for millisecond latency) or batch mode (for warehouse-scale analytics). Same table. Same SQL. Zero duplication. Easier management. 

Key Features:

Stream Directly to the Data Lake: With ACID transactions, automatic compaction, and native change data capture (CDC) support.

Query Both Ways: Real-time dashboards and deep historical analytics on the same table.

Multi-Engine Compatibility: Flink writes streams, while Spark, Presto, Trino, and your BI tools all operate on the same tables. No duplication, no data silos. 

Automatic Schema Evolution: Add columns or change types with no downtime or redeployment.

Time Travel and Audit Trails: Query tables as of a specific timestamp or snapshot ID. Easily restore previous versions for debugging, auditing, or recovery.

Unified Storage: Build one durable, cost-effective data layer. A single source of truth with no data duplication.

New Connectors and Catalogs

Meet the new connectors that link your data sources and destinations, and catalogs that organize and govern your data assets with metadata and lineage tracking available with this release.

Explore the full connector and catalog libraries. 

Now Available:

Delta Lake Connector:

 

This connector provides a first-class, Unity Catalog–backed metadata layer for writing Flink batch and streaming data into Delta Lake tables, so you can manage Delta tables using fully qualified names (catalog.schema.table) with consistent governance across engines. By integrating natively with Flink and supporting exactly-once semantics for both batch and streaming workloads, the Catalog simplifies table management, eliminates filesystem-level configuration, and lays the foundation for reliable append and CDC-friendly write patterns, delivering a production-grade, lakehouse-aligned experience for data engineers.

Databricks Unity Catalog Integration:

 

Use the centralized metadata, governance, and access-control layer that allows Apache Flink jobs to discover, read from, and write to lakehouse tables (such as Delta Lake) using fully qualified names 9like catalog.schema.table) instead of managing table paths and metadata manually.

Unity Catalog acts as the authoritative metastore that provides consistent table definitions, permissions, and lineage, enabling governed, production-grade batch and streaming pipelines that integrate cleanly with the Databricks lakehouse ecosystem.

Apache Iceberg Catalog Support:

 

The Iceberg Catalog provides a first-class, centrally managed Apache Iceberg metadata layer into Ververica, enabling users to configure Iceberg backends once and reference tables consistently as catalog.database.table across all Flink SQL and job deployments. By aligning with the upstream Iceberg Flink catalog integration and integrating it into the native Ververica catalog experience, it eliminates repeated per-table configuration, improves discoverability and governance, and ensures consistent, reliable Iceberg table access across teams, environments, and deployment modes.

Streamhouse

Streamhouse

Unified Streaming and Analytics

Now available in Ververica platform 3.0 as part of the VERA engine 

The Problem: Your legacy data architecture is faced with an impossible choice: real-time streaming OR cost-effective analytics. You run duplicate pipelines, streaming in Apache Flink, and batch ETL to warehouses. Two systems means double the maintenance, and a permanent lag between real-time and historical data.

The Solution: Streamhouse unifies streaming and batch with a lakehouse architecture. Stream your data directly into open lakehouse table formats such as Apache Paimon or Iceberg stored in S3, GCS, or Azure Data Lake. Query in streaming mode (for millisecond latency) or batch mode (for warehouse-scale analytics). Same table. Same SQL. Zero duplication. Easier management. 

Key Features:

  • Stream Directly to the Data Lake with ACID transactions, automatic compaction, and native change data capture (CDC) support.
  • Query Both Ways: Real-time dashboards and deep historical analytics on the same table.
  • Multi-Engine Compatibility: Flink writes streams, while Spark, Presto, Trino, and your BI tools all operate on the same tables. No duplication, no data silos. 
  • Automatic Schema Evolution: Add columns or change types with no downtime or redeployment.
  • Time Travel and Audit Trails: Query tables as of a specific timestamp or snapshot ID. Easily restore previous versions for debugging, auditing, or recovery.
  • Unified Storage: Build one durable, cost-effective data layer. A single source of truth with no data duplication.
Developer Efficiency

Developer Efficiency

Ship Faster, Break Less

Before Ververica: Data engineers spend more time fighting the platform than building pipelines. Typos in SQL? Find out at runtime. Need to test? Deploy to production and hope it goes well. Tracking deployment versions? Keep your own spreadsheet.

With Ververica: Full validation catches errors before deployment. Sandbox test with mock data to ensure you are production-ready. Utilize auto-versioning with one-click rollback. Lose zero work with auto-save.

Key Features:

  • Declarative CDC in YAML: Git-tracked, CI/CD-ready, reusable across environments.
  • SQL Validation That Works: Catch missing tables, wrong columns, and connector misconfigurations before you hit deploy.
  • Debug Mode with Mock Data: Upload CSV files, run SQL in isolation, see live charts, and take zero risk.
  • Deployment Versioning: Every deployment is auto-versioned. Track side-by-side diffs, and use one-click rollback.
  • Named Parameters in UDFs: Call functions like code: MyUDF(threshold => 0.95, mode => 'strict')
  • Native AI Inference: Call AI models directly from SQL: CREATE MODEL, ML_PREDICT(), ML_EMBED(). Access real-time sentiment analysis, RAG workflows, and semantic search, all in-pipeline.
Operational Excellence

Operational Excellence

Diagnose Problems in 60 Seconds (Instead of 45 Minutes)

Before 3.0: Ops teams live in log files, Kubernetes dashboards, and hope for the best. Any failed deployment results in 45 minutes of detective work across pods, namespaces, and external tools.

With 3.0: With one glance, your health dashboard pinpoints exactly what's wrong, where, and why, all in under 60 seconds. Get real-time notifications, centralized logs, and full visibility, no kubectl required.

Key Features:

  • System Health Dashboard: One glance shows Running vs. Error vs. Transitioning across all deployments.
  • Notification Manager: Real-time job events stream to your notifications. Filter by status. Deep-link to deployment.
  • Unified Event View: Cluster events, operation logs, and job failures into one timeline. Root-cause analysis goes from guesswork to definitive answers.
  • Separated Startup vs. Runtime Logs:  Know immediately whether the problem happened during boot or execution.
  • Centralized Log Hub: Access JobManager and TaskManager logs, metrics, configs, thread dumps, and memory charts all in one place. Full visibility, zero kubectl commands required.
  • Failed TaskManager Archive: Logs persist after crashes, letting you trace root-cause OOM kills without digging into Kubernetes forensics.

Ready to watch a demo or talk to a live expert?

The Ververica Team is here to help

Book a demo
Data Governance

Data Governance

Compliance Audits in Days, Not Months

Before 3.0: Questions like "Show us how Personally Identifiable Information (PII) flows through your system" results in weeks of grep'ing SQL files and drawing diagrams in Lucidchart. Column-level tracking? Forget it.

With 3.0: Arm your compliance officer with complete lineage graphs in 30 seconds. Click a column, and see the entire upstream and downstream flow. Export to JSON/CSV for auditors.

Key Features:

  • Interactive Table & Column-Level Lineage: See your entire data flow with transformations. Hover for metadata. Deep-link to deployments.
  • Search and Auto-Focus: Type table/field name, and the graph jumps to it. Complex environments become navigable.
  • Field-Level Filtering: Show only PII columns that matter. Track CPU/memory/latency per node.
  • One-Click Export: Export lineage to JSON/CSV. Compliance documentation packages assemble in minutes.
  • Built for GDPR, HIPAA, CCPA: Column-level transparency, and audit-ready out of the box.
Elasticity

Elasticity

The Platform That Scales Itself

Before 3.0: Scaling is manual. Traffic spiked at 9 AM? Someone has to notice, calculate new parallelism, trigger a savepoint, and redeploy. You pay for peak capacity 24/7 or suffer degraded performance.

With 3.0:  Meet the platform that scales itself. Autopilot 2.0 monitors CPU, memory, and latency across any source. Adaptive mode handles unpredictable spikes. Stable mode locks in optimal configuration, while scheduled tuning follows business cycles.

Key Features:

  • Autopilot 2.0: Monitor any Apache Flink source (Kafka, CDC, JDBC, and files). It automatically right-sizes parallelism and memory, using two strategies:
    • Adaptive: Continuously optimize for unpredictable workloads (like fraud detection or IoT surges).
    • Stable: Converge to optimal configuration, then lock it in to support steady workloads.
  • Scheduled Tuning: Define time-based resource plans (including daily, weekly, and monthly). Auto-scale for peak hours, scale down overnight. Meet SLAs while cutting off-peak costs by 40%.
  • Dynamic Parameter Updates: Update parallelism, checkpoint settings, and timeouts on running jobs in seconds, all with zero downtime.

Let’s Talk

Ververica's Unified Streaming Data Platform helps organizations to create more value from their data, faster than ever. Generally, our customers are up and running in days and immediately start to see positive impact.


Once you submit this form, we will get in touch with you and arrange a follow-up call to demonstrate how our Platform can solve your particular use case.