Ververica Platform: Self-Managed 3.x

10x: Faster Deployment
90%: Faster Diagnostics
97%: Faster Snapshots
40-60%: Cost Reduction

One Major Release

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Ververica's Unified Streaming Data Platform Self-Managed version 3 isn't an upgrade; it's a complete reimagining of what a unified streaming platform can do for real-time data teams. After listening to data engineers overwhelmed by deployment complexity, operation teams battling production issues at 3 AM, and architects losing sleep over governance and compliance audits, we built Ververica Platform 3 to tackle these challenges head-on.

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Powered by the VERA Engine

Enterprise-grade performance, now available in Ververica platform self-managed version 3

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. 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.

Infrastructure Improvements

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

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 Streaming and Analytics

Now available in Ververica Platform 3 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.

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.

Diagnose Problems in 60 Seconds (Instead of 45 Minutes)

Before 3: 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: 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.

Compliance Audits in Days, Not Months

Before 3: 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: 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.

The Platform That Scales Itself

Before 3: 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: 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.

See It in Action

$400 in free credits. First pipeline in hours. Architecture that scales from prototype to petabytes.

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