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Ecosystem: Streamhouse

Streamhouse complements Ververica's streaming-first architecture by providing seamless integration for both batch and streaming workloads. By integrating real-time data processing capabilities with advanced storage systems, Streamhouse serves as a critical tool for organizations seeking scalable and efficient data pipelines for AI, ML, and analytics workloads, all within the Ververica ecosystem.

Streamhouse is a solution that provides fast, fresh data, while remaining cost-effective and easy for engineers to implement.

Streamhouse Integration

How Streamhouse Fits into Ververica's Ecosystem​

Streamhouse strengthens the Ververica ecosystem by providing an integrated, unified platform that makes managing streaming and batch data more efficient, scalable, and aligned with the broader goals of real-time data processing and analytics. It simplifies the complexities of real-time data processing by seamlessly integrating Apache Flink® with high-performance storage systems. Ververica Unified Streaming Data Platform is the first and only platform that supports both real-time streaming and Streamhouse.

Ververica developed Streamhouse by optimizing Flink and introducing Apache Flink CDC and Apache Paimon. It is powered by these three open-source software technologies:

  • Apache Flink CDC handles Change Data Capture streaming data ingestion.
  • Apache Flink serves as the computation engine for unified batch and stream processing.
  • Apache Paimon provides unified Lakehouse storage for batch, OLAP, and streaming queries.

Streamhouse orchestrates the capabilities of Flink, Flink CDC, and Paimon to enhance Flink-powered applications and foster innovation in real-time data processing within the Ververica ecosystem.

Key Features​

The key Streamhouse features and capabilities leveraged in the Ververica ecosystem include:

CDC Data Ingestion for Lakehouse Integration​

Streamhouse supports organizations with strong requirements for CDC data ingestion, offering a reliable pathway into data lakes.

Cost-Effective Processing for Near-Real-Time SLAs​

With 1-minute latency capabilities, Streamhouse is a cost-effective alternative for scenarios where strict real-time SLAs are not mandatory.

Fresh Data Availability for Up-to-Date Lakehouses​

By providing up-to-date data on lakehouses with 1-minute intervals, Streamhouse ensures consistent data freshness.

Efficient Incremental Updates​

Streamhouse simplifies incremental updates and streaming materialized views, avoiding costly recalculations for predefined queries.

High-Performance OLAP with Fast Writes​

Streamhouse excels in scenarios requiring rapid writes and high-performance OLAP queries.

Streamlining Connections Between Upstream and Downstream Data​

Streamhouse facilitates seamless connections between upstream and downstream tables, enabling tasks like deduplication, multi-stream merging, lookup joins, and business KPI aggregation within a unified system.

Testing Streaming Use Cases with Minimal Investment​

Streamhouse empowers businesses to experiment with streaming scenarios without significant upfront investment, making it a practical solution for batch-oriented infrastructures exploring streaming capabilities.

For more information on Streamhouse, see:

See also Apache Flink, Apache Paimon, and Flink CDC.

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