The Stream House
The natural next step for Confluent
A "stream house" concept refers to integrating streaming platforms with lakehouse architectures. If Confluent were to implement a stream house, it would likely involve the seamless combination of streaming data processing with the capabilities of a lakehouse, allowing for efficient data storage and retrieval using open table formats like Apache Iceberg.
In this context, a stream house would enable users to access streaming and batch data without duplicating data across different systems. Confluent's existing capabilities, such as support for Iceberg and the concept of multimodal streams, would play a crucial role in this implementation. The stream house would serve as a single source of truth for data, allowing for real-time data processing and analytics while leveraging the benefits of a lakehouse architecture.
Overall, a stream house would represent a promising new paradigm where streaming platforms like Confluent not only handle real-time data ingestion and processing but also provide robust storage and querying capabilities akin to those found in lakehouses. This potential to bridge the gap between streaming and batch processing is a significant step forward in data management.
We can infer some potential key features that a stream house might include:
Seamless Integration of Streaming and Batch Data: A stream house would efficiently handle both streaming and batch data, enabling users to access and analyze data in real-time and historical data without duplication.
Support for Open Table Formats: Utilizing open table formats like Apache Iceberg would be essential for managing data compatible with streaming and batch processing, facilitating easier data management and querying.
Real-Time Data Processing: The ability to process data in real-time as it arrives, enabling immediate insights and analytics, would be a core feature of a stream house.
Multimodal Streams: The concept of multimodal streams would allow users to work with data in both streaming and table formats, providing flexibility in how data is consumed and processed.
Data Lake Capabilities: A stream house would likely incorporate features typical of data lakes, such as storing large volumes of structured and unstructured data, making it easier to manage diverse data types.
Scalability: The architecture must be scalable to handle varying data loads, accommodating high-velocity streaming data and large batch datasets.
Data Governance and Management: Features for data governance, including data lineage, access control, and compliance, would be important to ensure that data is managed securely and responsibly.
Support for Complex Transformations: The ability to perform complex transformations on streaming data, similar to those available in stream processing platforms, would be crucial for deriving insights from the data.
While these features are inferred, they represent the potential capabilities that a stream house could offer in bridging the gap between streaming and lakehouse architectures.
Confluent already offers several features that support the concept of a stream house. Its support for open table formats like Apache Iceberg enables efficient data management and querying, crucial for integrating streaming and batch data. Confluent's "multimodal streams" provide data in both streaming and table formats, allowing for real-time access and historical analysis. Apache Kafka, at Confluent’s core, powers real-time data ingestion and processing, while features like Tableflow expose data as Iceberg tables for seamless batch-stream integration. Confluent also integrates with frameworks like Apache Flink for real-time analytics and offers data governance tools and a wide range of connectors through Kafka Connect, ensuring flexibility and secure data management in a stream house architecture.
These features and the recent acquisition of WarpStream collectively position Confluent as a strong candidate for supporting a stream house architecture, enabling the unified integration of streaming and batch data processing.
What is missing is a query engine that allows for real-time analytics on top of both open table formats and Kafka topics like Apache Pinot, which is essential for a stream house. Adding this technology could catapult Confluent to be the next Databricks or Snowflake.