Streaming Lakehouse

A Streaming Lakehouse (also known as a Real-time Lakehouse) is a modern data management architecture designed to unify real-time stream processing with large-scale batch analytics directly on cost-effective data lake storage. It extends the capabilities of a standard Data Lakehouse by incorporating a dedicated stream processing engine, enabling low-latency data ingestion, continuous processing, and immediate querying of fresh data alongside historical information.

The primary goal of a Streaming Lakehouse is to eliminate the traditional complexities and latencies associated with separate batch and streaming systems (like the Lambda architecture), providing a single, reliable, and scalable platform for diverse data workloads.

The Journey: From Warehouse to Streaming Lakehouse

Understanding the Streaming Lakehouse requires looking at the evolution of data architectures:

1. Data Warehouse: Optimized for structured data and Business Intelligence (BI). Often rigid, expensive, and primarily handled data in batches, leading to data freshness delays.
2. Data Lake: Offered flexibility for diverse data types and low-cost storage (like AWS S3, GCS, ADLS). However, lacked transactional guarantees, schema enforcement, and query performance, often leading to "data swamps".
3. Data Lakehouse: Bridged the gap by adding data warehouse-like structure and reliability features (ACID transactions, schema evolution, time travel) directly onto data lake storage, primarily through open table formats like Apache Iceberg, Delta Lake, and Apache Hudi. This enabled reliable batch processing and BI on the lake.
4. The Streaming Gap: While Data Lakehouses improved batch processing on the lake, efficiently integrating real-time data streams and making them instantly available for querying alongside historical data remained a challenge, often requiring separate, complex streaming pipelines. The Streaming Lakehouse directly addresses this gap.

Core Components of a Streaming Lakehouse

A typical Streaming Lakehouse architecture integrates the following key components:

• Cloud Object Storage: The scalable, durable, and cost-effective foundation (e.g., AWS S3, Google Cloud Storage, Azure Blob Storage) where all data resides.
• Open Table Format: Essential for bringing database-like reliability and management to the raw storage. Apache Iceberg is frequently used in Streaming Lakehouses due to its robust features for handling concurrent writes, schema evolution, and efficient partitioning, which are critical for streaming updates. Delta Lake and Apache Hudi are other options.
• Streaming Ingestion & Processing Engine: The heart of the "streaming" capability. This component continuously ingests data streams (from sources like Apache Kafka, message queues, or Change Data Capture (CDC) feeds), processes them in real-time using Streaming SQL, performs transformations, aggregations, and joins, and often maintains results in Materialized Views for low-latency access. RisingWave is specifically designed to fulfill this role efficiently.
• (Optional) Batch Query Engines: Standard batch processing tools (like Apache Spark, Trino, Presto, Flink batch) can still operate directly on the same open table format tables managed by the streaming engine, ensuring compatibility for large-scale historical analysis or ad-hoc queries.

How a Streaming Lakehouse Works

[Diagram placeholder: Illustrate data flow from sources like Kafka through RisingWave, sinking to Iceberg on S3, with query paths shown.]

In a simplified flow:

1. Raw data streams (e.g., Kafka topics, Debezium CDC events) are ingested by the Streaming Engine (e.g., RisingWave).
2. The Streaming Engine uses Streaming SQL to define continuous queries that process, transform, join, and aggregate this data.
3. Results are often maintained incrementally within the Streaming Engine's state (e.g., via Materialized Views) for ultra-low-latency queries on the freshest data.
4. The Streaming Engine uses a Sink connector to write processed data or changes from Materialized Views into Open Table Format tables (e.g., Apache Iceberg) residing on cloud object storage.
5. Downstream applications can now:
   • Query the Streaming Engine's Materialized Views for near real-time insights.
   • Use batch or ad-hoc query engines to analyze the full historical data stored in the Iceberg tables.

Key Benefits

• Data Freshness: Enables access to processed, queryable data with end-to-end latencies measured in seconds or milliseconds, rather than hours or days.
• Unified Architecture: Simplifies the data stack by potentially eliminating separate batch and speed layers (Lambda architecture), reducing complexity and operational overhead. Provides a single source of truth for both real-time and historical data.
• Scalability & Elasticity: Leverages cloud-native principles, often allowing independent scaling of storage and compute resources to meet varying demands.
• Cost-Effectiveness: Utilizes affordable cloud object storage and open-source formats, reducing vendor lock-in and total cost of ownership compared to traditional warehouses.
• Reliability: Inherits ACID transactions, schema enforcement, and time travel capabilities from the underlying open table format, ensuring data integrity even with concurrent streaming updates.
• Flexibility: Supports diverse data types (structured, semi-structured) and allows various query engines and BI tools to access the same underlying data.

Common Use Cases

The Streaming Lakehouse architecture is well-suited for applications requiring fresh, reliable data, such as:

• Real-time Dashboards & Business Intelligence
• Operational Analytics (system monitoring, application performance)
• Real-time Personalization & Recommendation Engines
• Fraud Detection and Anomaly Detection
• Streaming ETL/ELT Pipelines
• Real-time IoT Data Analysis
• ML Feature Engineering and Online Serving

RisingWave in the Streaming Lakehouse

RisingWave is designed to be a powerful and efficient streaming engine within a Streaming Lakehouse architecture. Its key enabling features include:

• PostgreSQL-Compatible Streaming SQL: Allows users to define complex stream processing logic using familiar SQL syntax.
• Incremental Materialized Views: Persistently stores and continuously updates query results with very low latency, serving as the real-time query layer.
• Built-in State Management: Reliably manages the state required for complex operations like joins and aggregations.
• Connectors: Offers source connectors for common streaming platforms (Kafka, Pulsar, Kinesis, CDC) and sink connectors, crucially including an Apache Iceberg sink, enabling it to write results directly to the lakehouse storage layer.
• Separation of Storage and Compute: Aligns with the scalable, cloud-native principles of the lakehouse.

Conclusion

The Streaming Lakehouse represents a significant evolution in data architecture, merging the best of data lakes, data warehouses, and stream processing. By leveraging open table formats like Apache Iceberg and powerful streaming engines like RisingWave, organizations can build unified, scalable, and cost-effective platforms that deliver real-time insights from their data.

Related Glossary Terms

• Data Lakehouse
• Apache Iceberg
• Stream Processing
• Materialized View
• Open Table Format
• Cloud Object Storage
• Streaming SQL
• Change Data Capture (CDC)
• Kappa Architecture
• Lambda Architecture
• Streaming Database