In this post, we’ll explore and compare RisingWave’s Materialized Views with BigQuery’s Continuous Queries (Materialized Views with refresh enabled), Snowflake’s Dynamic Tables, and Databricks' Delta Live Tables. These features represent how these platforms are addressing real-time data processing and analytics needs, even as they position themselves as unified data platforms rather than dedicated stream processing solutions.
Disclaimer: This analysis is based on publicly available information as of Feb. 17, 2025 and is not intended to serve as purchasing advice.
Why Real-Time Data Processing Matters
Before diving into the comparison, let’s quickly recap the fundamental difference between batch and stream processing:
Batch Processing: Processes data in large chunks at scheduled intervals, like nightly ETL jobs. It’s efficient for analyzing historical datasets but introduces significant latency.
Stream Processing: Processes data as it arrives, enabling real-time insights and immediate actions. This is ideal for applications such as fraud detection, real-time dashboards, and high-frequency trading.
Use Cases for Real-Time Analytics
The need for real-time insights is exploding across industries. Here are some common scenarios where real-time data processing capabilities shine:
Fraud Detection: Spot suspicious transactions and take action immediately to prevent financial losses.
Real-Time Dashboards: Monitor key business metrics or user behavior with up-to-the-second accuracy.
Personalized Recommendations: Deliver tailored content or offers based on real-time user activity.
IoT Device Monitoring: Analyze and respond to sensor data in real time, enabling predictive maintenance or immediate alerts.
Comparing Real-Time Features Across Platforms
We’ll evaluate RisingWave, BigQuery, Snowflake, and Databricks specifically on their respective real-time processing and analytics features, rather than their full product offerings.
| Feature | RisingWave Materialized Views | BigQuery Materialized Views with refresh enabled | Snowflake Dynamic Tables | Databricks Delta Live Tables |
| Data Sources | Event streams (Kafka, etc.), Databases (PostgreSQL, MySQL), CDC streams, Batch sources | BigQuery tables only | Snowflake tables, Materialized Views, Snowflake-managed Iceberg tables | Auto Loader (S3, cloud storage), Streaming Tables, Kafka, CDC from databases |
| Data Sinks | Online applications, Real-time dashboards, Analytical systems (Snowflake, Redshift) | Pub/Sub or BigQuery tables | Dynamic Tables, Snowflake-managed Iceberg Tables | Delta Tables |
| SQL Capabilities | Full ANSI SQL support, including joins, aggregations, window functions | Limited to stateless operations (filter, project), UDFs, GNAI functions | Broad SQL support, but operational complexity for joins and unions | Spark SQL (mostly ANSI SQL compliant) |
| Freshness | Milliseconds to seconds (real-time) | Minutes (periodic or on-demand refresh) | Seconds to minutes, configurable refresh intervals | Near real-time (sub-minute, configurable mini-batch size) |
| Fault Tolerance | Second-level recovery (using S3 for state storage) | Supports instance failure recovery (stateless operations) | Virtual warehouse failover, automatic restart | Spark fault tolerance mechanisms |
| Dynamic Scaling | Yes | No (requires manual resource provisioning) | Yes (virtual warehouse scaling) | Yes |
Deep Dive into Each Feature
RisingWave Materialized Views
Architecture: RisingWave is purpose-built for streaming, leveraging a distributed streaming engine to deliver high performance.
Strengths: It offers true real-time analytics with millisecond-level latency, full ANSI SQL support (including joins, aggregations, and window functions), and seamless integration with open ecosystems (e.g., Kafka, PostgreSQL). Recovery from failures is fast, making it highly resilient.
Considerations: As a relatively new entrant, RisingWave’s community and ecosystem are still growing.
BigQuery Materialized Views with refresh enabled
Mechanism: BigQuery’s Materialized Views incrementally update based on changes in base tables.
Strengths: It’s simple to set up within the BigQuery ecosystem and works well for pre-computing repetitive queries, especially for batch-oriented workflows.
Weaknesses: BigQuery’s Materialized Views are limited to its own tables as data sources, and its SQL capabilities are restricted to stateless operations. The latency (minutes) makes it unsuitable for true real-time use cases like fraud detection or IoT monitoring.
Snowflake Dynamic Tables
Mechanism: Snowflake Dynamic Tables combine batch and streaming paradigms by automatically refreshing data based on defined intervals.
Strengths: Snowflake offers strong performance, configurable refresh intervals, and integration with its ecosystem of tables and managed Iceberg tables.
Weaknesses: SQL limitations are more about operational complexity and cost-efficiency rather than technical restrictions. Frequent refreshes can also lead to higher costs.
Databricks Delta Live Tables
Mechanism: Delta Live Tables take a declarative approach to building data pipelines using Spark Structured Streaming.
Strengths: Databricks supports a wide range of data sources, near real-time performance, and robust pipeline management features. These capabilities make it ideal for complex ETL workflows.
Weaknesses: Data sink options are more limited compared to other platforms, and costs can be higher due to the compute-intensive nature of Spark.
Conclusion
The features compared here highlight how these platforms are addressing the needs of real-time data processing and analytics as part of their broader offerings. While RisingWave is purpose-built for streaming and real-time analytics, the other platforms—Google BigQuery, Snowflake, and Databricks—are positioning themselves as unified data platforms that include real-time capabilities as part of their broader ecosystems.
Here’s a quick summary of the key takeaways:
RisingWave Materialized Views: Best for organizations seeking a dedicated real-time analytics solution with low latency and full SQL support.
BigQuery Materialized Views with Refresh: Suitable for pre-computed analytics within the Google ecosystem, though it’s limited in real-time use cases.
Snowflake Dynamic Tables: Offers a balanced solution for batch-stream hybrid use cases with configurable refresh intervals, but limited in external data integration.
Databricks Delta Live Tables: Excels in managing complex data pipelines and near real-time ETL workflows, though it may not be ideal for simpler real-time analytics needs.
Ultimately, your choice should depend on your specific requirements for latency, SQL complexity, data sources, and integration with existing ecosystems. While these features address real-time needs, their role within each platform’s broader ecosystem is equally important to consider.
