Companies are shifting from batch to real-time machine learning. This move brings several benefits. It improves model accuracy and speeds up time-to-market. It also better supports real-time use cases like fraud detection.

However, real-time ML pipelines are more complex than batch processes. A crucial component is feature engineering. This involves ingesting raw data, transforming it into meaningful features, and making them ready for ML models. All of this must be done with low latency.

RisingWave is designed for this task. It's built to ingest and process streaming data in real-time.

Real-time feature engineering presents several key challenges that organizations must address.

Latency is crucial in this domain. For many real-time applications, features need to be computed and served within milliseconds. This requires highly optimized systems and efficient algorithms.

Reliability is equally important. Real-time systems often need to maintain extremely high uptime, typically 99.95% or higher. This demands robust architecture and failover mechanisms to ensure continuous operation.

Data quality in real-time streams poses unique challenges. Systems must be capable of handling out-of-order data or corrupted inputs on the fly. This requires sophisticated error handling and data validation techniques.

Scalability is another critical factor. As data volumes and serving requests grow, the system must scale seamlessly to maintain performance. This often involves distributed processing and dynamic resource allocation.

Lastly, monitoring becomes even more critical in real-time scenarios. With rapidly changing data, it's essential to continuously monitor data quality and model drift. This helps maintain the accuracy and relevance of machine learning models in production.

RisingWave combines a stream processing engine with a fast data store. It ingests data from multiple sources and transforms it into useful features. These features are instantly available for ML models. These capabilities make RisingWave a excellent solution for real-time feature engineering.

The capabilities of RisingWave in details:

Postgres-compatible SQL as the interface

As RisingWave uses Postgres-compatible SQL to ingest and transform data, collaboration between data engineers and data scientists becomes easier.

Efficient data handling

RisingWave ingests and processes large volumes of high-velocity data from various sources such as messaging platforms and databases in real-time.

Broad connectivity

It offers robust connectors for popular data systems, enabling easy data input and output.

Wide compatibility

As a Postgres-compatible database, RisingWave works with many analytics tools through standard Postgres drivers.

Low latency

RisingWave delivers results in milliseconds, using real-time materialized views that update continuously.

Complex transformations

It efficiently performs operations like filtering, joins, and aggregates across multiple data sources.

Consistency and correctness guarantees

RisingWave maintains data integrity through its exactly-once semantics and out-of-order processing capabilities. The exactly-once semantics ensure each data point is processed only once, preventing duplicates or data loss. Meanwhile, out-of-order processing handles non-chronological data arrivals, producing accurate results even with delayed or asynchronous streams.

Simple scaling

You can add new nodes as needed without system downtime.

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