TL;DR

In the competitive world of capital markets, real-time data processing is essential for swift decision-making and detecting market anomalies. Fahad Shah from RisingWave Labs presented a comprehensive approach to building a scalable pipeline that processes market data in real-time using SQL-based analytics. This pipeline enhances trading efficiency by providing low-latency insights, enabling rapid responses to dynamic market conditions.

Opening

Imagine the impact of a millisecond delay in processing a trade on Wall Street. That's how crucial real-time data processing is in capital markets, where decisions are made in the blink of an eye. Fahad Shah, in his insightful session at the Data Streaming Summit, highlighted the critical role of robust data pipelines that can ingest, analyze, and process market events instantaneously, ensuring traders have the actionable insights they need at their fingertips.

What You'll Learn (Key Takeaways)

• Building Scalable Pipelines – Learn how to construct a robust data pipeline that streams, analyzes, and transforms market data into actionable insights using tools like Solace for event distribution and RisingWave for real-time analytics.
• SQL-Based Real-Time Analytics – Discover the power of SQL-based analytics within RisingWave, enabling practitioners to perform complex real-time data processing with familiar query languages.
• Real-World Application – Understand the implementation of this architecture in capital markets for use cases like anomaly detection, portfolio monitoring, and compliance, ensuring low-latency and high-frequency trading capabilities.
• Feedback Loop for Execution – Explore how processed insights are fed back into trading systems to automate decision-making and enhance trading strategies, creating a closed-loop real-time data pipeline.

Q&A Highlights

Q: Is RisingWave primarily SQL-based for analytics?
A: Yes, RisingWave uses SQL for real-time data analytics, offering familiar query language capabilities enhanced with streaming-specific features like windowing and watermarking.

Q: What is the scalability story of RisingWave?
A: RisingWave is designed for large-scale deployment, supporting high event throughput with features like pre-computed materialized views that ensure low-latency performance and scalability.

Q: Does RisingWave support CDC (Change Data Capture) for analytics?
A: Absolutely, RisingWave integrates with transactional databases like Postgres and MySQL, making it suitable for CDC use cases.

Q: How does RisingWave interact with different data sources?
A: RisingWave supports connectors for various message brokers like Kafka and Pulsar, as well as databases, enabling seamless data ingestion and processing.

Q: What are the future directions for RisingWave in terms of data architecture?
A: RisingWave is moving towards supporting streaming lake houses with native Iceberg table engine support, allowing for efficient data management and interoperability.