Name: Data Infra for Capital Markets
Start: 2024-12-12T10:00:00.000Z
End: 2024-12-12T12:00:00.000Z
Location: 16 Collyer Quay, Level 12

**About**

As manufacturing shifts toward smarter solutions, more factories are adopting real-time data processing technologies to boost production efficiency, detect anomalies sooner, and improve supply chain integration. While many companies see the benefits of integrating real-time data processing into their operations, finding and implementing the right solution can be challenging due to the complexity of deployment, usability of existing options, and lack of in-house expertise to manage and optimize the systems. Fortunately, an increasing number of real-time data companies understand these challenges and are innovating to offer effective solutions. Pairing up Coreflux and RisingWave becomes a strong choice for manufacturing businesses seeking reliable real-time production monitoring.

In this webinar, Hugo Vaz of Coreflux and Emily Le of RisingWave will showcase the synergy of Coreflux and RisingWave for real-time monitoring in the case of a beverage factory.

They will explain how sensors on the production lines collect operational data, such as bottle presence and filling status. This data is serialized into JSON format and published to Coreflux’s MQTT Broker using Flux Assets like Siemens S7 and Omron PLC connectors. The Coreflux Hub serves as the central interface for managing these assets, ensuring the smooth flow of data between the PLCs and the broker. The Bridge Asset then securely transmits data from the Edge MQTT Broker to the Coreflux Cloud MQTT Broker, where it is stored for analysis. In the cloud, RisingWave subscribes to the relevant MQTT topics, ingesting real-time data for processing. This allows the factory to continuously monitor key performance indicators, detect anomalies, and optimize production efficiency.

**Speakers**
\- Hugo Vaz \- Co\-Founder of Coreflux
\- Emily Le \- Developer Advocate at RisingWave

Streaming Stories Meetups

Jovana

Andreas Jägle

Tobi M.

Real-Time Production Line Monitoring Using Coreflux and RisingWave

Online event

**About**
The world of data streaming is rapidly evolving, especially as AI-native products become the norm. In this webinar, Yingjun will share with you RisingWave’s 2025 roadmap and its developments, including Python SDK/API, Iceberg support, batch mode capabilities, and, most notably, our AI initiatives. He will also share his unique vision for the future of data streaming and stream processing, exploring how these technologies will transform in 2025, alongside RisingWave’s predictions and strategic responses to these emerging trends.

**Speaker**
[Yingjun Wu](https://www.linkedin.com/in/yingjun-wu/) is the founder of RisingWave Labs, a database company developing RisingWave, a distributed SQL database for stream processing. Before starting the company, Yingjun was a software engineer at the Redshift team, AWS, and a researcher at the Database group, IBM Almaden Research Center. Yingjun received his PhD degree from the National University of Singapore and was a visiting PhD at Carnegie Mellon University. He has been working in the field of stream processing and database systems for over a decade.

Event Stream Processing with Python, Iceberg, Batch Mode, and AI

**About**

In today’s capital markets, the ability to process data in real-time is essential. From interpreting market trends to managing transactions and identifying fraudulent activities, stream processing empowers financial institutions to react swiftly and make smarter decisions in dynamic environments. Join us to explore how real-time stream processing can address critical challenges faced in the financial sector.

**Agenda**
6:00 - 6:30 Networking
6:30 - 7:30 Talks

* Nicolas Hourcard and Vlad Ilyushchenko (QuestDB) - Analyzing Financial Markets with SQL: Functions and Queries + demo
* Samuel Hu (RisingWave) - Real-time Market Analysis: SQL in Stream

7:30-8:00 Networking

**Abstracts**
*Analyzing Financial Markets with SQL: Functions and Queries*
Nicolas and Vlad will showcase a live demonstration of ingesting financial market data in real-time, including both Level 1 and Level 2 market data, into QuestDB. During the session, they will utilize SQL to perform advanced queries and computations, including VWAP, OHLC charts, trade blotters, volume heatmaps, moving averages, and order slippage. Additionally, they will delve into how QuestDB employs open formats like Parquet to efficiently store historical data on object stores, enabling seamless integration and optimized performance for large-scale financial datasets.

*Real-time Market Analysis: SQL in Stream*
In his talk, Samuel will highlight how RisingWave, a SQL-based streaming database, can help industries gain actionable insights from real-time market data, both structured and unstructured, transforming how businesses make live decisions.

**Bios**
Nicolas Hourcard: Nic is co-founder and CEO of QuestDB, an open-source time-series database focused on performance and usability. He co-founded QuestDB in 2019 alongside Vlad Ilyushchenko, who spent decades building low-latency FX trading systems at large banks and trading firms. Nic previously worked in financial services, focusing on data and analytics at Nasdaq and Rothschild.

Vlad Ilyushchenko: I bring over 30 years of experience in software engineering to my current role as the Chief Technology Officer and Co-founder at QuestDB. In this capacity, I thrive on addressing multifaceted challenges across technology, team dynamics, business growth, customer relations, and marketing strategies. Prior to co-founding QuestDB, I specialized in developing electronic trading systems and providing robust technological support for a diverse range of financial markets, including FX, Equity, Commodities, and cryptocurrency.

Samuel Hu: As a founding member of RisingWave Labs, I specialize in data systems and stream processing to advance real-time analytics.

Data Infrastructure Singapore

Lan Fangzhou

sudhir

Data Infra for Capital Markets

16 Collyer Quay, Level 12

Modern Real-Time Data Stack Meetup

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