Exploring the future of financial data architecture: AI-native financial data foundation, large-scale financial data processing, and enterprise data quality & governance.
Tag: Real-Time Analytics
One of the reasons — actually, the core reason — I chose DolphinDB as the built-in streaming engine for QuantFlow's streaming execution layer is that it's really fast, even for the kind of complicated computation that requires chained steps. Thanks to that speed, and with QuantFlow's MarketState engine and FeatureDAG compiler on top, we can … Continue reading QuantFlow Fun – Build a Low-Latency Feature Monitor Dashboard
In the previous blog post, we explored option Greeks calculations using the BSM model. In this post, I’ll have some coding fun by implementing real-time Greeks calculations with the formulas from the last post, but this time using the DolphinDB stream processing framework. Here, I plan to mimic a portfolio consisting of option contracts with … Continue reading Real-Time Option Greeks Calculation with DolphinDB
What makes me buy into DolphinDB: Friendly DolphinDB – Cross-Exchange Arbitraging Case Speedy DolphinDB – Why is DolphinDB so fast? Robust DolphinDB – Reliable, Scalable, Resilient, Consistent, and Monitorable Cost Effective DolphinDB – Worth the Money DolphinDB – An Integrated Financial Data Platform, Not Just a Time-Series Database When I chat about DolphinDB with someone, it’s common … Continue reading DolphinDB – An Integrated Financial Data Platform, Not Just a Time-Series Database
What makes me buy into DolphinDB: Friendly DolphinDB – Cross-Exchange Arbitraging Case Speedy DolphinDB – Why is DolphinDB so fast? Robust DolphinDB – Reliable, Scalable, Resilient, Consistent, and Monitorable Cost Effective DolphinDB – Worth the Money DolphinDB – An Integrated Financial Data Platform, Not Just a Time-Series Database This is the fourth post in my series where … Continue reading Cost Effective DolphinDB – Worth the Money
What makes me buy into DolphinDB: Friendly DolphinDB – Cross-Exchange Arbitraging Case Speedy DolphinDB – Why is DolphinDB so fast? Robust DolphinDB – Reliable, Scalable, Resilient, Consistent, and Monitorable Cost Effective DolphinDB – Worth the Money DolphinDB – An Integrated Financial Data Platform, Not Just a Time-Series Database As a high-performance database built for business-critical financial applications … Continue reading Robust DolphinDB – How does DolphinDB Achieve Scalability, Reliability, Resilience, Consistency, and Monitorability
What makes me buy into DolphinDB: Friendly DolphinDB – Cross-Exchange Arbitraging Case Speedy DolphinDB – Why is DolphinDB so fast? Robust DolphinDB – Reliable, Scalable, Resilient, Consistent, and Monitorable Cost Effective DolphinDB – Worth the Money DolphinDB – An Integrated Financial Data Platform, Not Just a Time-Series Database I reckon there is no doubt that kdb+ has … Continue reading Friendly DolphinDB – Cross-Exchange Arbitraging Case
From Spark v3.2, session window is natively supported by Spark Structured Streaming. Session window based aggregation is a common requirement of streaming data processing, especially in the use cases such as user behaviour analytics. In this blog post, I will discuss how session window works under the hood in Spark Structured Streaming. Compared to the … Continue reading Spark Structured Streaming Deep Dive (8) – Session Window
This blog post discusses another stateful operation supported by Spark Structured Streaming, Stream-Stream Join, which joins two streaming datasets. Unlike static datasets join, for the rows reaching to one side of the input streams in a micro-batch, the matching rows would highly likely be not received in the other side of the input streams at … Continue reading Spark Structured Streaming Deep Dive (7) – Stream-Stream Join
There are two types of streaming processing modes, Stateless and Stateful. Stateless is easy to understand that each message is processed independently without the needs to maintain the states across multiple messages. The challenge and fun one is the Stateful streaming processing where the processing of a message depends on the result of the processing … Continue reading Spark Structured Streaming Deep Dive (6) – Stateful Operations
Spark Structured Streaming reuses the Spark SQL execution engine, including the analyser, optimiser, planner, and runtime code generator. QueryExecution is the core component of the Spark SQL execution engine, which manages the primary workflow of a relational query execution using Spark. IncrementalExecution is a variant of QueryExecution that supports the execution of a logical plan … Continue reading Spark Structured Streaming Deep Dive (5) – IncrementalExecution
Data Ninjago (Financial Data Architecture) 
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