Stream Processing
Stream Processing is a advanced techniques concept in the Data Engineering pillar.
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Explain Stream Processing as if teaching a colleague who is new to advanced techniques. Cover: what it is, how it works, and why it matters.
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Create a diagram that demonstrates Stream Processing in a real-world advanced techniques scenario. Walk through your design decisions.
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A diagram for Stream Processing should include: 1. The core components of streaming 2. How they interact 3. Expected outcomes or outputs
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Streaming ETL for Suspicious Activity Reports: Real-Time AML Data Pipelines with Kafka and Flink
Architecture patterns for building real-time AML surveillance data pipelines using Apache Kafka for transaction ingestio
Real-Time Data Engineering for Algorithmic Trading: From Tick Data to Execution Signals
Production-grade data pipelines for algorithmic trading systems: handling market data at microsecond latency, feature en
Akidau et al (2015) - The Dataflow Model: Balancing Correctness, Latency, and Cost
The Dataflow Model provides a unified framework for batch and stream processing by separating What, Where, When, and How
Exactly-Once Semantics in Stream Processing
How Kafka + Flink/Spark achieve exactly-once processing: idempotent producers, transactional log offsets, and checkpoint
RecHash: Segmented Hash-Entangled Chains for Stateful Sequential Authentication
RecHash is a stateful sequential authentication construction based on paired hash-chain openings and an authenticated re
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