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Topic · IT & Coding
distributed systems
73 shorts across 1 course, in learning order
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Results · 12 for “Real-Time Data Processing”
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⚡ One HLD concept. 60 seconds. Interview ready
Stream Processing Architecture: From Events to Real-Time Insights
Stream Processing Architecture helps turn incoming events into real-time insights efficiently.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Event Time vs Processing Time in Stream Processing
Event time is when an event occurs, while processing time is when it is handled.
⚡ One HLD concept. 60 seconds. Interview ready
HLD: Late-Arriving Events - Drop, Update, or Replay?
Late-arriving events can be dropped, updated, or routed based on system needs.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Watermarks in Stream Processing
Watermarks help stream processing systems manage event time and late events.
⚡ One HLD concept. 60 seconds. Interview ready
Kafka Exactly-Once Processing: Avoiding Duplicates
Kafka can help ensure messages are processed once, but requires careful design.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Lamport Timestamps in Distributed Systems
Lamport Timestamps help order events in distributed systems while preserving causality.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Physical vs Logical Time in Distributed Systems
Physical time tells when events happen, while logical time tells their order.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Apache Kafka for Scalable Event Streaming
Apache Kafka is a system for managing large volumes of events efficiently.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Distributed Clocks in Systems Design
In distributed systems, timestamps don't always reflect the true order of events.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Vector Clocks in Distributed Systems
Vector clocks help track the order and concurrency of events in distributed systems.
⚡ One HLD concept. 60 seconds. Interview ready
Read-After-Write Consistency in Database Systems
Read-after-write consistency ensures users see their latest updates immediately.
⚡ One HLD concept. 60 seconds. Interview ready
Kafka Backpressure Strategies: Handling Traffic Spikes
Backpressure helps manage message flow in Kafka during traffic spikes.
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Not tagged “distributed systems”, but closely connected
