Day 23: Deterministic Data Generation
Systemdrllp5
Day 23: Deterministic Data Generation
1:30
Day 24: Garbage Collection Tuning: How Java and Go GC Shape Your Latency Profile
Systemdrllp5
Day 24: Garbage Collection Tuning: How Java and Go GC Shape Your Latency Profile
4:21
Day 40: Fallback Logic & Self-Healing
Systemdrllp5
Day 40: Fallback Logic & Self-Healing
2:15
Day 22: Metrics Instrumentation — Observability Without Overhead
Systemdrllp5
Day 22: Metrics Instrumentation — Observability Without Overhead
1:10
Day 23: Tail Latency (P99) Optimization: Why Averages Lie and How to Fix Outliers
Systemdrllp5
Day 23: Tail Latency (P99) Optimization: Why Averages Lie and How to Fix Outliers
2:50
Day 39: The Validator Agent: Risk & Compliance
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Day 39: The Validator Agent: Risk & Compliance
2:04
Day 21: Handling Simulation Backpressure
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Day 21: Handling Simulation Backpressure
2:08
Day 22: Load Shedding and Request Prioritization: Keeping Critical Flows Alive During Outages
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Day 22: Load Shedding and Request Prioritization: Keeping Critical Flows Alive During Outages
1:29
Day 38: The Validator Agent: Factual Consistency
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Day 38: The Validator Agent: Factual Consistency
1:40
Day 20: Concurrency Simulation – Virtual Threads vs Scheduled Executor Service
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Day 20: Concurrency Simulation – Virtual Threads vs Scheduled Executor Service
1:13
Day 21: The Thundering Herd Problem: Mitigation Strategies for Cache Stampedes
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Day 21: The Thundering Herd Problem: Mitigation Strategies for Cache Stampedes
1:42
Day 37: The Retriever Agent & Reranker Tool
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Day 37: The Retriever Agent & Reranker Tool
2:14
Day 19: Path Interpolation – Reconstructing Continuous Movement from Discrete GPS Samples
Systemdrllp5
Day 19: Path Interpolation – Reconstructing Continuous Movement from Discrete GPS Samples
1:42
Day 20: State Management in Stream Processing: How Apache Flink and Kafka Streams Handle State
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Day 20: State Management in Stream Processing: How Apache Flink and Kafka Streams Handle State
2:17
Day 36: The Planner Agent: Query Decomposition
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Day 36: The Planner Agent: Query Decomposition
1:34
Day 19: Live Streaming Architecture: Ingest, Transcoding, and Delivery at Scale
Systemdrllp5
Day 19: Live Streaming Architecture: Ingest, Transcoding, and Delivery at Scale
2:41
Day 18: Modeling the Driver – H3-Aware Movement vs Lat/Lon Chaos
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Day 18: Modeling the Driver – H3-Aware Movement vs Lat/Lon Chaos
1:10
Day 35: Architecting Agentic RAG
Systemdrllp5
Day 35: Architecting Agentic RAG
1:33
Day 18: Understanding Head-of-Line Blocking: HTTP/2 vs. HTTP/3 (QUIC) in Production
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Day 18: Understanding Head-of-Line Blocking: HTTP/2 vs. HTTP/3 (QUIC) in Production
1:27
Day 34: Planning Loop Controls & Budgeting
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Day 34: Planning Loop Controls & Budgeting
2:42
Day 17: Compression Algorithms - The Physics of Batch Compression in Geospatial Streams
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Day 17: Compression Algorithms - The Physics of Batch Compression in Geospatial Streams
1:06
Day 17 : Optimistic Locking vs. Pessimistic Locking: Handling Concurrency in High-Traffic Systems
Systemdrllp5
Day 17 : Optimistic Locking vs. Pessimistic Locking: Handling Concurrency in High-Traffic Systems
1:35
Day 33: Implementing Self-Correction (Reflexion)
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Day 33: Implementing Self-Correction (Reflexion)
1:20
Day 16: Artificial Latency (linger.ms) — The Producer Batching Lever
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Day 16: Artificial Latency (linger.ms) — The Producer Batching Lever
1:30
Day 16: Designing a Distributed Job Scheduler: Handling Delayed and Recurring Tasks
Systemdrllp5
Day 16: Designing a Distributed Job Scheduler: Handling Delayed and Recurring Tasks
2:24
Day 15: Batching Physics - The Record Accumulator's Role in High-Velocity Geo-Spatial Streams
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Day 15: Batching Physics - The Record Accumulator's Role in High-Velocity Geo-Spatial Streams
0:53
Day 32: Hands-On: ReAct Agent Build
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Day 32: Hands-On: ReAct Agent Build
1:56
Day 15: MQTT vs. CoAP: IoT Protocols for Real-Time Device Communication
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Day 15: MQTT vs. CoAP: IoT Protocols for Real-Time Device Communication
1:45
Day 31: Deep Dive into ReAct Planning
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Day 31: Deep Dive into ReAct Planning
2:36
Day 14: Asynchronous I/O – Why Synchronous Send() Kills Throughput
Systemdrllp5
Day 14: Asynchronous I/O – Why Synchronous Send() Kills Throughput
1:01
Day 14: Edge Caching Dynamic Content: Strategies for Reducing Latency for Global Users
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Day 14: Edge Caching Dynamic Content: Strategies for Reducing Latency for Global Users
1:24
Day 30: Introduction to Observability for RAG
Systemdrllp5
Day 30: Introduction to Observability for RAG
2:50
Day 13: The Kafka Producer API – Engineering for 3,000+ Events/Second
Systemdrllp5
Day 13: The Kafka Producer API – Engineering for 3,000+ Events/Second
1:02
Day 13: Server-Side Rendering (SSR) vs. Client-Side Rendering (CSR): Performance Implications
Systemdrllp5
Day 13: Server-Side Rendering (SSR) vs. Client-Side Rendering (CSR): Performance Implications
1:45
Day 12: Topic Engineering – The Immutable Scaling Contract
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Day 12: Topic Engineering – The Immutable Scaling Contract
1:14
Day 12: Real-Time Ad Bidding Systems (RTB): Designing for less than 100ms Responses
Systemdrllp5
Day 12: Real-Time Ad Bidding Systems (RTB): Designing for less than 100ms Responses
1:23
Day 28: Project 2: Tool-Equipped Agent Build
Systemdrllp5
Day 28: Project 2: Tool-Equipped Agent Build
3:05
Day 29: Project 2 Review: Accuracy & Latency
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Day 29: Project 2 Review: Accuracy & Latency
1:29
Day 11: Configuration Architecture for Distributed Streaming Systems
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Day 11: Configuration Architecture for Distributed Streaming Systems
1:05
Day 11: Kappa vs. Lambda Architecture: Evolution of Big Data Processing Pipelines
Systemdrllp5
Day 11: Kappa vs. Lambda Architecture: Evolution of Big Data Processing Pipelines
2:14
Day 10: Production-Grade Dependency Management for Kafka Streams with Native Libraries
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Day 10: Production-Grade Dependency Management for Kafka Streams with Native Libraries
1:05
Day 9: Project Initialization
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Day 9: Project Initialization
0:58
Day 27: RAG Evaluation Metrics & Benchmarks
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Day 27: RAG Evaluation Metrics & Benchmarks
1:33
Day 10. Handling Late Data and Watermarks: Accuracy in Real-Time Stream Processing
Systemdrllp5
Day 10. Handling Late Data and Watermarks: Accuracy in Real-Time Stream Processing
2:04
Day 9: Windowing Strategies in Stream Processing: Tumbling, Sliding, and Session Windows Explained
Systemdrllp5
Day 9: Windowing Strategies in Stream Processing: Tumbling, Sliding, and Session Windows Explained
2:18
Day 8: Push vs. Pull Architectures in Real-Time Systems: When to Poll and When to Push
Systemdrllp5
Day 8: Push vs. Pull Architectures in Real-Time Systems: When to Poll and When to Push
1:19
Day 181: The Future of System Design: Emerging Patterns
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Day 181: The Future of System Design: Emerging Patterns
1:40
Day 180: System Design for AI-Powered Applications
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Day 180: System Design for AI-Powered Applications
2:21