Скачать или смотреть Master Distributed Systems: The Complete Guide to Data Flow & Architecture

Most developers treat their database as the only source of truth, but at scale, this breaks. In this video, we explore why modern architectures are moving away from monolithic databases toward log-based data integration and unified stream processing.
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Designing Data-Intensive Applications || Chapter Summarized


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The Summary:
We break down the complex world of distributed data systems, moving beyond simple CRUD apps to robust, scalable architectures. You'll learn how to keep different data systems (search indexes, caches, analytics) in sync without using fragile distributed transactions. We also cover the convergence of batch and stream processing, how to achieve "exactly-once" semantics in a messy network, and the critical trade-offs between "Write Path" and "Read Path" optimization.
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What You Will Learn:

Data Integration Strategy: Why "Dual Writes" kill consistency and how to use Change Data Capture (CDC) and Event Logs instead.
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Batch vs. Stream Processing: Understanding the Lambda vs. Kappa architecture and how modern tools unify historical replay with real-time events.
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The Idempotence Key: How to solve the "double-charge" problem and achieve exactly-once semantics using operation IDs and deterministic processing.
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Read vs. Write Path: How to optimize latency by shifting work (indexing, caching) to the write path, illustrated by Twitter's fan-out architecture.
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Senior Engineer Reality Checks: Real-world production pitfalls like "Last Writer Wins" lies, state management explosions, and the hidden costs of microservices.
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Target Audience:
Perfect for Backend Engineers, Senior Developers, and anyone preparing for System Design Interviews (L5/L6 level) who wants to understand the deep principles behind tools like Kafka, Flink, and Elasticsearch.

3. SEO Tags & Category
Keywords (15-20):
System Design, Distributed Systems, Data Integration, Stream Processing, Batch Processing, Change Data Capture, CDC, Idempotence, Exactly-Once Semantics, Kafka Architecture, Event Sourcing, CQRS, Microservices Patterns, Software Architecture, Backend Engineering, CAP Theorem, Apache Flink, Data Engineering, DDIA, System Design Interview.

Hashtags:
#SystemDesign #DistributedSystems #SoftwareEngineering #BackendDeveloper #DataArchitecture

YouTube Category:
Science & Technology