📘 Designing Data-Intensive Applications – (2025–2026 Edition)

📚 Designing Data-Intensive Applications (2025–2026 Edition) is a structured, academic, and syllabus-based resource created for BS/CS, BS/IT, Software Engineering students, and aspiring data engineers. This app provides comprehensive notes, MCQs, and quizzes to support learning, exam preparation, and interview readiness. With a clear layout and detailed coverage, it helps learners master modern data systems and application design with confidence.

This edition covers fundamental to advanced topics including reliability, scalability, maintainability, data models, encoding, replication, partitioning, transactions, batch and stream processing, and emerging data technologies. Designed around a syllabus format, it provides a step-by-step learning path that ensures a strong foundation for both academic study and professional development.

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📂 Chapters & Topics

🔹 Chapter 1: Foundations of Data Systems
- Importance of Data-Intensive Applications
- Characteristics of Reliable, Scalable, and Maintainable Systems
- Balancing Consistency, Availability, and Latency

🔹 Chapter 2: Data Models and Query Languages
- Relational Models
- Document and Graph Models
- Declarative vs. Imperative Queries

🔹 Chapter 3: Storage and Retrieval
- Disk and Memory-Based Storage
- Indexes and B-Trees
- Hash Indexes and Log-Structured Storage

🔹 Chapter 4: Encoding and Evolution
- Data Encoding Formats (JSON, XML, Avro, Protocol Buffers)
- Schema Evolution and Compatibility
- Handling Migrations

🔹 Chapter 5: Replication
- Leader-Based Replication
- Multi-Leader Replication
- Leaderless Replication
- Challenges of Data Synchronization

🔹 Chapter 6: Partitioning
- Horizontal vs. Vertical Partitioning
- Partitioning Strategies
- Rebalancing and Scalability Tradeoffs

🔹 Chapter 7: Transactions
- ACID Properties
- Isolation Levels
- Serializability and Concurrency Control

🔹 Chapter 8: The Trouble with Distributed Systems
- Network Faults
- Time and Clocks in Distributed Systems
- Consensus Problems

🔹 Chapter 9: Consistency and Consensus
- Linearizability
- CAP Theorem
- Distributed Consensus (Paxos, Raft)

🔹 Chapter 10: Batch Processing
- MapReduce Fundamentals
- Dataflow Systems
- Parallel Execution Models

🔹 Chapter 11: Stream Processing
- Event-Driven Architectures
- State Management in Streams
- Fault Tolerance in Stream Systems

🔹 Chapter 12: Combining Batch and Stream Processing
- Lambda Architecture
- Kappa Architecture
- Real-Time Analytics

🔹 Chapter 13: Designing for Reliability
- Fault Detection and Recovery
- Idempotence and Retry Mechanisms
- Ensuring Durability

🔹 Chapter 14: Designing for Scalability
- Load Balancing
- Caching Strategies
- Elastic Scaling

🔹 Chapter 15: Designing for Maintainability
- Operability Principles
- Schema Design and Evolution
- Monitoring and Observability

🔹 Chapter 16: The Future of Data Systems
- Emerging Trends in Data Engineering
- Cloud-Native Data Infrastructure
- The Evolution of Databases

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🌟 Why Choose this App?
- Covers the complete Designing Data-Intensive Applications syllabus in structured academic format.
- Includes MCQs and quizzes for thorough practice and exam readiness.
- Provides clear notes for quick revision and deep conceptual understanding.
- Supports projects, coursework, and technical interviews with reliable content.
- Builds strong foundations in data systems and large-scale application design.

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✍ This app is inspired by the authors:
Martin Kleppmann, Peter Haase, Benjamin S. Blanchard, E. Edward Lowery, Eric A. Brewer

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📥 Download Now!
Get your Designing Data-Intensive Applications (2025–2026 Edition) today and start building reliable, scalable, and maintainable systems with confidence!