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Data Architecture & Persistence

Building reliable, scalable data systems with proven patterns and technologies

Overview​

Data architecture forms the backbone of modern applications. This section explores how to design, implement, and scale data systems that grow with your business while maintaining consistency, performance, and reliability.

Whether you're building a startup MVP or scaling to millions of users, understanding trade-offs between different storage models, data modeling techniques, and persistence patterns is crucial for making informed architectural decisions.

Five Core Pillars​

1. Storage Models & Technologies​

Understand the landscape of modern databases and storage systems:

  • Relational (RDBMS): ACID-compliant, structured data, proven at scale
  • Key-Value Stores: Ultra-fast reads/writes, simple model, cache-friendly
  • Document Stores: Flexible schemas, nested data, query flexibility
  • Wide-Column Stores: Time-series, massive scale, analytical workloads
  • Graph Databases: Relationship-heavy data, complex queries
  • Time-Series Databases: Events, metrics, monitoring at scale
  • Search Engines: Full-text search, relevance ranking, analytics
  • In-Memory Systems: Sub-millisecond latency, session stores
  • Object Storage: Unstructured data, blobs, files at petabyte scale

2. Data Modeling & Access​

Design data structures and access patterns for efficiency:

  • Normalization vs denormalization trade-offs
  • ORM patterns (Active Record, Data Mapper)
  • Query patterns: pagination, filtering, sorting
  • Indexing strategies and performance optimization
  • Multi-tenancy approaches (pooled vs siloed)

3. Performance & Scale​

Implement patterns to handle growth:

  • Caching layers (write-through, write-behind, cache-aside)
  • Read replicas and fan-out architectures
  • Sharding strategies and rebalancing
  • Materialized views for pre-computation
  • Search offloading and aggregation optimization

4. Data Pipelines & Analytics​

Build data movement and transformation at scale:

  • Batch vs streaming trade-offs
  • ETL, ELT, data lakes, and warehouses
  • Event streams and log-based integration
  • Data quality, lineage, and governance
  • ML feature stores and model serving

5. Data Lifecycle & Compliance​

Manage data through its entire journey:

  • Retention policies and archival strategies
  • PII classification, masking, and tokenization
  • Right to erasure and data portability
  • Audit trails and tamper-evident logs

Key Decisions​

When architecting data systems, ask:

QuestionImplications
How consistent must data be?Impacts ACID requirements, replication strategy
What's the read/write ratio?Influences caching, denormalization, replication
How fast must queries complete?Drives indexing, materialized views, search offloading
How much data will we store?Affects sharding, compression, archival
What compliance rules apply?Determines encryption, auditing, retention
How distributed is our system?Impacts consistency models, replication strategy

Learning Path​

Beginner: Start with Storage Models and foundational Data Modeling concepts Intermediate: Explore Performance & Scale patterns and practical optimization Advanced: Deep-dive into Data Pipelines, ML integration, and Compliance

Quick Start Checklist​

Before building your data system:

  • Define consistency requirements (strong vs eventual)
  • Estimate data volume, growth rate, and access patterns
  • Choose appropriate storage models for different data types
  • Plan for replication and backup
  • Design for observability and operational hygiene
  • Map compliance and privacy requirements
  • Plan migration and deployment strategy

Common Pitfalls to Avoid​

  1. Over-normalizing when denormalization would serve better
  2. Ignoring access patterns during schema design
  3. Delaying sharding until performance crisis
  4. Treating compliance as afterthought instead of first-class concern
  5. Single point of failure in critical data systems
  6. Inadequate monitoring until issues reach production
  7. Poor data quality processes at pipeline entry points

References & Further Reading​

  • "Designing Data-Intensive Applications" by Martin Kleppmann
  • "Building Microservices" by Sam Newman
  • DDIA Design Patterns & Architectural patterns
  • Stripe, Uber, Netflix engineering blogs on data architecture
  • Cloud provider documentation (AWS, Azure, GCP) on storage services

Start exploring by diving into Storage Models to understand your technology options, then move through Data Modeling, Performance patterns, Pipelines, and Compliance in sequence.