Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

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A comprehensive technical deep-dive into the fundamental principles of building robust data systems at scale, inspired by Martin Kleppmann's seminal work.

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Martin Kleppmann’s “Designing Data-Intensive Applications” helps us understand the fundamental trade-offs that every distributed system designer faces: consistency vs. availability, latency vs. throughput, and complexity vs. simplicity.

In this technical deep-dive, I’ll explore the core concepts that make data-intensive applications reliable, scalable, and maintainable. We’ll go beyond surface-level explanations to understand the underlying principles that govern modern data systems.

The Three Pillars: Reliability, Scalability, and Maintainability

Reliability: The Foundation of Trust

Reliability is the system’s ability to continue functioning correctly even when things go wrong. In large-scale systems, failures are not exceptional—they’re the norm.

Hardware Faults

Disk failures: With 10,000 disks, expect daily failures
Memory corruption: ECC memory helps, but cosmic rays still cause bit flips
Network partitions: Temporary network issues that split clusters
Power failures: UPS systems and graceful shutdown procedures

Software Faults

Bugs: Even well-tested code has edge cases
Resource leaks: Memory leaks, file descriptor exhaustion
Cascading failures: One failure triggering others
Clock drift: NTP synchronization issues in distributed systems

Human Errors

Configuration mistakes: Wrong timeout values, misconfigured load balancers
Deployment failures: Rolling back to previous versions
Operational errors: Accidentally deleting production data

Fault Tolerance Strategies

Redundancy: Multiple copies of critical components
Graceful degradation: System continues with reduced functionality
Circuit breakers: Prevent cascading failures
Chaos engineering: Proactively testing failure scenarios

Scalability: Growing Without Pain

Scalability is the system’s ability to handle increased load gracefully. Kleppmann emphasizes that scalability isn’t just about performance—it’s about the ability to add resources proportionally.

Load Parameters

Web applications: Requests per second, concurrent users
Databases: Read/write ratios, data size, query complexity
Batch processing: CPU time, I/O bandwidth, memory usage
Real-time systems: Event frequency, processing latency

Performance Characteristics

Throughput: Number of records processed per second
Response time: Time to process a single request
Latency percentiles: p50, p95, p99, p999
Tail latency: Worst-case performance matters more than average

Scaling Strategies

Vertical scaling: Adding more resources to a single machine
Horizontal scaling: Adding more machines to the cluster
Functional scaling: Separating different types of workloads
Geographic scaling: Distributing systems across regions

Scalability Patterns

Load balancing: Distributing requests across multiple servers
Caching: Reducing load on backend systems
Asynchronous processing: Handling non-time-critical operations
Database sharding: Distributing data across multiple databases

Maintainability: The Long Game

Maintainability ensures that engineers can work on the system productively over time. This is often overlooked but critical for long-term success.

Operability

Monitoring: Comprehensive metrics and alerting
Deployment: Automated, safe deployment processes
Troubleshooting: Clear logging and debugging tools
Documentation: Up-to-date operational procedures

Simplicity

Abstraction: Hiding complexity behind clean interfaces
Modularity: Breaking systems into manageable components
Consistency: Using similar patterns throughout the system
Elimination: Removing unnecessary features and complexity

Evolvability

Backward compatibility: New versions work with old data
Schema evolution: Changing data structures safely
API versioning: Managing interface changes
Feature flags: Gradual rollout of new functionality

Data Models and Query Languages

The Relational Model: Mathematical Foundation

The relational model’s greatest strength is its mathematical foundation in set theory and predicate logic. This provides:

Mathematical Properties

Set operations: Union, intersection, difference, Cartesian product
Relational algebra: Selection, projection, join operations
Functional dependencies: Normalization theory
ACID transactions: Atomicity, consistency, isolation, durability

SQL Advantages

Declarative: Describe what you want, not how to get it
Query optimization: Database chooses efficient execution plans
ACID guarantees: Transactional consistency
Schema enforcement: Data integrity constraints

Normalization Trade-offs

First Normal Form (1NF): Atomic values, no repeating groups
Second Normal Form (2NF): No partial dependencies
Third Normal Form (3NF): No transitive dependencies
Boyce-Codd Normal Form (BCNF): Every determinant is a candidate key

Document Model: Schema Flexibility

Document databases (MongoDB, CouchDB) offer different trade-offs:

Advantages

Schema flexibility: Fields can be added/removed without migrations
Better locality: Related data stored together
Elimination of joins: Data is denormalized
Natural mapping: Objects map directly to application code

Challenges

Referential integrity: No foreign key constraints
Complex queries: Limited support for joins and aggregations
Schema evolution: No built-in schema validation
Transaction support: Limited multi-document transactions

Use Cases

Content management: Blog posts, articles, product catalogs
Real-time analytics: Event data, user behavior tracking
Mobile applications: Offline-first data synchronization
IoT data: Time-series data with varying schemas

Graph Models: Complex Relationships

When relationships become complex, graph models excel:

Property Graphs

Nodes: Entities with properties
Edges: Relationships with properties
Labels: Categorizing nodes and edges
Indexes: Fast traversal of specific patterns

Triple Stores

Subject: The entity being described
Predicate: The relationship or property
Object: The value or related entity
RDF: Resource Description Framework standard

Graph Algorithms

Shortest path: Finding optimal routes
PageRank: Measuring node importance
Community detection: Identifying clusters
Recommendation: Finding similar entities

Use Cases

Social networks: Friend relationships, content sharing
Knowledge graphs: Semantic relationships, ontologies
Fraud detection: Identifying suspicious patterns
Recommendation systems: Finding related items

Storage and Retrieval

Log-Structured Storage: The Power of Append-Only

The log is the most fundamental data structure in computing. It’s append-only, immutable, and provides a complete audit trail.

Write-Ahead Logging (WAL)

Durability guarantee: Changes logged before acknowledgment
Crash recovery: Replay log to restore state
Performance: Sequential writes are much faster than random writes
Implementation: fsync() calls ensure disk persistence

Replication Logs

Change propagation: Shipping changes to followers
Ordering: Maintaining operation sequence across nodes
Conflict resolution: Handling concurrent modifications
Lag monitoring: Tracking replication delays

Event Sourcing

Complete history: All changes stored as events
Temporal queries: Understanding system state at any point
Audit trails: Compliance and debugging requirements
State reconstruction: Deriving current state from events

Log Compaction

Space efficiency: Removing obsolete entries
Recovery speed: Starting from recent snapshots
Compaction strategies: Size-tiered, leveled, time-windowed
Garbage collection: Managing log growth

B-Trees: The Workhorse of Relational Databases

B-trees have been the foundation of relational databases for decades:

Structure

Balanced tree: All leaf nodes at same level
Page-based: Nodes aligned with disk sectors (typically 4KB)
Key ordering: Sorted keys for efficient range queries
Fan-out: High branching factor minimizes tree height

Operations

Search: O(log n) complexity with good cache locality
Insert: Split nodes when they become full
Delete: Merge nodes when they become too empty
Range queries: Efficient traversal of key ranges

Optimizations

Prefix compression: Sharing common key prefixes
Suffix truncation: Removing unnecessary key parts
Bulk loading: Efficient initial tree construction
Write optimization: Minimizing disk I/O

Write Amplification

In-place updates: Modifying existing pages
Page splits: Creating new pages during inserts
Rebalancing: Maintaining tree balance
Logging overhead: WAL and checkpoint operations

LSM-Trees: Write-Optimized Storage

Log-Structured Merge Trees optimize for write-heavy workloads:

Components

Memtable: In-memory buffer for recent writes
SSTables: Immutable sorted string tables on disk
Bloom filters: Probabilistic membership tests
Compaction: Background merging of SSTables

Write Path

Buffering: Writes accumulate in memtable
Flushing: Memtable flushed to disk when full
Sorting: Keys sorted within each SSTable
Indexing: Creating metadata for fast lookups

Read Path

Bloom filter: Quick check if key might exist
Memtable check: Look in recent writes first
SSTable search: Binary search within sorted files
Merge logic: Combining results from multiple levels

Compaction Strategies

Size-tiered: Merging files of similar sizes
Leveled: Maintaining sorted runs at each level
Time-windowed: Partitioning by time
Hybrid approaches: Combining multiple strategies

Trade-offs

Write performance: Excellent for high-write workloads
Read performance: Multiple SSTable lookups required
Space amplification: Multiple copies during compaction
Write amplification: Background compaction overhead

Encoding and Evolution

Data Formats: Choosing the Right Representation

Different formats offer different trade-offs:

JSON

Human-readable: Easy to debug and inspect
Schema-less: Flexible structure evolution
Verbose: Larger size compared to binary formats
Parsing overhead: Slower than binary formats

Protocol Buffers

Compact: Efficient binary encoding
Schema evolution: Forward/backward compatibility
Code generation: Type-safe language bindings
Validation: Runtime schema checking

Avro

Schema resolution: Runtime schema evolution
Compact: Efficient binary encoding
Dynamic typing: No code generation required
Schema registry: Centralized schema management

MessagePack

JSON-compatible: Similar data model to JSON
Binary format: More compact than JSON
Fast parsing: Optimized for performance
Language support: Available in many languages

Schema Evolution: Managing Change Over Time

Schema evolution is critical for long-lived systems:

Compatibility Types

Backward compatibility: New code reads old data
Forward compatibility: Old code reads new data
Full compatibility: Both directions work
Breaking changes: Incompatible schema modifications

Evolution Strategies

Additive changes: Always add optional fields
Removal policies: Never remove required fields
Type changes: Use union types for major changes
Versioning: Explicit schema version management

Implementation Patterns

Schema registry: Centralized schema storage
Runtime validation: Checking data against schemas
Migration tools: Automated schema updates
Rollback procedures: Reverting to previous schemas

Best Practices

Plan for evolution: Design schemas with change in mind
Test compatibility: Verify both directions work
Document changes: Clear migration procedures
Monitor usage: Track schema adoption and issues

Replication

Leader-Based Replication: The Classic Approach

Leader-based replication is the most common strategy:

Architecture

Leader: Single node handling all writes
Followers: Receiving and applying write-ahead logs
Read replicas: Handling read-only queries
Failover: Automatic leader promotion on failure

Replication Modes

Synchronous: Wait for all followers to acknowledge
Asynchronous: Don’t wait for follower acknowledgment
Semi-synchronous: Wait for some followers
Mixed modes: Different policies for different operations

Challenges

Replication lag: Followers may be seconds behind
Failover detection: Determining when leader has failed
Split-brain: Multiple leaders in partitioned network
Consistency: Ensuring all replicas eventually converge

Solutions

Heartbeats: Regular leader health checks
Timeouts: Configurable failure detection
Consensus algorithms: Raft, Paxos for leader election
Quorum reads: Ensuring consistency across replicas

Multi-Leader Replication: Write Availability

When you need write availability across multiple data centers:

Advantages

Better availability: Writes succeed even if some DCs fail
Lower latency: Writes go to nearest data center
Offline operation: Local writes when disconnected
Geographic distribution: Global write availability

Challenges

Conflict resolution: Handling concurrent writes
Eventual consistency: Temporary inconsistencies
Complex topology: Managing multiple write paths
Monitoring: Tracking replication across DCs

Conflict Resolution Strategies

Last-write-wins: Simple but can lose data
Application-specific: Custom merge logic
CRDTs: Conflict-free replicated data types
Operational transformation: Text editing algorithms

Use Cases

Multi-datacenter deployments: Global applications
Offline-first applications: Mobile and desktop apps
Collaborative editing: Google Docs, Figma
IoT systems: Edge computing with local writes

Leaderless Replication: Dynamo-Style

Dynamo-style replication eliminates single points of failure:

Architecture

No leader: All nodes can handle writes
Quorum operations: W and R nodes must agree
Vector clocks: Tracking causal relationships
Hinted handoff: Handling temporary node failures

Quorum Configuration

W + R > N: Ensuring consistency
W = N/2 + 1: Majority writes for safety
R = N/2 + 1: Majority reads for consistency
Tunable consistency: Adjusting W and R values

Conflict Resolution

Vector clocks: Detecting concurrent writes
Last-write-wins: Using timestamps or version numbers
Application merge: Custom conflict resolution logic
CRDTs: Mathematical conflict resolution

Advantages

No single point of failure: Better availability
Lower latency: No leader coordination overhead
Geographic distribution: Global write availability
Fault tolerance: Handles arbitrary node failures

Partitioning

Partitioning Strategies: Distributing Data

Partitioning is essential for handling large datasets:

Key-Range Partitioning

Sorted keys: Data ordered by key values
Range queries: Efficient for sequential access
Hotspot risk: Popular key ranges can overload nodes
Rebalancing: Moving ranges between nodes

Hash Partitioning

Even distribution: Hash function spreads load evenly
Range query performance: Poor for sequential access
Rebalancing complexity: Moving individual keys
Consistent hashing: Minimizing data movement

Consistent Hashing

Virtual nodes: Improving load distribution
Minimal disruption: Adding/removing nodes efficiently
Hash ring: Circular key space
Replication: Multiple copies for fault tolerance

Composite Partitioning

Multi-level: Combining multiple strategies
Functional partitioning: Different data types on different nodes
Time-based: Partitioning by time periods
Hybrid approaches: Best of multiple strategies

Partitioning and Secondary Indexes

Secondary indexes introduce complexity in partitioned systems:

Local Secondary Indexes

Per-partition: Each partition maintains its own indexes
Scatter-gather: Querying all partitions for results
Simple maintenance: No cross-partition coordination
Query performance: Slower for global queries

Global Secondary Indexes

Cross-partition: Index spans all partitions
Complex maintenance: Coordinating updates across partitions
Better query performance: Single index lookup
Consistency challenges: Maintaining index consistency

Index Maintenance Strategies

Synchronous updates: Index updated with data
Asynchronous updates: Background index maintenance
Eventual consistency: Indexes may lag behind data
Bulk operations: Efficient index rebuilding

Query Routing

Index location: Knowing which partition has the index
Query planning: Optimizing multi-partition queries
Result aggregation: Combining results from partitions
Caching: Storing frequently accessed results

Transactions

ACID Properties: The Transaction Foundation

ACID transactions provide strong guarantees:

Atomicity

All-or-nothing: Either all operations succeed or none do
Rollback: Automatic cleanup on failure
Implementation: Undo logs or shadow pages
Recovery: Handling system crashes during transactions

Consistency

Valid state: Database moves between valid states
Constraints: Referential integrity, check constraints
Application logic: Business rules enforced
Invariants: System properties maintained

Isolation

Concurrent execution: Multiple transactions can run simultaneously
Serializability: Equivalent to some serial execution
Anomalies: Preventing race conditions
Locking: Controlling access to shared resources

Durability

Permanent storage: Committed transactions survive crashes
Write-ahead logging: Changes logged before acknowledgment
Checkpointing: Periodic state persistence
Recovery procedures: Restoring state after failures

Isolation Levels: Balancing Consistency and Performance

Different isolation levels offer different guarantees:

Read Uncommitted

No isolation: Dirty reads possible
Performance: No locking overhead
Use cases: Reporting, analytics
Risks: Inconsistent data, incorrect results

Read Committed

Basic isolation: No dirty reads
Non-repeatable reads: Same query may return different results
Implementation: Row-level locks or MVCC
Performance: Moderate overhead

Repeatable Read

Stronger isolation: No dirty or non-repeatable reads
Phantom reads: Range queries may return different rows
Implementation: Snapshot isolation
Use cases: Financial transactions, inventory management

Serializable

Strongest isolation: No anomalies possible
Performance impact: Higher overhead, potential deadlocks
Implementation: Strict two-phase locking
Use cases: Critical financial systems, booking systems

Common Anomalies

Dirty Read

Transaction A reads uncommitted data from Transaction B
Transaction B rolls back, leaving A with invalid data
Prevention: Read locks or MVCC

Non-Repeatable Read

Transaction A reads a row, Transaction B updates it
Transaction A reads the same row again, gets different data
Prevention: Row-level locks or snapshot isolation

Phantom Read

Transaction A reads a range, Transaction B inserts matching rows
Transaction A reads the same range again, gets additional rows
Prevention: Range locks or serializable isolation

Write Skew

Two transactions read the same data, make conflicting updates
Each update is valid, but combination violates constraints
Prevention: Serializable isolation or application-level checks

Distributed Transactions

Two-Phase Commit (2PC): The Classic Approach

2PC provides atomicity across multiple nodes:

Phase 1: Prepare

Coordinator sends prepare message to all participants
Participants perform local validation and logging
Participants respond with prepare/abort decision
Coordinator waits for all responses

Phase 2: Commit

If all participants prepared successfully, send commit
If any participant aborted, send abort to all
Participants perform local commit/abort
Coordinator waits for acknowledgments

Failure Scenarios

Participant failure: Coordinator can abort or retry
Coordinator failure: Participants may be left in uncertain state
Network partition: Some participants may commit, others abort
Timeout handling: Deciding when to abort

Problems with 2PC

Blocking: Participants may be blocked indefinitely
Performance: Multiple round trips required
Scalability: Coordinator becomes bottleneck
Recovery complexity: Handling uncertain participants

Alternative Approaches

Saga Pattern

Long-running transactions broken into local transactions
Compensating transactions for rollback
Event-driven coordination
Use cases: Microservices, long-running workflows

Event Sourcing

Store all changes as sequence of events
Current state derived by replaying events
Enables audit trails and temporal queries
Challenges: Event schema evolution, storage requirements

CQRS (Command Query Responsibility Segregation)

Separate read and write models
Write model: Command handlers, event stores
Read model: Optimized for specific query patterns
Benefits: Performance, scalability, flexibility

Outbox Pattern

Local transaction writes to outbox table
Background process publishes events
Ensures exactly-once delivery
Use cases: Event-driven architectures

Consistency and Consensus

Linearizability: The Strongest Consistency

Linearizability provides the strongest consistency guarantees:

Definition

Operations appear to happen atomically at some point in time
All nodes see operations in the same order
Real-time ordering preserved
Example: Distributed lock service

Implementation

Single-leader replication: All writes go through leader
Consensus algorithms: Raft, Paxos for ordering
Global timestamps: Lamport clocks or vector clocks
Synchronization: Coordinating across all nodes

Challenges

Performance impact: Operations must be ordered globally
Availability trade-off: CAP theorem implications
Network partitions: Linearizability impossible during partitions
Complexity: Hard to implement correctly

Use Cases

Distributed locks: Ensuring exclusive access
Leader election: Single coordinator
Unique constraints: Preventing duplicate keys
Financial systems: Ensuring transaction ordering

Eventual Consistency: Weaker but Available

Eventual consistency accepts temporary inconsistencies:

Definition

All replicas eventually converge to same state
Temporary inconsistencies acceptable
Better availability and performance
Example: DNS, CDN content

Trade-offs

Better availability: System continues during partitions
Higher performance: No global coordination required
Temporary inconsistencies: Application must handle them
Complex reasoning: Hard to reason about system state

Implementation Patterns

Vector clocks: Tracking causal relationships
Conflict resolution: Merging concurrent changes
Anti-entropy: Background synchronization
Read repair: Fixing inconsistencies during reads

Use Cases

Content delivery: CDNs, social media feeds
Offline applications: Mobile apps, desktop software
Real-time collaboration: Google Docs, Figma
IoT systems: Edge computing with eventual sync

Consensus Algorithms: Agreement in Distributed Systems

Consensus algorithms enable nodes to agree on values:

Paxos

Classic algorithm: Foundation for many systems
Complex to understand: Hard to implement correctly
Used by: Google’s Chubby, Apache ZooKeeper
Three roles: Proposers, acceptors, learners

Raft

Designed for understandability: Easier than Paxos
Leader-based: Single leader handles all requests
Log replication: Maintaining consistent logs
Used by: etcd, Consul, many distributed databases

Byzantine Fault Tolerance

Handles arbitrary failures: Malicious behavior
More expensive: Higher message complexity
Use cases: Blockchain, secure systems
Algorithms: PBFT, Tendermint

Consensus Properties

Safety: No two nodes decide different values
Liveness: Eventually some value is decided
Termination: All correct nodes eventually decide
Validity: Only proposed values can be decided

Stream Processing

Event Streams: The Foundation of Modern Systems

Event streams are becoming the backbone of data systems:

Event Sourcing

Complete history: All changes stored as events
Current state: Derived by replaying events
Audit trails: Compliance and debugging
Temporal queries: Understanding state at any point

Benefits

Debugging: Replay events to reproduce issues
Analytics: Analyze patterns over time
Compliance: Complete audit trail
Flexibility: Multiple read models from same events

Challenges

Storage: Events accumulate over time
Performance: Replaying long event histories
Schema evolution: Changing event structures
Complexity: More complex than traditional CRUD

Implementation

Event store: Append-only log of events
Aggregates: Domain objects with event sourcing
Snapshots: Periodic state checkpoints
Projections: Read models built from events

CQRS: Separating Read and Write Concerns

Command Query Responsibility Segregation separates different operations:

Command Side

Command handlers: Processing write operations
Event stores: Storing domain events
Aggregates: Enforcing business rules
Validation: Input validation and business logic

Query Side

Read models: Optimized for specific queries
Projections: Building read models from events
Caching: Storing frequently accessed data
Optimization: Denormalization for performance

Benefits

Performance: Optimize each side independently
Scalability: Scale read and write separately
Flexibility: Different storage for different needs
Maintainability: Clear separation of concerns

Challenges

Complexity: More complex than traditional CRUD
Eventual consistency: Read models may lag behind
Data synchronization: Keeping read models up to date
Learning curve: New patterns and concepts

Stream Processing Systems

Apache Kafka

Distributed commit log: High-throughput message broker
Fault tolerance: Replicated across multiple brokers
Use cases: Event streaming, message queues
Features: Exactly-once delivery, stream processing

Apache Flink

Stream processing: Real-time data processing
Exactly-once semantics: Ensuring data consistency
Stateful computations: Maintaining state across events
Use cases: Real-time analytics, fraud detection

Apache Spark Streaming

Micro-batch processing: Processing data in small batches
Integration: Works with batch processing
Use cases: Near real-time analytics
Features: Fault tolerance, scalability

Stream Processing Patterns

Windowing: Processing data in time windows
Joins: Combining multiple streams
Aggregations: Computing statistics over streams
Pattern matching: Detecting sequences of events

The Future of Data Systems

Trends and Challenges

Unbundling the Database

Specialized systems: Different tools for different workloads
Polyglot persistence: Use the right tool for each job
Microservices: Each service with its own data store
Data mesh: Decentralized data ownership

Machine Learning Integration

ML models: Part of data pipelines
Real-time features: Computing features on streams
Automated decisions: ML-driven system behavior
Model serving: Deploying models in production

Privacy and Compliance

GDPR, CCPA: Data protection regulations
Data lineage: Tracking data origins and transformations
Governance: Data quality and access control
Differential privacy: Protecting individual privacy

Edge Computing

Local processing: Computing closer to data sources
Reduced latency: Faster response times
Bandwidth optimization: Processing data locally
Offline operation: Working without network connectivity

Emerging Technologies

NewSQL Databases

ACID transactions: Traditional database guarantees
Horizontal scaling: Distributed across multiple nodes
SQL compatibility: Familiar query language
Examples: CockroachDB, TiDB, YugabyteDB

Time-Series Databases

Optimized storage: Efficient for time-ordered data
Compression: Reducing storage requirements
Aggregation: Pre-computing common queries
Examples: InfluxDB, TimescaleDB, Prometheus

Graph Databases

Relationship modeling: Natural for complex relationships
Traversal queries: Finding paths between entities
Graph algorithms: PageRank, community detection
Examples: Neo4j, Amazon Neptune, ArangoDB

Vector Databases

Similarity search: Finding similar vectors
Embedding storage: Storing ML model embeddings
Semantic search: Finding conceptually similar items
Examples: Pinecone, Weaviate, Qdrant

Conclusion

Designing data-intensive applications requires understanding fundamental trade-offs between consistency, availability, and partition tolerance. Kleppmann’s book teaches us that there are no silver bullets—every design decision involves trade-offs that must be made based on specific requirements.

The key principles are:

Start simple: Begin with the simplest solution that meets your needs
Understand trade-offs: Every choice has consequences
Plan for failure: Design for the failures you’ll encounter
Monitor everything: Observability is crucial for production systems
Evolve complexity: Add complexity only when necessary

Remember: Simplicity is the ultimate sophistication. The best data systems are those that solve real problems with the minimum necessary complexity, not those that showcase the most advanced techniques.

Whether you’re building a simple web application or a global distributed system, these principles provide the foundation for making informed architectural decisions. The goal is not to build the most complex system possible, but to build the simplest system that meets your requirements for reliability, scalability, and maintainability.

This blog post is inspired by Martin Kleppmann’s “Designing Data-Intensive Applications.” For a deeper dive into any of these concepts, I highly recommend reading the book itself—it’s an essential resource for anyone building systems that handle data at scale.

The book covers these topics in much greater detail, with real-world examples, implementation details, and practical advice for system designers and engineers.