Unit 3 DS - Distributed Data Storage and Retrieval - PowerPoint Content.pdf

Unlock the core principles behind Distributed Data Storage and Retrieval with this brief presentation by Dr. Ayesha Butalia. Explore how modern systems achieve scalability, fault tolerance, and low latency through replication models, consistency mechanisms, Distributed Hash Tables (DHTs), distributed file systems, and NoSQL databases.

Check https://www.slideshare.net/slideshow/behind-the-scenes-at-netflix-distributed-systems-nosql-architecture-create-description/283270842

Featuring real-world case studies from Netflix, Uber, and Facebook, this presentation bridges theory with industry practice—showing how global tech leaders manage petabytes of data with near-perfect uptime. Ideal for students, researchers, and cloud professionals, this guide provides a complete view of how distributed systems power the data-driven world.

1. Distributed Data Storage and Retrieval
PowerPoint Presentation Content
Slide 1: Title Slide
Distributed Data Storage and Retrieval
Replication, Consistency Models, DHTs, Distributed File Systems, and NoSQL Databases
Faculty Coordinator: Dr. Ayesha Butalia
Slide 2: Agenda
Introduction to Distributed Data Storage
Replication and Consistency Models
Distributed Hash Tables (DHTs)
Distributed File Systems
NoSQL Databases and Applications
Case Studies and Real-World Examples
Performance Analysis and Trade-offs
Future Trends and Conclusion
Slide 3: Introduction to Distributed Data Storage
Why Distributed Data Storage? As data volumes grow exponentially, traditional centralized storage
systems face limitations in scalability, availability, and performance.
Key Challenges:
Scalability: Handling petabytes of data across thousands of nodes
Availability: Ensuring 99.99% uptime despite hardware failures
Consistency: Maintaining data integrity across replicas
Performance: Achieving low latency and high throughput
Partition Tolerance: Operating during network splits
Benefits:
Horizontal scalability
Fault tolerance through redundancy

2. Geographic distribution
Cost-effective storage solutions
Improved performance through parallelism
Slide 4: CAP Theorem - Fundamental Trade-offs
CAP Theorem States: Any distributed system can guarantee only two out of three properties:
Consistency (C):
All nodes see the same data simultaneously
Strong consistency requires synchronization
Example: Traditional RDBMS with ACID properties
Availability (A):
System remains operational even during failures
Every request receives a response
Example: DNS system, web caches
Partition Tolerance (P):
System continues despite network partitions
Essential for distributed systems
Example: Systems spanning multiple data centers
Real-World Implications:
CA Systems: Traditional databases (MySQL, PostgreSQL)
CP Systems: MongoDB, Redis Cluster
AP Systems: Amazon DynamoDB, Cassandra
Slide 5: Replication Models
Primary-Backup Replication:
Single primary handles all writes
Backups serve read requests
Simple consistency model
Example: MySQL Master-Slave setup
Multi-Master Replication:

3. Multiple nodes accept writes
Conflict resolution required
Higher availability but complex consistency
Example: CouchDB, Cassandra
Chain Replication:
Writes propagate through ordered chain
Strong consistency with good performance
Example: Microsoft Azure Storage
Quorum-Based Replication:
Majority consensus for operations
Configurable consistency levels
Example: Amazon DynamoDB, Apache Cassandra
Slide 6: Consistency Models - Spectrum
Strong Consistency:
All replicas have identical data at all times
Synchronous updates across all replicas
Higher latency but guaranteed correctness
Example: Google Spanner, traditional RDBMS
Eventual Consistency:
Replicas converge to same state eventually
Asynchronous propagation of updates
Better performance but temporary inconsistencies
Example: Amazon DynamoDB, DNS system
Causal Consistency:
Causally related operations are seen in same order
Concurrent operations may be seen differently
Balance between performance and consistency
Example: Apache Cassandra with lightweight transactions
Session Consistency:

4. Consistency within a single session
Different sessions may see different states
Common in web applications
Example: Social media platforms
Slide 7: Consistency Models - Implementation Examples
Strong Consistency Example - Banking System:
All replicas must confirm before transaction commits
Ensures account balances are always consistent
Higher latency but critical for financial accuracy
Eventual Consistency Example - Social Media:
Post appears immediately for the user
Friends may see the post with slight delay
Acceptable for social media applications
Prioritizes user experience over immediate consistency
Slide 8: Distributed Hash Tables (DHTs) - Overview
Definition: DHTs provide a decentralized method for storing and retrieving data across distributed nodes
using consistent hashing.
sql
-- Transfer $100 from Account A to Account B
BEGIN TRANSACTION;
UPDATE accounts SET balance = balance - 100 WHERE id = 'A';
UPDATE accounts SET balance = balance + 100 WHERE id = 'B';
COMMIT;
javascript
// User posts a status update
POST /api/posts
{
"user_id": "123",
"content": "Just visited the Eiffel Tower!",
"timestamp": "2024-01-15T10:30:00Z"
}

5. Key Characteristics:
Decentralized: No single point of failure
Scalable: Handles addition/removal of nodes efficiently
Fault Tolerant: Continues operation despite node failures
Self-Organizing: Automatically maintains routing information
Core Operations:
PUT(key, value): Store data
GET(key): Retrieve data
JOIN(): Add new node to network
LEAVE(): Remove node from network
Hash Function Properties:
Uniform distribution of keys
Deterministic output
Efficient computation
Avalanche effect for similar inputs
Slide 9: DHT Algorithms - Chord Protocol
Chord Algorithm: Nodes and keys are arranged in a logical ring using consistent hashing.
Key Concepts:
Ring Structure: Nodes organized in circular key space (0 to 2^m - 1)
Successor: Next node in clockwise direction
Finger Table: Routing table with O(log N) entries
Stabilization: Periodic updates to maintain correctness
Routing Process:
1. Node receives request for key K
2. If key is local, return data
3. Otherwise, forward to closest predecessor
4. Continue until reaching responsible node
Performance:
Lookup Time: O(log N) hops

6. Storage: O(log N) routing entries per node
Join/Leave: O(log²N) messages
Example Implementation:
Slide 10: DHT Case Study - BitTorrent
Background: BitTorrent uses DHT (Kademlia) for decentralized peer discovery and file sharing.
Architecture:
Tracker-less Operation: No central server required
Peer Discovery: Find peers sharing specific files
Metadata Storage: Store torrent information
Resilience: Survives node failures and network partitions
Kademlia Features:
XOR Metric: Distance calculation between node IDs
k-buckets: Routing table organization
Parallel Lookups: Multiple simultaneous queries
Iterative Routing: Gradual convergence to target
Implementation Details:
python
class ChordNode:
def __init__(self, id):
self.id = id
self.finger_table = [None] * M # M-bit identifier space
self.successor = None
self.predecessor = None

def lookup(self, key):
if self.id < key <= self.successor.id:
return self.successor
else:
return self.closest_preceding_node(key).lookup(key)
python

7. Performance Results:
Supports millions of simultaneous users
Average lookup time: 4-6 hops
Handles 90% node churn rate
Distributes 40% of global internet traffic
Slide 11: Distributed File Systems - Overview
Definition: Distributed file systems provide transparent access to files stored across multiple networked
computers.
Key Features:
Transparency: Files appear to be stored locally
Scalability: Handle petabytes of data
Fault Tolerance: Survive hardware failures
Consistency: Maintain file integrity
Performance: Optimize for read/write operations
Architecture Components:
Client Interface: API for file operations
Metadata Servers: Store file system structure
# Kademlia distance calculation
def distance(id1, id2):
return id1 ^ id2
# k-bucket organization
class KBucket:
def __init__(self, k=20):
self.contacts = []
self.k = k

def add_contact(self, contact):
if len(self.contacts) < self.k:
self.contacts.append(contact)
else:
# Replace least recently seen contact
self.contacts.pop(0)
self.contacts.append(contact)

8. Data Servers: Store actual file content
Replication Manager: Handle data redundancy
Load Balancer: Distribute client requests
Common Challenges:
Cache coherence across clients
Atomic file operations
Concurrent access control
Network partition handling
Security and access control
Slide 12: Google File System (GFS) - Architecture
Design Assumptions:
Component failures are the norm
Files are huge (multi-GB)
Workloads consist of large streaming reads and small random reads
Workloads have many large, sequential writes
High sustained bandwidth more important than low latency
Architecture:
Master Server: Single master manages metadata
Chunk Servers: Store 64MB chunks of files
Clients: Access files through GFS library
Master Responsibilities:
File and chunk namespace
Chunk location information
Access control information
Chunk migration decisions
Chunk Server Responsibilities:
Store chunks as Linux files
Handle read/write requests
Chunk replication
Integrity checking

9. Example Operations:
Slide 13: Hadoop Distributed File System (HDFS)
Architecture:
NameNode: Master server storing metadata
DataNodes: Worker nodes storing actual data
Secondary NameNode: Backup for NameNode
Client: Applications accessing HDFS
Key Features:
Write-Once, Read-Many: Optimized for batch processing
Large Block Size: 128MB default (vs 4KB in local filesystems)
Replication: 3x replication by default
Rack Awareness: Intelligent replica placement
Data Storage Process:
1. Client requests file creation from NameNode
2. NameNode checks permissions and creates file record
3. Client writes data to DataNode pipeline
4. DataNodes replicate data to other nodes
5. NameNode updates metadata after successful write
Real-World Usage:
Yahoo: 40,000+ nodes, 40PB storage
Facebook: 21,000+ nodes, 20PB storage
python
# GFS file read operation
def read_file(filename, offset, length):
# 1. Contact master for chunk locations
chunk_info = master.get_chunk_info(filename, offset)

# 2. Contact chunk server directly
data = chunk_server.read_chunk(chunk_info.chunk_id, offset, length)

return data

10. LinkedIn: 2,000+ nodes, 2PB storage
Performance Characteristics:
Throughput: 100+ MB/s per DataNode
Scalability: Supports 10,000+ nodes
Availability: 99.9% uptime typical
Fault Tolerance: Automatic failure recovery
Slide 14: Amazon S3 - Object Storage System
Architecture:
Buckets: Containers for objects
Objects: Individual files with metadata
Keys: Unique identifiers within buckets
Regions: Geographic distribution
Availability Zones: Fault isolation
Storage Classes:
Standard: Frequently accessed data
Infrequent Access: Monthly access patterns
Glacier: Long-term archival
Deep Archive: Rarely accessed data
Consistency Model:
Strong Consistency: All operations after December 2020
Read-after-Write: Immediate consistency for new objects
Eventual Consistency: Legacy behavior for overwrite operations
API Examples:
python

11. Performance and Scale:
Throughput: 3,500 PUT/COPY/POST/DELETE, 5,500 GET/HEAD per second per prefix
Durability: 99.999999999% (11 9's)
Availability: 99.99% designed availability
Scale: Trillions of objects, petabytes of data
Slide 15: NoSQL Database Categories
Document Databases:
Structure: JSON-like documents
Schema: Flexible, schema-less
Use Cases: Content management, catalogs, user profiles
Examples: MongoDB, CouchDB, Amazon DocumentDB
Key-Value Stores:
Structure: Simple key-value pairs
Schema: None (values are opaque)
Use Cases: Caching, session storage, shopping carts
Examples: Redis, Amazon DynamoDB, Apache Cassandra
Column-Family:
Structure: Rows with dynamic columns
import boto3
# Create S3 client
s3 = boto3.client('s3')
# Upload file
s3.upload_file('local_file.txt', 'my-bucket', 'remote_file.txt')
# Download file
s3.download_file('my-bucket', 'remote_file.txt', 'downloaded_file.txt')
# List objects
response = s3.list_objects_v2(Bucket='my-bucket')
for obj in response['Contents']:
print(obj['Key'])

12. Schema: Column families are pre-defined
Use Cases: Time series, IoT data, analytics
Examples: Apache Cassandra, HBase, Amazon SimpleDB
Graph Databases:
Structure: Nodes and edges
Schema: Property graphs
Use Cases: Social networks, recommendation engines, fraud detection
Examples: Neo4j, Amazon Neptune, ArangoDB
Slide 16: MongoDB - Document Database Case Study
Architecture:
Replica Sets: Primary-secondary replication
Sharding: Horizontal partitioning
Config Servers: Metadata storage
Mongos: Query routers
Document Structure:
Query Examples:
json
{
"_id": ObjectId("507f1f77bcf86cd799439011"),
"name": "John Doe",
"email": "[email protected]",
"age": 30,
"address": {
"street": "123 Main St",
"city": "New York",
"zipcode": "10001"
},
"hobbies": ["reading", "swimming", "cycling"],
"created_at": ISODate("2024-01-15T10:30:00Z")
}
javascript

13. Real-World Applications:
Forbes: Content management system
eBay: Product catalog and search
Adobe: Customer data platform
Bosch: IoT data collection
Slide 17: Apache Cassandra - Wide Column Store
Architecture:
Ring Topology: Peer-to-peer distributed system
Consistent Hashing: Data distribution
Replication: Configurable replication factor
Tunable Consistency: Flexible consistency levels
Data Model:
// Find documents
db.users.find({ "age": { $gte: 25 } })
// Update document
db.users.updateOne(
{ "_id": ObjectId("507f1f77bcf86cd799439011") },
{ $set: { "age": 31 } }
)
// Aggregation pipeline
db.users.aggregate([
{ $match: { "age": { $gte: 25 } } },
{ $group: { _id: "$city", count: { $sum: 1 } } },
{ $sort: { count: -1 } }
])
cql

14. Consistency Levels:
ONE: Single replica acknowledgment
QUORUM: Majority consensus
ALL: All replicas must acknowledge
LOCAL_QUORUM: Majority in local data center
Performance Characteristics:
Writes: 10,000+ writes per second per node
Reads: 5,000+ reads per second per node
Latency: Sub-millisecond for local operations
Scalability: Linear scale with node addition
Use Cases:
Netflix: Recommendation engine data
Uber: Trip and location data
Apple: iOS push notifications
Spotify: Music metadata and playlists
Slide 18: Redis - In-Memory Key-Value Store
Architecture:
-- Create keyspace
CREATE KEYSPACE ecommerce
WITH replication = {'class': 'SimpleStrategy', 'replication_factor': 3};
-- Create table
CREATE TABLE ecommerce.products (
category text,
product_id UUID,
name text,
price decimal,
description text,
created_at timestamp,
PRIMARY KEY (category, product_id)
);
-- Insert data
INSERT INTO ecommerce.products (category, product_id, name, price, description, created_at)
VALUES ('electronics', uuid(), 'iPhone 15', 999.99, 'Latest smartphone', toTimestamp(now()));

15. Single-Threaded: Event-driven architecture
Persistence: RDB snapshots and AOF logs
Replication: Master-slave replication
Clustering: Automatic sharding
Data Types:
Advanced Features:
Pub/Sub: Real-time messaging
Lua Scripting: Atomic operations
Transactions: MULTI/EXEC commands
Expiration: TTL for cache management
Geospatial: Location-based queries
Performance Metrics:
Throughput: 100,000+ operations per second
python
import redis
r = redis.Redis(host='localhost', port=6379, decode_responses=True)
# String operations
r.set('user:1000', 'John Doe')
r.get('user:1000')
# Hash operations
r.hset('user:1001', mapping={'name': 'Jane Smith', 'age': 25})
r.hgetall('user:1001')
# List operations
r.lpush('tasks', 'task1', 'task2', 'task3')
r.lrange('tasks', 0, -1)
# Set operations
r.sadd('tags', 'python', 'redis', 'database')
r.smembers('tags')
# Sorted set operations
r.zadd('leaderboard', {'player1': 1000, 'player2': 1500})
r.zrange('leaderboard', 0, -1, withscores=True)

16. Latency: Sub-millisecond response times
Memory: In-memory storage for speed
Persistence: Configurable durability options
Slide 19: Graph Database Case Study - Neo4j
Architecture:
Property Graph Model: Nodes and relationships with properties
ACID Transactions: Full transaction support
Clustering: High availability clustering
Indexing: Efficient property and label indexing
Graph Model Example - Social Network:
Real-World Applications:
LinkedIn: Professional network analysis
cypher
// Create nodes
CREATE (john:Person {name: 'John Doe', age: 30, city: 'New York'})
CREATE (jane:Person {name: 'Jane Smith', age: 25, city: 'Boston'})
CREATE (bob:Person {name: 'Bob Johnson', age: 35, city: 'Chicago'})
// Create relationships
CREATE (john)-[:FRIENDS_WITH {since: '2020-01-15'}]->(jane)
CREATE (john)-[:LIKES {rating: 5}]->(movie:Movie {title: 'The Matrix', year: 1999})
CREATE (jane)-[:WORKS_AT]->(company:Company {name: 'Tech Corp'})
// Query examples
// Find friends of John
MATCH (john:Person {name: 'John Doe'})-[:FRIENDS_WITH]->(friend)
RETURN friend.name, friend.age
// Find mutual friends
MATCH (john:Person {name: 'John Doe'})-[:FRIENDS_WITH]->(mutual)<-[:FRIENDS_WITH]-(jane:Person {name: 'Jane Smi
RETURN mutual.name
// Recommendation: Find friends of friends who like similar movies
MATCH (john:Person {name: 'John Doe'})-[:FRIENDS_WITH*2]->(fof)-[:LIKES]->(movie)
WHERE john-[:LIKES]->(movie)
RETURN fof.name, movie.title

17. Walmart: Supply chain optimization
NASA: Space mission data analysis
Airbnb: Fraud detection and prevention
Slide 20: NoSQL Database Comparison
Performance Comparison:
Database Type Read Latency Write Latency Throughput Scalability
MongoDB Document 1-10ms 1-10ms 10K-100K ops/sec Horizontal
Cassandra Wide Column 1-5ms 0.1-1ms 100K+ ops/sec Linear
Redis Key-Value 0.1-1ms 0.1-1ms 100K+ ops/sec Vertical
Neo4j Graph 1-10ms 1-10ms 10K-50K ops/sec Vertical
DynamoDB Key-Value 1-10ms 1-10ms 40K+ ops/sec Horizontal
Use Case Selection:
Scenario Recommended Database Reasoning
Content Management MongoDB Flexible schema, rich queries
Session Storage Redis In-memory speed, TTL support
Time Series Data Cassandra Write-heavy, time-based queries
Social Networks Neo4j Relationship-centric queries
Gaming Leaderboards Redis Sorted sets, real-time updates
IoT Data Collection Cassandra High write throughput, scalability
Slide 21: Case Study - Netflix Data Architecture
Background: Netflix processes 500+ billion events daily across 190+ countries, serving 230+ million
subscribers.
Data Architecture:
Kafka: Real-time event streaming
Cassandra: User profiles and viewing history
Elasticsearch: Search and recommendation indexing
Redis: Caching and session management
S3: Video content storage and analytics data
Data Flow:

18. 1. Event Generation: User interactions, video playback
2. Stream Processing: Real-time analytics with Kafka
3. Data Storage: Cassandra for operational data
4. Batch Processing: Spark jobs for ML model training
5. Serving Layer: Redis for real-time recommendations
Technology Stack:
Results:
Latency: Sub-second recommendation updates
Throughput: 500B+ events processed daily
Availability: 99.99% uptime globally
Scalability: Linear scaling with demand
Slide 22: Case Study - Uber's Schemaless Architecture
Background: Uber built Schemaless, a scalable datastore handling thousands of services across multiple
data centers.
python
# Example: User viewing event processing
class ViewingEvent:
def __init__(self, user_id, content_id, timestamp, duration):
self.user_id = user_id
self.content_id = content_id
self.timestamp = timestamp
self.duration = duration
# Kafka producer
producer = KafkaProducer(bootstrap_servers=['kafka1:9092'])
producer.send('viewing_events', viewing_event)
# Cassandra storage
session.execute("""
INSERT INTO user_viewing_history (user_id, content_id, timestamp, duration)
VALUES (?, ?, ?, ?)
""", (user_id, content_id, timestamp, duration))
# Redis caching
redis_client.hset(f'user:{user_id}:recommendations',
content_id, recommendation_score)

19. Architecture:
MySQL Shards: Horizontal partitioning
Consistent Hashing: Automatic shard assignment
Replication: Multi-master setup across regions
Buffering: Write-ahead logging for durability
Data Model:
Sharding Strategy:
Shard Key: UUID-based consistent hashing
Replication: 3x replication across availability zones
Failover: Automatic promotion of replicas
Load Balancing: Connection pooling and routing
Performance Metrics:
Queries: 100M+ queries per second
Latency: P99 < 5ms for reads
Availability: 99.99% uptime
json
{
"uuid": "550e8400-e29b-41d4-a716-446655440000",
"created_at": 1642248000,
"updated_at": 1642248000,
"body": {
"trip_id": "12345",
"driver_id": "67890",
"passenger_id": "54321",
"pickup_location": {
"lat": 37.7749,
"lng": -122.4194
},
"dropoff_location": {
"lat": 37.7849,
"lng": -122.4094
},
"status": "completed",
"fare": 15.50
}
}

20. Scalability: 1000+ database nodes
Lessons Learned:
Consistent hashing enables seamless scaling
Buffering is crucial for write performance
Monitoring and alerting are essential
Multi-region replication provides disaster recovery
Slide 23: Case Study - Facebook's Social Graph Storage
Background: Facebook stores social connections for 3+ billion users, handling complex graph queries at
massive scale.
TAO (The Associations and Objects) Architecture:
Objects: User profiles, posts, photos, comments
Associations: Friendships, likes, shares, comments
Graph Structure: Directed edges with properties
Caching: Multi-tier caching strategy
Data Model:
python

21. Storage Strategy:
MySQL: Persistent storage with sharding
Memcached: L1 cache for hot data
TAO: Graph-aware caching layer
Async Replication: Cross-region data consistency
Query Patterns:
# Object types
class User:
id: int
name: str
email: str
created_time: datetime
class Post:
id: int
author_id: int
content: str
created_time: datetime
# Association types
class Friendship:
id1: int # User 1
id2: int # User 2
time: datetime
type: str # 'friend', 'follow', etc.
class Like:
id1: int # User
id2: int # Post
time: datetime
sql

22. Performance Results:
Queries: 1B+ queries per second
Cache Hit Rate: 99.8% for read queries
Latency: < 1ms for cached reads
Consistency: Eventually consistent across regions
Slide 24: Performance Optimization Strategies
Data Partitioning:
Horizontal Partitioning: Split rows across nodes
Vertical Partitioning: Split columns across nodes
Functional Partitioning: Split by feature/service
Consistent Hashing: Automatic load balancing
Caching Strategies:
Cache-Aside: Application manages cache
Write-Through: Cache updated on writes
Write-Behind: Asynchronous cache updates
Cache Hierarchies: Multiple cache levels
Indexing Optimization:
-- Get user's friends
SELECT id2 FROM friendships WHERE id1 = ? AND type = 'friend'
-- Get post likes
SELECT id1 FROM likes WHERE id2 = ? ORDER BY time DESC LIMIT 100
-- Get news feed (complex aggregation)
SELECT p.* FROM posts p
JOIN friendships f ON p.author_id = f.id2
WHERE f.id1 = ? AND f.type = 'friend'
ORDER BY p.created_time DESC LIMIT 50
python

23. Query Optimization:
Query Planning: Analyze execution paths
Materialized Views: Pre-computed results
Denormalization: Trade storage for speed
Batch Processing: Group operations
Monitoring and Tuning:
Metrics: Latency, throughput, error rates
Profiling: Slow query identification
Capacity Planning: Resource utilization
A/B Testing: Performance comparisons
Slide 25: Security in Distributed Data Systems
Authentication and Authorization:
Role-Based Access Control (RBAC): User roles and permissions
Attribute-Based Access Control (ABAC): Fine-grained policies
OAuth/JWT: Token-based authentication
Multi-Factor Authentication: Enhanced security
Data Encryption:
Encryption at Rest: Storage-level encryption
Encryption in Transit: Network-level encryption
Key Management: Secure key distribution
Field-Level Encryption: Selective data protection
Security Examples:
# MongoDB indexing examples
db.users.createIndex({ "email": 1 }) # Single field
db.users.createIndex({ "age": 1, "city": 1 }) # Compound
db.users.createIndex({ "name": "text" }) # Text search
db.users.createIndex({ "location": "2dsphere" }) # Geospatial
# Cassandra indexing
CREATE INDEX ON products (price);
CREATE INDEX ON products (description);

24. Security Best Practices:
Regular security audits
Principle of least privilege
Network segmentation
Data anonymization
Compliance with regulations (GDPR, HIPAA)
Slide 26: Future Trends and Emerging Technologies
Multi-Model Databases:
ArangoDB: Document, graph, and key-value in one system
CosmosDB: Multiple API support (SQL, MongoDB, Cassandra)
FaunaDB: Serverless, globally distributed
Benefits: Reduced complexity, unified operations
Blockchain and Distributed Ledgers:
Immutable Storage: Tamper-proof data records
Consensus Mechanisms: Proof of Work, Proof of Stake
Smart Contracts: Programmable data operations
Use Cases: Supply chain, digital identity, finance
Edge Computing and Data:
python
# MongoDB authentication
from pymongo import MongoClient
client = MongoClient('mongodb://username:password@localhost:27017/')
# Cassandra SSL/TLS
from cassandra.cluster import Cluster
from cassandra.auth import PlainTextAuthProvider
auth_provider = PlainTextAuthProvider(username='user', password='pass')
cluster = Cluster(['127.0.0.1'], auth_provider=auth_provider, ssl_context=ssl_context)
# Redis AUTH
import redis
r = redis.Redis(host='localhost', port=6379, password='secret')

25. Edge Databases: Data processing at network edge
Fog Computing: Hierarchical data processing
5G Integration: Ultra-low latency applications
IoT Data Management: Massive sensor data handling
AI and Machine Learning Integration:
Automated Tuning: Self-optimizing systems
Predictive Scaling: Proactive resource allocation
Anomaly Detection: Security and performance monitoring
Query Optimization: AI-driven query planning
Serverless Databases:
Auto-scaling: Dynamic resource allocation
Pay-per-use: Cost-effective pricing
Managed Services: Reduced operational overhead
Examples: AWS DynamoDB, Azure Cosmos DB
Slide 27: Choosing the Right Storage Solution
Decision Framework:
1. Data Model: Structured vs. unstructured
2. Consistency Requirements: Strong vs. eventual
3. Scale Requirements: Current and projected
4. Performance Needs: Latency vs. throughput
5. Budget Constraints: Operational and licensing costs
Selection Matrix:
| Use Case | Data Model | Consistency | Scale |