Big Data Definition and 5 V's
Learn the fundamental characteristics that define big data through the 5 V's framework.
Volume
Velocity
Variety
Veracity
Value
Big data is characterized by the 5 V's: Volume (scale of data), Velocity (speed of data generation), Variety (different data types), Veracity (data quality), and Value (business insights).
Traditional vs Big Data Challenges
Understand why traditional data processing approaches fail with big data and what new challenges emerge.
Traditional: Single machine processing, relational databases, batch processing
Big Data: Distributed processing, NoSQL databases, real-time processing
# Traditional approach limitations
traditional_limits = {
"storage": "Single machine disk capacity",
"processing": "CPU and memory constraints",
"scalability": "Vertical scaling only",
"availability": "Single point of failure"
}
# Big data solutions
bigdata_solutions = {
"storage": "Distributed file systems (HDFS)",
"processing": "Parallel processing (MapReduce)",
"scalability": "Horizontal scaling",
"availability": "Fault tolerance and replication"
}
Distributed Computing Concepts
Learn the fundamental principles of distributed systems that enable big data processing.
Distributed computing involves coordinating multiple machines to work together as a single system, sharing data and computational load across the network.
# Distributed computing principles
import hashlib
def hash_partition(key, num_partitions):
"""Distribute data across partitions"""
hash_value = hashlib.md5(key.encode()).hexdigest()
return int(hash_value, 16) % num_partitions
# Example: Distribute user data across 4 nodes
users = ["alice", "bob", "charlie", "diana"]
for user in users:
partition = hash_partition(user, 4)
print(f"User {user} → Node {partition}")
Scalability and Performance
Understand different scaling approaches and performance considerations in big data systems.
Horizontal Scaling
Vertical Scaling
Linear Scalability
# Scaling comparison
def calculate_processing_time(data_size, num_nodes=1):
"""Simplified scaling model"""
base_time = data_size / 1000 # Base processing time
# Linear scaling assumption
return base_time / num_nodes
data_sizes = [1000, 10000, 100000] # GB
node_counts = [1, 4, 16]
for size in data_sizes:
for nodes in node_counts:
time = calculate_processing_time(size, nodes)
print(f"{size}GB on {nodes} nodes: {time:.1f} hours")
CAP Theorem
Learn the CAP theorem and its implications for distributed big data systems design.
CAP Theorem: A distributed system can guarantee at most two of three properties:
Consistency, Availability, and Partition tolerance
# CAP theorem trade-offs
cap_tradeoffs = {
"CA_systems": {
"examples": ["Traditional RDBMS", "ACID databases"],
"sacrifice": "Partition tolerance"
},
"CP_systems": {
"examples": ["MongoDB", "HBase", "Redis"],
"sacrifice": "Availability"
},
"AP_systems": {
"examples": ["Cassandra", "DynamoDB", "CouchDB"],
"sacrifice": "Consistency"
}
}
Data Lakes vs Data Warehouses
Compare and contrast data lakes and data warehouses for different big data use cases.
Data Warehouse: Structured, schema-on-write, optimized for analytics
Data Lake: Raw data, schema-on-read, flexible storage for all data types
# Data storage comparison
storage_comparison = {
"data_warehouse": {
"structure": "Highly structured (tables)",
"schema": "Schema-on-write",
"processing": "ETL before storage",
"use_case": "Business intelligence, reporting"
},
"data_lake": {
"structure": "Raw, unstructured/semi-structured",
"schema": "Schema-on-read",
"processing": "ELT after storage",
"use_case": "Data science, machine learning"
}
}