BASE Properties; CAP Theorem; OLTP vs OLAP

BASE Properties

What is BASE?

BASE is the consistency model used by NoSQL databases (opposite of ACID):

Letter	Property	Definition
BA	Basically Available	System guarantees availability (not every request will get the latest data, but a response)
S	Soft State	The state of the system may change over time (even without new inputs — due to eventual consistency)
E	Eventually Consistent	System will become consistent over time (not immediately; all replicas eventually agree)

Example of Eventual Consistency:

User posts a photo on Instagram (write to primary replica)
Immediately visible on their phone
After 1-2 seconds: visible globally (replicas catch up)
Eventually consistent — not immediately consistent

CAP Theorem

What is CAP Theorem?

CAP Theorem (Brewer's Theorem, 2000): A distributed system can guarantee at most 2 out of 3 properties simultaneously:

Property	Description
C — Consistency	Every read receives the most recent write or an error
A — Availability	Every request receives a response (not necessarily latest)
P — Partition Tolerance	System continues operating despite network partitions

         C (Consistency)
             /\
            /  \
           /    \
          /      \
CA ------+--------+ CP
        /          \
       /            \
      /      AP      \
     +________________+
A (Availability)   P (Partition Tolerance)

Network partitions ALWAYS happen in distributed systems → you must choose between C and A.

Database Classification by CAP

Type	Prioritizes	Examples
CP	Consistency + Partition Tolerance (sacrifice Availability)	HBase, MongoDB, Redis
AP	Availability + Partition Tolerance (sacrifice Consistency)	Cassandra, DynamoDB, CouchDB
CA	Consistency + Availability (only on single node — no partition)	Traditional RDBMS

OLTP vs OLAP

OLTP — Online Transaction Processing

Feature	OLTP
Purpose	Day-to-day operations; transactions
Data	Current, operational data
Queries	Simple CRUD; INSERT, UPDATE, DELETE
Response time	Milliseconds
Users	Thousands of concurrent users (clerks, customers)
Data size	GB
Normalized	Highly normalized (3NF)

Examples: Banking transactions, e-commerce orders, inventory management

OLAP — Online Analytical Processing

Feature	OLAP
Purpose	Analysis, reporting, decision support
Data	Historical, aggregated data
Queries	Complex aggregations, multi-dimensional analysis
Response time	Seconds to minutes
Users	Few analysts and managers
Data size	TB to PB
Denormalized	Star schema, snowflake schema

Examples: Sales trend analysis, customer behavior analysis, financial reporting

OLTP vs OLAP Comparison

Feature	OLTP	OLAP
What	Record transactions	Analyze data
Query type	Simple (INSERT, simple SELECT)	Complex (GROUP BY, JOIN across years)
Users	Many concurrent (1000s)	Few (10s)
Data	Current (days)	Historical (years)
Schema	Normalized	Denormalized (star/snowflake)
Optimization	Fast transactions	Fast reads at scale
Database	MySQL, PostgreSQL	Snowflake, BigQuery, Redshift