Transaction VS Operation DBs

Transactional Databases (OLTP - Online Transaction Processing):

1. Primarily handle day-to-day transactions and real-time operations

2. Optimized for fast, small-scale read/write operations

3. Focus on maintaining data consistency and ACID properties (Atomicity, Consistency, Isolation, Durability)

4. Typically use normalized data structures to minimize redundancy

5. Example use cases: banking transactions, order processing, inventory management

6. Usually handle current, in-the-moment data

7. Designed for many concurrent users making small changes

Operational Databases (OLAP - Online Analytical Processing):

1. Designed for business analysis and decision support

2. Optimized for complex queries and large-scale data aggregation

3. Focus on data analysis and reporting rather than transaction processing

4. Often use denormalized data structures for faster querying

5. Example use cases: business intelligence, trend analysis, forecasting

6. Usually contain historical data spanning longer time periods

7. Designed for fewer users running complex queries on large datasets

Common Architectural patterns

For Transactional Databases:

1. Primary-Replica Pattern

   • Primary node handles writes

   • Multiple read replicas for scaling reads

   • Synchronous or asynchronous replication

2. Sharding Pattern

   • Data distributed across multiple nodes

   • Horizontal partitioning based on key ranges or hash values

   • Each shard handles subset of total data

For Operational Databases:

1. Star Schema

   • Fact tables in the center

   • Dimension tables around the edges

   • Optimized for analytical queries

2. Data Lake Architecture

   • Raw data storage

   • Schema-on-read approach

   • Multiple processing engines

3. Lambda Architecture

   • Batch processing layer

   • Speed layer for real-time processing

   • Serving layer for query results

Star Schema Pattern

Here are some official resources about Star Schema:

IBM Documentation: https://www.ibm.com/docs/en/ida/9.1.2?topic=schemas-star-schema

Microsoft Documentation: https://learn.microsoft.com/en-us/power-bi/guidance/star-schema

Let me create a visual representation of a typical star schema:

Key characteristics of Star Schema:

1. Central Fact Table:

   • Contains business metrics (measurements)

   • Foreign keys to all dimension tables

   • Typically numeric and additive

2. Dimension Tables:

   • Contain descriptive attributes

   • Connected to fact table via primary keys

   • Denormalized for query performance

3. Advantages:

   • Simple to understand and navigate

   • Optimized for OLAP queries

   • Efficient for aggregations and joins

4. Common Use Cases:

   • Sales analysis

   • Financial reporting

   • Inventory management

   • Customer behavior analysis

Would you like me to explain any specific aspect of star schema design or show how it compares to other schema types like snowflake schema?

Resources

Transactional (OLTP) Database Resources:

1. PostgreSQL Official Documentation on High Availability: https://www.postgresql.org/docs/current/high-availability.html

2. MySQL Documentation on Replication: https://dev.mysql.com/doc/refman/8.0/en/replication.html

3. Microsoft SQL Server Always On Architecture: https://learn.microsoft.com/en-us/sql/database-engine/availability-groups/windows/always-on-availability-groups-sql-server

Operational (OLAP) Database Resources:

1. Apache Hadoop Documentation: https://hadoop.apache.org/docs/stable/

2. Snowflake Architecture Guide: https://docs.snowflake.com/en/user-guide/intro-key-concepts

3. Amazon Redshift Architecture: https://docs.aws.amazon.com/redshift/latest/dg/c_high_level_system_architecture.html