SQL Server Mastery

Backup & Recovery Models: Full vs Simple vs Bulk-Logged

1 Views Updated 5/4/2026

Backup & Recovery Mastery

A database without a backup strategy is just a liability. But a backup strategy is more than just clicking 'Export'. You must choose the right Recovery Model based on how much data your business is willing to lose in a disaster.

1. SIMPLE Recovery

SQL Server automatically clears the transaction log as soon as the data is written to disk. You can only recover to the moment of your **last full backup**. If you backup at midnight and crash at 4 PM, you lose 16 hours of work. Best for Dev/Test or data warehouses.

2. FULL Recovery (Enterprise Standard)

Every transaction is kept in the log until you perform a **Log Backup**. This allows for **Point-in-Time Recovery**. You can restore your database to the exact second (e.g., 2:14:05 PM) before a someone accidentally deleted a table.

3. Transaction Log Tail

In a true disaster, even if your data file (.mdf) is deleted, you can often still back up the 'Tail' of the log (.ldf). This allows you to recover 100% of your data with **Zero Data Loss**, even if your hardware physically died.

4. Interview Mastery

Q: "What is the difference between RTO and RPO?"

Architect Answer: "**RPO (Recovery Point Objective)** is about *Data Loss*: 'How many minutes of work can we lose?' (e.g., 15 minutes of log backups). **RTO (Recovery Time Objective)** is about *Downtime*: 'How long does it take for us to be back online?' (e.g., '2 hours to pull the backup from off-site storage'). A senior architect must negotiate these metrics with the business *before* designing the backup strategy."

SQL Server Mastery
1. SQL Server Architecture & Basics
SQL Server Internals: How the Storage Engine works Relational Database Design & Normalization (1NF to 3NF) Data Types Mastery: Choosing the right type for performance
2. Advanced T-SQL Querying
Joins Deep Dive: Inner, Outer, Cross, and Self Joins Subqueries vs CTEs: Writing readable, high-performance code Window Functions: ROW_NUMBER, RANK, and LEAD/LAG Aggregations & Grouping Sets: Building complex reports Set Operators: UNION vs UNION ALL, INTERSECT, and EXCEPT
3. Indexing & Performance Tuning
Clustered vs Non-Clustered Indexes: The physical storage reality Covering Indexes & Included Columns: Reducing I/O costs Index Fragmentation: Why it happens and how to fix it Execution Plans: Reading the Query Optimizer's mind Statistics: Why 'Out of Date' stats kill performance SARGability: Writing queries that actually use indexes
4. Database Programmability
Stored Procedures: Security, Performance, and Best Practices User Defined Functions (UDF): Scalar vs Table-Valued Triggers: Auditing changes and the dangers of hidden logic Views & Indexed Views: Abstraction with performance Error Handling: TRY/CATCH and XACT_STATE()
5. Transactions & Concurrency
Transaction Isolation Levels: Read Uncommitted to Snapshot Locking & Blocking: Analyzing Deadlocks like a Pro Optimistic vs Pessimistic Concurrency
6. Administration & Security
SQL Server Security: Logins, Users, and Roles SQL Injection Prevention: Beyond simple parameterization Backup & Recovery Models: Full vs Simple vs Bulk-Logged Automating Maintenance: SQL Agent Jobs & Rebuilding Indexes
7. Modern SQL & Cloud
SQL Server & JSON: Storing and Querying semi-structured data Temporal Tables: Keeping track of data history automatically Introduction to Azure SQL: Database as a Service (PaaS) SQL Server Developer Interview: Junior to Senior Architect Level