Easy interview questions in Databases & Architecture, page 2
What is the difference between ETL and ELT?
ETL transforms before loading, on a separate engine; ELT loads raw then transforms inside a scalable warehouse. Choose ELT with cloud warehouses and large raw or schema-on-read data.
What is database replication and why use it?
Replication keeps copies of data on multiple servers; primary benefits are high availability through failover and improved read scalability by spreading reads.
What is sharding and why shard over vertical scaling?
Sharding splits one dataset across servers by a shard key so each holds a subset; you shard because vertical scaling hits hardware ceilings, gets costly, and remains a single point of failure.
Full, differential, and incremental backups
Full copies everything; differential copies all changes since the last full; incremental copies changes since the last backup of any type.
Least privilege for database service accounts
Grant each account only the minimum rights its job needs; for an app service account, scope grants to specific tables and verbs, never use the superuser.
Purpose of database drivers (JDBC/ODBC)
A driver translates a standard API into each database's wire protocol, so app code stays portable across vendors.
Connection pools and the problem they solve
A pool reuses pre-opened connections so requests skip the expensive connect handshake; without one, every request pays setup latency and may overwhelm the database.
Managed RDS vs self-managed DB on EC2
Managed RDS offloads patching, backups, failover, and replication, freeing the team to build product; self-managed EC2 means you own all that toil.
Read replica vs Multi-AZ in RDS
Multi-AZ is synchronous standby for failover, read replicas are async copies for read throughput, and the two solve different problems.
Replica lag and read-your-writes consistency
Stale reads come from async replica lag, the guarantee a user expects is read-your-writes, and you route that user's reads to the primary after a write.
Data warehouse vs data lake
Warehouses store structured, schema-on-write data for BI; lakes store raw multi-format data with schema-on-read for exploration and ML.
Schema-on-read in data lakes
Structure is applied at query time not ingest, enabling flexible raw storage and ML, but costing query-time validation and risking data swamps.
Inverted index in search engines
An inverted index maps each term to the list of documents containing it, making keyword lookup O(1)-ish instead of scanning every document.
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