Database selection is not about SQL syntax - it is about analyzing requirements, comparing options, testing with realistic data, and making informed decision. Key steps: (1) Define requirements (workload, scale, budget, team skills), (2) Compare databases (features, performance, cost), (3) Build proof of concept (test with realistic data), (4) Use decision matrix (score each option), (5) Document decision (reasoning, trade-offs). No single right answer - depends on specific requirements.

Analyze requirements first: Workload type (OLTP vs OLAP), scale (users, data size), consistency needs (ACID vs eventual)

Compare databases systematically: Features, performance, cost, ecosystem, team skills, support

Build proof of concept: Test with realistic data (not toy data), measure performance of critical queries, simulate realistic load

Use decision matrix: Score each database (1-10) on key criteria, weight criteria by importance, calculate total score

Document decision: Write down reasoning, trade-offs considered, why chosen database is best fit for requirements

Plan for scale: Consider how database will handle 10x, 100x growth, plan sharding/replication strategy if needed

Consider total cost: Licensing + hosting + bandwidth + support + developer time (learning curve)

Best for: Simple web apps, read-heavy workloads, teams preferring simplicity. Proven at scale: Facebook, YouTube, GitHub, Shopify. Cost: Free (open-source). Learning curve: Easy.

Best for: Complex queries, JSON data, analytics, high concurrency. Proven at scale: Instagram (2B users), Uber (50K writes/sec), Stripe. Cost: Free (open-source). Learning curve: Medium.

Best for: Windows/.NET environments, enterprise apps, need for great tooling. Proven at scale: Stack Overflow, Microsoft. Cost: $14K-$47K/core. Learning curve: Medium.

Best for: Mission-critical systems (banks, airlines), need for 24/7 support, advanced features. Proven at scale: Banks, airlines, hospitals. Cost: $47K/core/year. Learning curve: Hard.

Core references in this topic include Row, WHERE, =. Learn what each one does, when to use it, and the execution or engine rules that matter.

Row

Represents one record inside a table, such as one customer, one order, or one product.

customer_id = 101, name = Alice Johnson

Rows are the individual business facts you query, update, and join.

WHERE

Filters rows before projection and sorting. It decides which rows continue through the query pipeline.

SELECT ... FROM table WHERE condition;

Most performance issues start with a weak WHERE clause or a missing supporting index.

=

Returns rows where the left and right values are exactly equal.

column = value

Use with exact matches. Do not use = NULL.

<, >, <=, >=

Range comparison operators for less-than, greater-than, and inclusive boundary checks.

salary >= 80000

AND

Requires every condition in the boolean expression to evaluate to TRUE.

condition_a AND condition_b

AND has higher precedence than OR.

BETWEEN

Checks whether a value falls inside an inclusive lower/upper range.

order_total BETWEEN 100 AND 500

LIKE

Pattern-matching operator for wildcard string searches.

name LIKE 'Joh%'

EXISTS

Tests whether a correlated or non-correlated subquery returns at least one row.