Learn PARTIQL with Real Code Examples

PartiQL extends SQL to work natively with nested and hierarchical data.

It enables uniform queries across relational, document, key-value, and graph-like data models.

PartiQL queries run without needing to flatten or restructure source data.

SELECT, INSERT, UPDATE, DELETE support

Path navigation for JSON

Lambda-style iterators (FROM x IN y)

Unnesting & flattening with ease

Schema-on-read flexibility

Record & array manipulation

SQL extensions for nested data

Document navigation with dot/path

Item collections (arrays, maps)

Iterators for unnesting

Filters and condition expressions

Schema-optional data querying

PartiQL query scripts

DynamoDB table definitions

Backend adapters

AWS IAM access configurations

Local/production data models

Define data model (JSON or DynamoDB tables)

Insert data with PartiQL INSERT

Run SELECT queries over nested attributes

Transform data with UPDATE/DELETE

Optimize access patterns with keys/indexes

Beginner: simple SELECT queries

Intermediate: nested JSON filtering

Advanced: array iteration + UNNEST

Expert: federated + multi-model queries

More flexible than SQL for nested data

Easier than MongoDB query language for SQL users

More universal than DynamoDB native API

Simpler than Athena SQL for semi-structured data

2019 – PartiQL initial release

2020 – DynamoDB full PartiQL support

2021 – PartiQL open-source spec

2023–2025 – AWS-wide PartiQL expansion

Document path: dot navigation

Unnesting: flattening arrays

Schema-optional: flexible structure

Item: NoSQL record

Projection: selecting attributes