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SQL
SQL, a domain-specific language, is pivotal in programming, especially for managing data in relational database management systems or stream processing in relational data stream systems.
It’s closely tied to Jinja Template within dbt. Beyond Business Intelligence tools, a growing number of data engineering tools are now exclusively integrating with SQL. This is further explored in Building an Analytics API with GraphQL - The Next Level of Data Engineering.
For additional insights, refer to Components of an Analytics API.
# History
SQL was invented in the 1970s based on the Relational Data Model. It was initially known as the structured English query language (SEQUEL). The term was later shortened to SQL.
Oracle, formerly known as Relational Software, became the first vendor to offer a commercial SQL relational database management system.
“History of SQL”
SQL -> Data Mart -> Materialized View -> BI Report -> Traditional OLAP -> BI Dashboard -> Modern OLAP -> dbt tables -> One Big/Wide/Super Table -> Semantic Layer -> Natural Language Queries
# ANSI SQL
What is ANSI SQL?
# SQL Syntax
- Nice new features: UNION ALL BY NAME
# Declarative
# Extending SQL for analytics
SQL is the origin of data, and it has been extended over the years. We have geospatial capabilities, window functions, and other features throughout the year. Why not add analytics semantics, too?
See Extending SQL for analytics.
# Different parts of an SQL-Statement
From parsing to compiling and executing. Possible with SDF and SQLGlot:
source
# Resources
- Recommended Reading: Efficient Data Processing in SQL by Joseph Machado
- Comprehensive Guide: Ultimate SQL Guide from Count-co
Origin:
References:
Created 2022-08-08