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.

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

Since then, SQL has come to dominate the landscape. (“Never bet against SQL,” I like to say.) Most users have moved to cloud SQL services (BigQuery, Snowflake) because convenience is more important than the flexibility and raw power of Apache Hadoop or Spark. SQL now handles workloads—streaming, data transformation, documents, geospatial, machine learning—previously handled by non-SQL systems. But there are two areas where SQL has not yet prevailed—Business Intelligence and data-intensive computing—and I want to investigate whether the solution is SQL or something else. Julian Hyde on Into the Wilderness

What is ANSI SQL?

• Nice new features: UNION ALL BY NAME

SQL is declarative.

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.

From parsing to compiling and executing. Possible with SDF and SQLGlot:

source

• Recommended Reading: Efficient Data Processing in SQL by Joseph Machado
• Comprehensive Guide: Ultimate SQL Guide from Count-co
• Learning SQL as a detective: SQL Noir

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Created 2022-08-08