Many relational databases now support JSON columns. JSON columns within a relational database provide a bridge between the structured world of SQL and the flexibility of NoSQL. They allow you to leverage the strengths of both approaches within a single system. The JSON functions in many popular databases also offer a way to convert JSON into a table record set. This opens up all kinds of possibilities. Using JSON in a CTE is a much more efficient way to JOIN on an external set of data. This article explains how to convert a JSON dataset into a table in many of the most common databases.

MySQL and SAP HANA happen to create JSON records sets the same way.

SELECT * 
FROM JSON_TABLE(
    '[{"firstName":"Paulo","lastName":"Coelho"},{"firstName":"Markolo","lastName":"Hondachi"}]', 
    '$[*]'
    COLUMNS ( 
        first_name nvarchar(200) PATH '$.firstName',
        last_name nvarchar(200) PATH '$.lastName' 
        ) 
    ) jt

Oracle is very close to MySQL and SAP HANA but notice the varchar definition difference. Oracle requires varchar2 instead of nvarchar.

SELECT * 
FROM JSON_TABLE(
    '[{"firstName":"Paulo","lastName":"Coelho"},{"firstName":"Markolo","lastName":"Hondachi"}]', 
    '$[*]'
    COLUMNS ( 
        first_name varchar2(200) PATH '$.firstName',
        last_name varchar2(200) PATH '$.lastName' 
        ) 
    ) jt

PostgreSQL has a specific function called JSON_TO_RECORDSET to handle this

SELECT * 
FROM JSON_TO_RECORDSET(
    '[{"firstname":"Paulo","lastname":"Coelho"},{"firstname":"Markolo","lastname":"Hondachi"}]'
    ) AS jt(
        firstname varchar(200),
        lastname varchar(200)
    )

Microsoft SQL Server also has their special function OPENJSON

SELECT * 
FROM OPENJSON(
    '[{"firstname":"Paulo","lastname":"Coelho"},{"firstname":"Markolo","lastname":"Hondachi"}]'
    )
    WITH (
        firstname nvarchar(200) '$.firstname',
        lastname nvarchar(200) '$.lastname'
    )

SQLite uses JSON_EXTRACT and JSON_EACH

SELECT
    JSON_EXTRACT(value,'$.firstName') as firstname,
    JSON_EXTRACT(value,'$.lastName') as lastname
FROM JSON_EACH(
    '[{"firstName":"Paulo","lastName":"Coelho"},{"firstName":"Markolo","lastName":"Hondachi"}]'
    )

Last but not least is Snowflake

WITH BASE AS (
    SELECT PARSE_JSON(
        '[{"firstName":"Paulo","lastName":"Coelho"},{"firstName":"Markolo","lastName":"Hondachi"}]'
    ) data 
)
SELECT 
    value:"firstName"::varchar(50) AS firstName, 
    value:"lastName"::varchar(50) AS lastName
FROM BASE, LATERAL FLATTEN(input=>data)

Extracting data from JSON record sets is definitely unique per database system but still very very powerful. Try using them with prepared statements and passing in the JSON dataset as a variable. Try using them as a CTE (Common Table Expression) to get rid of large IN statements in your queries. What other ways can you think of to use JSON record sets?