Dipping into the Cookie Jar
This post is a response to this month's T-SQL Tuesday #112 prompt by Shane O'Neill. T-SQL Tuesday is a way for the SQL Server community to share ideas about different database and...
2019-03-12
6 reads
This post is a response to this month's T-SQL Tuesday #112 prompt by Shane O'Neill. T-SQL Tuesday is a way for the SQL Server community to share ideas about different database and...
2019-03-12
6 reads
Watch this week’s episode on YouTubeAT TIME ZONE is great because it makes it easy to perform daylight saving time and...
2019-03-19 (first published: 2019-03-05)
9,216 reads
Watch this week's video on YouTube
AT TIME ZONE is great because it makes it easy to perform daylight saving time and time zone conversions in our queries.
However, when using AT...
2019-03-05
7 reads
Watch this week's video on YouTube
AT TIME ZONE is great because it makes it easy to perform daylight saving time and time zone conversions in our queries.
However, when using AT...
2019-03-05
27 reads
Watch this week’s video on YouTubeComputed column indexes make querying JSON data fast and efficient, especially when the schema of...
2019-02-26
926 reads
Watch this week's video on YouTube
Computed column indexes make querying JSON data fast and efficient, especially when the schema of the JSON data is the same throughout a table.
It's...
2019-02-26
9 reads
Watch this week's video on YouTube
Computed column indexes make querying JSON data fast and efficient, especially when the schema of the JSON data is the same throughout a table.
It's...
2019-02-26
18 reads
Watch this week’s episode on YouTube.
One thing I see fairly often (and am occasionally guilty of myself) is using COUNT(DISTINCT)...
2019-03-05 (first published: 2019-02-19)
3,548 reads
Watch this week's video on YouTube
One thing I see fairly often (and am occasionally guilty of myself) is using COUNT(DISTINCT) and DISTINCT interchangeably to get an idea of the...
2019-02-19
17 reads
Watch this week's video on YouTube
One thing I see fairly often (and am occasionally guilty of myself) is using COUNT(DISTINCT) and DISTINCT interchangeably to get an idea of the...
2019-02-19
11 reads
If you've ever loaded a 2 GB CSV into pandas just to run a...
By James Serra
What problem is Fabric Ontology trying to solve? For years, most data conversations have...
By Steve Jones
Recently I ran across some code that used a lot of QUOTENAME() calls. A...
Comments posted to this topic are about the item The New Software Team
Comments posted to this topic are about the item Database Mail in SQL Server...
Comments posted to this topic are about the item The string_agg function
We create the following table and then insert some records in it:
create table t1 ( id int primary key, category char(1) not null, product varchar(50) ); insert into t1 values (1, 'A', 'Product 1'), (2, 'A', 'Product 2'), (3, 'A', 'Product 3'), (4, 'B', 'Product 4'), (5, 'B', 'Product 5');What happens if we execute the following query in both Sql Server and PostgreSQL?
select id,
category,
string_agg(product, ';')
over (partition by category order by id
rows between unbounded preceding and unbounded following) as stragg
from t1; See possible answers