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Implementing a Data Quality Monitoring Framework

For any Business Intelligence solution to be useful for analytical and decision-making purposes, it is imperative that the underlying data quality is good so, that reports generated are reliable to make business decisions. This article from Imran Quadri Syed walks the through steps involved to implement a successful Data Quality Monitoring framework that would validate incoming data against predefined data quality rules and capture data quality metrics.

2020-05-26

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Technical Article

[Video] Free DBA Training Month: Configuring TempDB, Lock Pages in Memory, and the DAC

While you’re stuck inside wondering if you’ll ever be able to attend a user group meeting again, I’m giving you free access to training materials that your boss probably wouldn’t have been nice enough to buy you anyway. So far this week, we’ve covered DBCC CHECKDB, how to set up Ola Hallengren’s maintenance scripts, and restoring for oops deletes. Today, let’s talk about two common configuration issues. Don’t dilly-dally on these though: these blog posts will be online during May only, and they’ll disappear on June 1.

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2020-05-22

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Creating a JSON Document IV

By Steve Jones - SSC Editor

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Question of the Day

Creating a JSON Document IV

I have this data in a table called dbo.NFLTeams

TeamID  TeamName       City             YearEstablished
------  --------       ----             ---------------
1       Cowboys        Dallas           1960
2       Eagles         Philadelphia     1933
3       Packers        Green Bay        1919
4       Chiefs         Kansas City      1960
5       49ers          San Francisco    1946
6       Broncos        Denver           1960
7       Seahawks       Seattle          1976
8       Patriots       New England      1960
If I run this code, how many rows are returned?
SELECT 
  YearEstablished,
  json_objectagg(city : TeamName)
FROM dbo.NFLTeams
GROUP BY  YearEstablished;

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