Additional Articles


External Article

How We Ate Our ​Own Dog Food​ To Level-Up Internal Testing with Redgate Clone

Most applications have large and complex databases at the back end, making it hard for developers to adequately test their work before it goes out. Having a fast, repeatable process to deliver data on demand is an essential part of an effective software development lifecycle, ultimately leading to improved customer satisfaction. In this article, we’ll explore the journey our own engineering team went on to leverage our own tool, Redgate Clone, to spin up short-lived database instances in containers for automated testing.

2023-10-25

Technical Article

Top 10 Methods to Improve ETL Performance Using SSIS

Extraction Transformation Load (ETL) is the backbone for any data warehouse. In the data warehouse world data is managed by the ETL process, which consists of three processes, Extraction-Pull/Acquire data from sources, Transformation-change data in the required format and Load-push data to the destination generally into a data warehouse or a data mart.

2023-10-25

Blogs

AI: Blog a Day – Day 4: Transformers – Encoder, Decoder, and Attention

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Continuing from Day 3 where we covered LLM models open/closed and their parameters, Today...

Flyway Tips: Multiple Projects

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One of the nice things about Flyway Desktop is that it helps you manage...

What DevOps Look Like in Microsoft Fabric

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Microsoft Fabric (not to be confused with the more general term “fabric” in DevOps)...

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Forums

Can an Azure App Service Managed Identity be used for SQL Login?

By jasona.work

I'm fairly certain I know the answer to this from digging into it yesterday,...

Azure Synapse database refresh

By Sreevathsa Mandli

Hi Team, I am trying to refresh the Azure Synapse Dedicated pool from production...

how to write this query?

By water490

hi everyone I am not sure how to write the query that will produce...

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Question of the Day

Fun with JSON I

I have some data in a table:

CREATE TABLE #test_data
(
    id INT PRIMARY KEY,
    name VARCHAR(100),
    birth_date DATE
);

-- Step 2: Insert rows  
INSERT INTO #test_data
VALUES
(1, 'Olivia', '2025-01-05'),
(2, 'Emma', '2025-03-02'),
(3, 'Liam', '2025-11-15'),
(4, 'Noah', '2025-12-22');
If I run this query, how many rows are returned?
SELECT *
FROM OPENJSON(
     (
         SELECT t.* FROM #test_data AS t FOR JSON PATH
     )
             ) t;

See possible answers