There are several things that you can do to improve performance by throwing more hardware at the problem, but usually the place you get the most benefit from is when you tune your queries. One common problem that exists is the lack of indexes or incorrect indexes and therefore SQL Server has to process more data to find the records that meet the queries criteria. These issues are known as Index Scans and Table Scans.
In this next article, we are going to explore how to install, configure and use the command line to manage a couple different Fabric Lakehouse’s.
Purging data from a table is a common database maintenance task to prevent it from growing too large or to stay in compliance with data retention. When dealing with small amounts of data, this can be accomplished by a simple delete with no issues; however, with larger tables, this task can be problematic. Deleting records requires a lock that can block other processes from writing or even reading the data (depending on your isolation level). In this article I will share a technique I have used to work with some very large tables.
Steve has advice for students looking to become data professionals.
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Who among us hasn't deleted a production data file? Steve hopes most of us have not.
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What advice would you give to someone new to Power BI? What are common Power BI mistakes to avoid? In this article, we will discuss several Power BI mistakes to avoid to help you navigate this powerful tool effectively.
AI is changing work, sometimes for the better, sometimes not. Steve continues to examine and evaluate how GenAI systems might be used in today's world and asks if there is work that might help you.
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Comments posted to this topic are about the item Fun with JSON II
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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 t1.[key] AS row,
t2.*
FROM OPENJSON(
(
SELECT t.* FROM #test_data AS t FOR JSON PATH
)
) t1
CROSS APPLY OPENJSON(t1.value) t2; See possible answers