In this fifth level of the SSAS Tabular stairway, learn how to clean up dimensions.
For my new mission, I set out to convert a list of files from Excel to comma-separated values (CSV). We upload the original Excel files to a Data Lake in Fabric. We then need to convert a specific worksheet and move the CSV files to a different folder in Data Lake.
Over the past years, the tool dbt – short for data build tool – has become quite popular in the data engineering world for handling such an ELT process. dbt takes on the role of the “T”, meaning it’s responsible for transforming the data in a certain data store.
How to create a connection for StreamLit and Snowflake to upload data for visualization
How we handle data warehousing updates to dimension tables is crucial and this article covers Slowly Changing Dimensions versus overwriting tables.
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Today Steve talks about big data sets and whether we really need them, or even enjoy working with them.
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Comments posted to this topic are about the item Fun with JSON II
Comments posted to this topic are about the item Changing Data Types
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