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Bulk Credentials

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use masterCREATE LOGIN <user> use DatabaseCREATE USER <user> The EXECUTE permission was denied on the object ‘myhistory’, database ‘BMTDWHGLDB’, schema ‘get’. ALTER ROLE [exec_get] ADD MEMBER <user>;ALTER ROLE [read_hvw] ADD MEMBER <user>; DataSource.Error: Microsoft SQL: Error!! Error Number: 15151, Stored Procedure get_myHistory, Table Name: [hvw].[employee_details], Returned the following error : Cannot find the CREDENTIAL ‘https://bmtdwhuksstrprd.dfs.core.windows.net/gold/snapshot/employee/details/*.parquet’, … Read more

Last Published Date

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1. Create a blank query in Power Query Editor like so: 2. Keep “Include in report refresh” unselected. 3. Create a Card visual to show “LastPublishedDate”. 4. Before publishing the report, you need to refresh the data of the “LastPublishedDate” table, not refresh the report. 

sp_who2

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One of the first lines of defense in determining the causes of database slowdowns is to use sp_who2. sp_who2 shows all the sessions that are currently established in the database. These are denoted as SPID‘s, or Server process Id’s. Running sp_who2 is easy, all that is required is to type sp_who2 and execute it, however it’s … Read more

API Documentation: get_myview

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Purpose The get.myview stored procedure API allows Power Applications to interact with the DataWarehouse by performing SELECT queries on specified DataMart views. The results are returned as a JSON array. Security Parameters Required Optional Typical Use URL HTTP Header: cURL Example: PowerBI: PowerShell

Transforming Your Data with Python: CSV to Parquet Conversion and NaN Handling

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Efficient data storage and processing are crucial for businesses and organizations dealing with large datasets. Apache Parquet is a popular columnar storage format offering fast query performance and data compression, while CSV is a row-based format that may not be suitable for large-scale processing. This blog post covers how to convert CSV files to Parquet … Read more

Pandas.merge() function

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The merge() function in Pandas is a powerful tool for combining two or more dataframes based on one or more keys. It is analogous to the JOIN operation in SQL databases and offers various options to customize the merge behavior. Here’s the basic syntax of the merge() function: pandas.merge(left, right, how=’inner’, on=None, left_on=None, right_on=None, left_index=False, … Read more