Power BI Practice: Avoid DateTime Data Loss During Data Transformation
Scenario:
Given meta data:
column_name, data_example, valid_profile
IoT GUID, 48196321-3809-EC11-883D-0022489A2..., 100%
IoT DateTime, 21/05/2022 18:59:25, 100%
IoT ID, 10000010000, 100%
The IOT ID columns are unique to each row in query. You need to analyze I0T events by the hour and day of the year. The solution must improve dataset performance.
Solution: You change the IOT DateTime column to the Date data type. Does this meet the goal?
Answer: No, changing the IoT DateTime column to the Date data type does not meet the goal of improving dataset performance for analyzing events by the hour and day of the year.
Converting the IoT DateTime column to the Date data type will strip away the time component, which is crucial for hourly analysis. Instead, you should consider the following steps:
[1] Split the DateTime Column: Create separate columns for the date and time components. This allows you to perform more granular analysis.
[2] Optimize Data Types: Ensure that the date and time columns are using the most efficient data types for performance.
[3] Indexing: If possible, apply indexing on the date and time columns to speed up query performance.