Hey, you can easily achieve this transformation using SAS or Python, depending on your preference. In SAS: You can use the INPUT function to read the original datetime format and then split it into date and time columns:   sas data B; set A; format Date mmddyy10. Time timeampm.; datetime = input(OriginalColumn, datetime20.); Date = datepart(datetime); Time = timepart(datetime); run; In Python (Pandas): Using Python, you can achieve the same with datetime and strftime :   python   import pandas as pd data = {'OriginalColumn': ['11JAN21:13:15:00', '12JUL02:12:30:02']} df = pd.DataFrame(data) # Convert to datetime df['Datetime'] = pd.to_datetime(df['OriginalColumn'], format='%d%b%y:%H:%M:%S') # Extract Date and Time df['Date'] = df['Datetime'].dt.strftime('%m/%d/%Y') df['Time'] = df['Datetime'].dt.strftime('%I:%M:%S%p') print(df[['Date', 'Time']]) Handling date and time can sometimes cause issues with formats like leap years or AM/PM shifts. That could be tricky, especially with February's 28-29 day inconsistency. It was fixed with an additional C++ class in  Timechart Work Time. ... View more

Great article on automating SAS LASR Analytic server operations! I’ve been exploring similar automation for my team’s workflow. While implementing batch scripts, we often face challenges with monitoring execution logs effectively. Do you recommend any best practices for real-time log tracking during server start/stop processes? On a related note, I came across Work Time, which offers some interesting insights into task tracking and automation tools. Would you say such platforms could complement SAS environment monitoring, or is it better to stick with native tools for this use case? ... View more