Data Profiling – An example in Talend Profiler

Data is loaded into Data Warehouse (DW) from disparate systems and sometimes from external partners who have their own coding standards.  In any case, the quality of the data loaded into the data warehouse is often variable,and especially while discovering the data one may need to load some sample data and do some analysis including initial data profiling.  During this process one may discover differences which when resolved result in much smoother data flow along the process flow.   Or it may be at later stage, say after the summarization is completed one may need to do some analysis on type of data.  In all these cases data profiling helps and Talend provides a tool (Talend Open Profiler – TOP) to quickly and efficiently perform profiling.

Data profiling – the process of examining available data in different data sources, including databases, applications, files, data transfer from external systems etc., and collecting statistics and information – improves data quality and better reporting.

In date dimension, we have nearly 220,000 rows covering Jan.01,1900 to Dec.31,2500 (7 hundred year dates) and one of the column is ‘day_of_week_name’ (cardinality 7 – MONDAY, TUESDAY….).  This table has 70 columns including date, weeks, months, names, etc. For testing purpose, I wanted to check the nulls and pattern frequency (distribution) for ‘day_of_week_name’ column.

To do so, select the column to profile (day_of_week_name), drag and drop into “Analyzed columns” of “Analysis Settings” tab.  Then pick the indicators i.e., how you want the column measured (count, range, stats, etc.) and I picked row count and NULL count along with “Pattern Frequency Table”.  Pattern frequency will count different patterns. The results in “Analysis Results” tab shows as below.

There were 219,146 row count with no NULLs and the pattern frequency indicates 31,307 of pattern AAAAAAAAA (9A Uppercase letters), 31,307 of 7A pattern, 62,613 of 8A pattern and 93,919 of 5A pattern.

9A pattern count is of ‘WEDNESDAY’ rows, 5A pattern covers ‘SUNDAY’, ‘MONDAY’ and ‘FRIDAY’.  Similarly for other days.

You can also have your own ‘UDI’ – User Defined Indicators that can add more functionality to existing indicators.  You can build them in Java jar and import them.  But anytime you do processing in the profiler, the data needs to get transferred from database and possibly slowing down the profiling.  For smaller data set it may not be noticeable but any profiling on large tables can bog down either due to memory limitation and/or network delay.

Profiler converts your indicators to appropriate query and runs it.  For example, for the above pattern match, MySQL query looks like:

SELECT REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(`day_of_week_name`,'a','a'),'b','a'),'c','a'),'d','a'),'e','a'),'f','a'),'g','a'),'h','a'),'i','a'),'j','a'),'k','a'),'l','a'),'m','a'),'n','a'),'o','a'),'p','a'),'q','a'),'r','a'),'s','a'),'t','a'),'u','a'),'v','a'),'w','a'),'x','a'),'y','a'),'z','a'),'ç','a'),'â','a'),'ê','a'),'î','a'),'ô','a'),'û','a'),'é','a'),'è','a'),'ù','a'),'ï','a'),'ö','a'),'ü','a'),'A','A'),'B','A'),'C','A'),'D','A'),'E','A'),'F','A'),'G','A'),'H','A'),'I','A'),'J','A'),'K','A'),'L','A'),'M','A'),'N','A'),'O','A'),'P','A'),'Q','A'),'R','A'),'S','A'),'T','A'),'U','A'),'V','A'),'W','A'),'X','A'),'Y','A'),'Z','A'),'Ç','A'),'Â','A'),'Ê','A'),'Î','A'),'Ô','A'),'Û','A'),'É','A'),'È','A'),'Ù','A'),'Ï','A'),'Ö','A'),'Ü','A'),'0','9'),'1','9'),'2','9'),'3','9'),'4','9'),'5','9'),'6','9'),'7','9'),'8','9'),'9','9'), COUNT(*) c 
FROM `dw_mktg`.`dim_date` t
GROUP BY REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(REPLACE(`day_of_week_name`,'a','a'),'b','a'),'c','a'),'d','a'),'e','a'),'f','a'),'g','a'),'h','a'),'i','a'),'j','a'),'k','a'),'l','a'),'m','a'),'n','a'),'o','a'),'p','a'),'q','a'),'r','a'),'s','a'),'t','a'),'u','a'),'v','a'),'w','a'),'x','a'),'y','a'),'z','a'),'ç','a'),'â','a'),'ê','a'),'î','a'),'ô','a'),'û','a'),'é','a'),'è','a'),'ù','a'),'ï','a'),'ö','a'),'ü','a'),'A','A'),'B','A'),'C','A'),'D','A'),'E','A'),'F','A'),'G','A'),'H','A'),'I','A'),'J','A'),'K','A'),'L','A'),'M','A'),'N','A'),'O','A'),'P','A'),'Q','A'),'R','A'),'S','A'),'T','A'),'U','A'),'V','A'),'W','A'),'X','A'),'Y','A'),'Z','A'),'Ç','A'),'Â','A'),'Ê','A'),'Î','A'),'Ô','A'),'Û','A'),'É','A'),'È','A'),'Ù','A'),'Ï','A'),'Ö','A'),'Ü','A'),'0','9'),'1','9'),'2','9'),'3','9'),'4','9'),'5','9'),'6','9'),'7','9'),'8','9'),'9','9')
ORDER BY c DESC LIMIT 10

In other DBMS like Oracle, PostgresSQL and DB2 it will be much smaller query since those systems provide TRANSLATE function.
Profiler provides many built-in indicators like Soundex Frequency, Mean, Median, Mode, Range, Quartile Range, etc.

HTH,
Shiva

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