Python is probably the most efficient way if you have lots of tables, but there are ways to do this without.

e.g. a null attribute mapper to map all values to 0, a second to map all null values to 1 (edited for accuracy), an attributeexploder then an aggregator on AttributeValue and sum the values

Or after the null attribute mappers you could use a statistics calculator to sum the values and then pivot the output

Some other non-python suggestions here

https://community.safe.com/s/question/0D54Q000080heE3SAI/statistical-calculator

This is a great answer. I think you meant "a null attribute mapper to map all values to 0, a second to map all null values to 1". I hadn't realised that NullAttributeMapper could use a regex to process everthing that isn't null or empty.

One problem I found in the solution was the need to use Selected Attributes on NullAttributeMapper in order to process the "missing values". I found that I then needed a new transformer for every input table. I got around this by first converting all the data to SQLite, which then I think uses proper nulls rather than Missing.

If your backend database support SQL, that would be my preferred solution as it would be an order of magnitude quicker than doing it in FME, especially for larger tables. You can use the SQLExecutor in FME to get the results back into FME.

For example you could use the "Schema (any format)" reader to retrieve all the tables and column names, then send them to the SQLExecutor.

Example SQL:

SELECT
  COUNT(1) AS TotalRowCount
 ,COUNT(myColumn) AS TotalNotNull
 ,COUNT(1) - COUNT(myColumn) AS TotalNull
 ,100.0 * COUNT(myColumn) / COUNT(1) AS PercentNotNull
FROM myTable

This calculates the percentage of records where column myColumn is not null in table myTable.

I got interested again in this solution, since as you point out, it would scale up quite well. However it doesn't seem to work with SQLite.  My input data is mainly spreadsheets and Esri shapefile - its easy to add FeatureWriter to convert to SQLite Non Spatial .

Here is my SQL from the SQLExecutor - I need a different SQL for each column so I have substituted the column name in. What I think happens is that SQLExecutor deals with this correctly where the substution string is in place of a SQL expression, but not in place of schema strings such as COUNT(column_name).

SELECT '@Value(_attr_name)' AS AttrName
 ,COUNT(1) AS TotalRowCount
 ,COUNT('@Value(_attr_name)') AS TotalNotNull
 ,COUNT(1) - COUNT('@Value(_attr_name)') AS TotalNull
 ,100.0 * COUNT('@Value(_attr_name)') / COUNT(1) AS PercentNotNull
FROM "ForumExample"

Here is the table leading into my SQL Executor

And this is what I get out:

This is a great answer. I think you meant "a null attribute mapper to map all values to 0, a second to map all null values to 1". I hadn't realised that NullAttributeMapper could use a regex to process everthing that isn't null or empty.

One problem I found in the solution was the need to use Selected Attributes on NullAttributeMapper in order to process the "missing values". I found that I then needed a new transformer for every input table. I got around this by first converting all the data to SQLite, which then I think uses proper nulls rather than Missing.

I got interested again in this solution, since as you point out, it would scale up quite well. However it doesn't seem to work with SQLite.  My input data is mainly spreadsheets and Esri shapefile - its easy to add FeatureWriter to convert to SQLite Non Spatial .

Here is my SQL from the SQLExecutor - I need a different SQL for each column so I have substituted the column name in. What I think happens is that SQLExecutor deals with this correctly where the substution string is in place of a SQL expression, but not in place of schema strings such as COUNT(column_name).

SELECT '@Value(_attr_name)' AS AttrName
 ,COUNT(1) AS TotalRowCount
 ,COUNT('@Value(_attr_name)') AS TotalNotNull
 ,COUNT(1) - COUNT('@Value(_attr_name)') AS TotalNull
 ,100.0 * COUNT('@Value(_attr_name)') / COUNT(1) AS PercentNotNull
FROM "ForumExample"

Here is the table leading into my SQL Executor

And this is what I get out: