Analyzers , which are the most important components. Actually, without at least one analyzer the job will not run (if you execute the job without adding one, DataCleaner will suggest adding a basic one that will save the output to a file). An analyzer is a component that inspects the data that it receives and generates a result or a report. The majority of the data profiling cruft is created as analyzers.

Transformers are components used to modify the data before analyzing it. Sometimes it's neccessary to extract parts of a value or combine two values to correctly get an idea about a particular measure. In other scenarios, transformers can be used to perform reference data lookups or other similar tasks and place the results of an operation into the stream of data in the job.

The result of a transformer is a set of output columns. These columns work exactly like regular columns in your job, except that they have a preceding step in the flow before they become materialized.

Filters are components that split the flow of processing in a job. A filter will have a number of possible outcomes and depending on the outcome of a filter, a particular row might be processed by different sub-flows. Filters are often used simply to disregard certain rows from the analysis, eg. null values or values outside the range of interest.