Each column in the input has two settings that you can set. The first is the checkbox below screen shot shows a few columns selected. This determines whether the Fuzzy Grouping will use this column to identify duplicates. The Pass Through column enables columns to appear downstream even when they are not used in the identification of duplicates.
Another thing that above screen shot highlights is that the Fuzzy Grouping Transformation provides the same capability as the Fuzzy Lookup to set a minimum similarity on a column-by-column basis. On the Advanced tab, shown in below screen shot, you can fine-tune the Fuzzy Grouping to specify the overall Similarity threshold. If a potential matching row does not meet this threshold, it is not considered in the de-duplication.
You can also set the output columns. Just as in the Fuzzy Lookup, you can see the output by adding a Data Viewer to the output path from the Fuzzy Grouping. Below screen shot illustrates how the Fuzzy Grouping works. When the Fuzzy Grouping identifies a potential match, it moves the row next to the potential match row.
As the example in Figure shows, there are a couple of matches. In another couple of rows highlighted, the street address is slightly different. Alternately, you could define custom expression logic to choose an alternate row.
As the preceding two sections have demonstrated, both the Fuzzy Lookup and the Fuzzy Grouping provide very powerful data cleansing features that can be used in a variety of data scenarios.
Learn how to use SSIS, from beginner basics to advanced techniques, with online video tutorials taught by industry experts. Perfect guide for getting started to applied SSIS. Download Now! Power BI. Fuzzy lookup. Fuzzy Grouping. Fuzzy Lookup performs data standardization, correcting and providing missing values. Fuzzy Grouping performs a data cleaning task by identifying rows of data that are likely to be duplicated. Fuzzy Lookup enables you to match input records with clean, standardized records in a reference table.
Fuzzy Grouping enables you to identify groups of records in a table where each record in the group potentially corresponds to the same real-world entity. Fuzzy Lookup returns the closest match in order to perform the fuzzy join. It can be used to identify fuzzy duplicate rows within a single table or to fuzzy join similar rows between two different tables.
Select the left and right tables for the comparison to identify matches in a single table, set the left and right tables to be the same Columns with the same heading will be automatically joined. Fuzzy models or sets are mathematical means of representing vagueness and imprecise information hence the term fuzzy. These models have the capability of recognising, representing, manipulating, interpreting, and utilising data and information that are vague and lack certainty.
Fuzzy Search is the process to locate records that are relevant to a search , even when the search criteria doesn't match. Soundex is a phonetic algorithm for indexing names by sound, as pronounced in English.
It was developed and patented in and Category: technology and computing artificial intelligence. Unlike Lookup Transformation, the Fuzzy Lookup transformation in SSIS uses fuzzy matching to find one or more close matches in the reference table and replace the source data with reference data. How do I set up fuzzy lookup? What is similarity threshold in Fuzzy Lookup? What is multicast in SSIS? Sign in to vote. Hi all, Can you pls explain difference between Fuzzy lookup and Fuzzy grouping in simple word,pls Thanks Selva.
Monday, June 30, AM. Hi Selva, In brief, the Fuzzy Grouping Transformation can be used to group the similar rows in the source dataset and identify rows of data that are likely to be duplicate; while the Fuzzy Lookup Transformation can match records between the source table and reference table that are similar, but not identical to, the lookup key.
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