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ContentRecap and Q&ASplit and Join Transformations
Demo: Conditional Split Demo: Multicast Demo: Union All Demo: Merge Demo: Merge Join Demo: Lookup Demo: Cache
Business Intelligence Transformations Demo: SCD Demo: Fuzzy Grouping Demo: Term Extraction
Auditing Transformations Demo: Audit Demo: Row count
Summary
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Recap and Q&A Data Flow Transformations Synchronous vs Asynchronous Transformations Row Transformations
Demo: Character Map Demo: Copy Column Demo: Data Conversion Demo: Derived Column Demo: Export Column Demo: OLE DB Command
Rowset Transformations Demo: Aggregate Demo: Sort Demo: Pivot Demo: Unpivot Demo: Percentage Sampling Demo: Row Sampling
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Split and Join Transformations These transformations distribute rows to different outputs, create
copies of the inputs, join multiple inputs into one output, and perform lookup operations.
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Transformation DescriptionConditional Split Transformation
The transformation that routes data rows to different outputs.Multicast Transformation
The transformation that distributes data sets to multiple outputs.Union All Transformation
The transformation that merges multiple data sets.Merge Transformation The transformation that merges two sorted data sets.Merge Join Transformation
The transformation that joins two data sets using a FULL, LEFT, or INNER join.
Lookup Transformation The transformation that looks up values in a reference table using an exact match.
Cache Transform The transformation that writes data from a connected data source in the data flow to a Cache connection manager that saves the data to a cache file. The Lookup transformation performs lookups on the data in the cache file.
Conditional SplitThe transformation that routes data rows
to different outputs.
Similar to CASE decision structure
Must specify the default output for the transformation.
It has one input, one or more outputs, and one error output
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MulticastThe transformation that distributes data
sets to multiple outputs.
This capability is useful when the package needs to apply multiple sets of transformations to the same data
Multicast transformation directs every row to every output
It has one input , multiple outputs and no error output.
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Union All The transformation that merges multiple data
sets.
Inputs are added to output one after the other.
No reordering of rows occurs.
It has multiple inputs , one output and no error output.
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MergeThe transformation that merges two sorted
data sets. The rows from each dataset are inserted into the output based on values in their key columns.
It has two inputs, one output and no error output.
Use the Union All transformation instead of the Merge transformation in situations:
The transformation inputs are not sorted. The combined output does not need to be sorted. The transformation has more than two inputs.
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Merge Join This transformation that joins two data sets
using a FULL, LEFT, or INNER join.
Requires sorted data for its inputs.
Specify the join is a FULL, LEFT, or INNER join.
Specify the columns the join uses.
Specify whether the transformation handles null values as equal to other nulls.
It has two inputs, one output and no error output.
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LookupThe transformation that looks up values in a
reference table using an exact match.
Uses either an OLE DB connection manager or a Cache connection manager.
If there are multiple matches, returns only the first match.
Lookup match is case sensitive
It has input, match output, no matched output and error.
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Cache The Cache transformation generates a reference
dataset for the Lookup Transformation by writing data from a connected data source in the data flow to a Cache connection manager that saves the data to a cache file.
Writes only unique rows to the Cache connection manager.
In a single package, only one Cache Transform can write data to the same Cache connection manager.
If the package contains multiple Cache Transforms, the first Cache Transform that is called when the package runs, writes the data to the connection manager. The write operations of subsequent Cache Transforms fail.
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Business Intelligence Transformations These transformations perform BI operations such as cleaning
data, mining text, and running data mining prediction queries.
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Transformation Description
Slowly Changing Dimension Transformation
The transformation that configures the updating of a slowly changing dimension.
Fuzzy Grouping Transformation
The transformation that standardizes values in column data.
Fuzzy Lookup Transformation
The transformation that looks up values in a reference table using a fuzzy match.
Term Extraction Transformation
The transformation that extracts terms from text.
Term Lookup Transformation
The transformation that looks up terms in a reference table and counts terms extracted from text.
SCD The transformation that configures the updating
of a slowly changing dimension.
Supports four types of changes: changing attribute, historical attribute, fixed attribute, and inferred member.
Only supports connections to SQL Server.
It has one input , up to six outputs and no error.
It requires at least non null one business key column.
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Fuzzy Grouping The transformation that standardizes values in column
data.
Requires a connection to an instance of SQL Server.
Select the input columns to use when identifying duplicates, and select the type of match—fuzzy or exact.
Uses an equi-join to locate at least one matching record, and returns records with no matching records.
It has one input and one output. It does not support an error output.
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Term Extraction The transformation that extracts terms from text.
Works only with English text
Can extract nouns only, noun phrases only, or both but articles and pronouns are not extracted.
You can use the Term Extraction transformation to discover the content of a data set. For example, text that contains e-mail messages may provide useful feedback about products, so that you could use the Term Extraction transformation to extract the topics of discussion in the messages, as a way of analyzing the feedback.
One output column term contains the extracted terms and the other output column sore contains the score.
Articles and pronouns are not extracted. For example, the Term Extraction transformation extracts the term bicycle from the text the bicycle, my bicycle, and that bicycle.
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SummaryRecap and Q&ASplit and Join Transformations
Demo: Conditional Split Demo: Multicast Demo: Union All Demo: Merge Demo: Merge Join Demo: Lookup Demo: Cache
Business Intelligence Transformations Demo: SCD Demo: Fuzzy Grouping Demo: Term Extraction
Auditing Transformations Demo: Audit Demo: Row count
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DEMO6_2_DataFlowTransformation.dtsx 6_2_DataFlowTransformation_OnlineDemo.dtsx
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Resources & QuestionsContact me :
[email protected] http://pramodsingla.wordpress.com/
Microsoft Resources: http://www.phpring.com/data-flow-transformation-categories-in-ssis/ http://sqlblog.com/blogs/jorg_klein/archive/2008/02/12/ssis-lookup-transformation-is-
case-sensitive.aspx http://pivottransform.blogspot.in/ http://www.jasonstrate.com/2011/01/31-days-of-ssis-unpivot-transformation-1131/
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