Explain Various Transformations Available in SSIS

 DATACONVERSION: Converts columns data types from one to another type. It stands for Explicit Column Conversion.

DATAMININGQUERY: Used to perform data mining query against analysis services and manage Predictions Graphs and Controls.

DERIVEDCOLUMN: Create a new (computed) column from given expressions.

EXPORTCOLUMN: Used to export a Image specific column from the database to a flat file.

FUZZYGROUPING: Used for data cleansing by finding rows that are likely duplicates.

FUZZYLOOKUP: Used for Pattern Matching and Ranking based on fuzzy logic.

AGGREGATE: It applies aggregate functions to Record Sets to produce new output records from aggregated values.

AUDIT: Adds Package and Task level Metadata: such as Machine Name, Execution Instance, Package Name, Package ID, etc..

CHARACTERMAP: Performs SQL Server column level string operations such as changing data from lower case to upper case.

MULTICAST: Sends a copy of supplied Data Source onto multiple Destinations.

CONDITIONALSPLIT: Separates available input into separate output pipelines based on Boolean Expressions configured for each output.

COPYCOLUMN: Add a copy of column to the output we can later transform the copy keeping the original for auditing.

IMPORTCOLUMN: Reads image specific column from database onto a flat file.

LOOKUP: Performs the lookup (searching) of a given reference object set to a data source. It is used for exact matches only.

MERGE: Merges two sorted data sets into a single data set into a single data flow.

MERGEJOIN: Merges two data sets into a single dataset using a join junction.

ROWCOUNT: Stores the resulting row count from the data flow / transformation into a variable.

ROWSAMPLING: Captures sample data by using a row count of the total rows in dataflow specified by rows or percentage.

UNIONALL: Merge multiple data sets into a single dataset.

PIVOT: Used for Normalization of data sources to reduce anomalies by converting rows into columns

UNPIVOT: Used for de-normalizing the data structure by converts columns into rows in case of building Data Warehouses.

Comments

Post a Comment

Popular posts from this blog

Difference between ETL vs. ELT

Image
  Difference between ETL vs. ELT Basics ETL ELT Process Data is transferred to the ETL server and moved back to DB. High network bandwidth required. Data remains in the DB except for cross Database loads (e.g. source to object). Transformation Transformations are performed in ETL Server. Transformations are performed (in the source or) in the target. Code Usage Typically used for Source to target transfer Compute-intensive Transformations Small amount of data Typically used for High amounts of data Time-Maintenance It needs highs maintenance as you need to select data to load and transform. Low maintenance as data is always available. Calculations Overwrites existing column or Need to append the dataset and push to the target platform. Easily add the calculated column to the existing table. Analysis
Read more

What are the for each loop enumerators available in SSIS?

  Below are the lists of various types of enumerators provided by SSIS Foreach Loop Container: Foreach  File Enumerator : It enumerates files in a folder. The plus point here is it can traverse through subfolders also. Foreach  Item Enumerator : It enumerates items in a collection. Like enumerating rows and columns in an Excel sheet. Foreach  ADO Enumerator : Useful for enumerating rows in tables. Foreach  ADO.NET Schema  Rowset  Enumerator : To enumerate through schema information about a data source. For example, to get list of tables in a database. Foreach   From  Variable Enumerator : Used to enumerate through the object contained in a variable. (if the object is enumerable) Foreach   NodeList  Enumerator : Used to enumerate the result set of an XML Path Language (XPath) expression. Foreach  SMO Enumerator : It enumerates through SQL Server Management Objects (SMO) objects.
Read more

What are the SSIS package protection levels?

  There are 6 different types of protection levels. Do not save sensitive – (When exporting using DTUTIL specify for protection- 0) Encrypt sensitive with user key – 1 Encrypt sensitive with password – 2 Encrypt all with password -3 Encrypt all with user key – 4 Rely on server storage Do not save sensitive:  makes the sensitive data unavailable to other users. If a different user opens the package, the sensitive information is replaced with blanks and the user must provide the sensitive information. Encrypt sensitive with user key:  Uses a key that is based on the current user profile to encrypt only the values of sensitive properties in the package. Only the same user who uses the same profile can load the package. If a different user opens the package, the sensitive information is replaced with blanks and the current user must provide new values for the sensitive data. If the user attempts to execute the package, package execution fails. Encrypt sensitive with password:...
Read more