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Data Profiling Process After you create a data profile, you can run the profile session and view the results in a report. The following steps describe the data profiling process: Create a data profile. Run the profile session. View the reports. 1. Create a data profile.  Use the Profile Wizard in the Designer to create a data profile based on a source definition and a set of functions. The Profile Wizard generates a mapping and a session based on criteria that you provide. 2. Run the profile session.  You can choose to run the profile session when you finish the Profile Wizard, or you can run it from the Profile Manager. The Integration Service runs the session and loads the profile results to the Data Profiling warehouse. 3. View the reports.  View the Data Profiling report associated with the profile session. Based on the type of profile report, you can view reports from the Profile Manager or from Data Analyzer.

Data Profiling Connectivity Power Center Data Profiling uses the following types of connectivity: TCP/IP. Native. ODBC. JDBC. TCP/IP.  The PowerCenter Client and the Integration Service use native protocol to communicate with the Repository Service. Native.  The Integration Service uses native database connectivity to connect to the Data Profiling warehouse when it loads target data from the profiling sessions. ODBC.  The PowerCenter Client uses ODBC to connect to the Data Profiling warehouse when you run data profiling reports from the Profile Manager. JDBC.  Data Analyzer uses JDBC to connect to the Data Profiling warehouse when you run data profiling reports.

What is Data Profiling Data profiling is a technique used to analyze the content, quality, and structure of source data. Use Power Center Data Profiling to detect patterns and exceptions of source data during mapping development and during production. Use data profiling to make the following types of analyses: Make initial assessments. Validate business rules Verify assumptions. Verify report validity. Make initial assessments.  You can make initial assessments about data patterns and exceptions data during mapping development. As a result, you can design mappings and workflows on actual data, rather than make theoretical assumptions about sources. Validate business rules.  You can validate documented business rules about the source data. For example, if you have a business rule requiring columns in a source table to contain U.S. ZIP codes, you can profile the source data to verify that the rows in this table contain the proper values. Verify assumptions. ...

Informatica Operator Precedence The transformation language supports the use of multiple operators and the use of operators within nested expressions. If you write an expression that includes multiple operators, the PowerCenter Integration Service evaluates the expression in the following order: 1. Arithmetic operators 2. String operators 3. Comparison operators 4. Logical operators The PowerCenter Integration Service evaluates operators in the order they appear in the following table. It evaluates operators in an expression with equal precedence to all operators from left to right. The following table lists the precedence for all transformation language operators: Operator Meaning (  ) Parentheses. +, -, NOT Unary plus and minus and the logical NOT operator. *, /,% Multiplication, division, modulus. +, - Addition, subtraction. || Concatenate. <, <=, >, >= Less than, less than or equal to, greater than, greater than or equal to. =, <>,...

Introduction to Informatica Informatica is an ETL tool which is used to simplify the ETL operations. Informatica provides an environment that allows you to load data into a centralized location, such as a data warehouse or operational data store (ODS). You can extract data from multiple sources, transform the data according to business logic and load the transformed data into file or in target tables. What is ETL? ETL stands for  E xtract,  T ransform and  L oad. Extraction  – Extracting the data from different sources (it can be flat files, different      databases). Transformation  – Business logic that can be applied on    data to fulfill the requirement. Loading  – After applying the transformation, loading the data into the targeted file or database. Why ETL?      Reporting and Analyzing – Business users wants the reports that consists of data for the business purposes in...

Export Informatica Objects using shell script Exporting Informatica Objects from Informatica repository using shell script is not a complex one. It is very easy if you understand the functionality of certain commands like pmrep connect  and  pmrep ObjectExport. pmrep connect  is used to connect to a Informatica repository. pmrep ObjectExport  is used to export Objects from Informatica repository. Code to connect to Informatica Server: pmrep connect -r <<repo-name>> -d <<domain-name>>-n <<infa_user>> -x <<infa_pwd>> code to Export Informatica Objects : pmrep ObjectExport -n ${soure_infa} -o workflow -m -s -b -r  -f ${folder} -u ${tgt_infa}

Data Mart Pros and Cons Independent Data Marts Independent data marts are usually the easiest and fastest to implement and their payback value can be almost immediate. Some corporations start with several data marts before deciding to build a true data warehouse. This approach has several inherent problems: While data marts have obvious value, they are not a true enterprise-wide solution and can become very costly over time as more and more are added. A major problem with proliferating data marts is that, depending on where you look for answers, there is often more than one version of the truth. They do not provide the historical depth of a true data warehouse. Because data marts are designed to handle specific types of queries from a specific type of user, they are often not good at “what if” queries like a data warehouse would be. Logical Data Marts Logical data marts overcome most of the limitations of independent data marts. They provide a single version of the trut...