Error Handling In Dw Bi
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Logging and Handling Dimension Overview Create Dimensions Viewing Dimension Validating Dimensions Altering Dimensions Mviews Need Schema error handling in data warehouse Design Design Guidelines Types Manage Create Enable Logs Refresh Refresh
Error Handling And Logging Mechanism In Data Warehouse
Options Parallelism Overview Types Parallel SQL Rules Partitioning Overview Methods Performance Issues Table Compression error handling and logging mechanism in datastage Pruning Pruning Techniques Tips Joins and Partitions Partitition-wise Joins Partial Partitition-wise Joins Benefits Performance Index Partitioning Global Local Guidelines Refresh CDC Change Data Capture Advantages Modes Other Constraints Cost-Based Optimization Indexes Star Queries Star Transformation Informatica Tutorials Subscribe I Recommend DW Blogs Big Data Blog BI Portal ETL Process DW Basics DW Comparisons Ab Initio Blog 1010data Blog Actuate Blog Autosys Blog BO Blog Cognos Blog DataStage Blog Hadoop Blog Informatica Blog Greenplum Blog MapReduce Blog MicroStrategy Blog Netezza Blog Oracle Blog Pig Blog QlikView Blog SAS Blog Teradata Blog WebFOCUS Blog Zookeeper Blog Big Data Analytics Error Logging and Handling Mechanisms 9:38 AM divjeev Having data that is not clean is very common when loading and transforming data,especially when dealing with data coming from a variety of sources, includingexternal ones. If this dirty data causes you to abort a long-running load ortransformation operation, a lot of time and resources will be wasted. The followingsections discuss the two main causes of errors and how to address them:■ Business Rule Violations■ Data Rule Violations (Data Errors)Business Rule ViolationsData that is logically not clean violates business rules that are known prior to any data consumption. Most of the time, handling these kind of errors will be incorporated into the loading or transformation process. However, in situations whe
analysis can be flawed. Given the considerable dependence on data in EPM tables, all source data entering EPM must be validated. Data validations are performed when you run ETL jobs. Because we want to ensure that complete, accurate data resides in the OWE and MDW tables, data validations are embedded in the jobs that load data from the OWS to the OWE and MDW. Therefore, data that passes the validation process is loaded into OWE and MDW target http://oracle-datawarehousing.blogspot.com/2011/02/error-logging-and-handling-mechanisms.html tables, while data that fails the validation process is redirected to separate error tables in the OWS. This ensures that flawed data never finds its way into the target OWE and MDW tables. Error tables log the source values failing validation to aid correction of the data in the source system. There is an error table for https://docs.oracle.com/cd/E41507_01/epm91pbr3/eng/epm/penw/concept_UnderstandingDataValidationAndErrorHandlingInTheETLProcess.html each OWS driver table. OWS driver tables are those tables that contain the primary information for the target entity (for example customer ID). Data Completeness Validation and Job Statistic Summary for Campus Solutions, FMS, and HCM Warehouses A separate data completeness validation and job statistic capture is performed against the data being loaded into Campus Solutions, FMS, and HCM MDW tables (for example, validating that all records, fields, and content of each field is loaded, determining source row count versus target insert row count, and so forth). The validation and job statistic tracking is also performed in ETL jobs. The data is output to the PS_DAT_VAL_SMRY_TBL and PS_DATVAL_CTRL_TBL tables with prepackaged Oracle Business Intelligence (OBIEE) reports built on top of the tables. See PeopleSoft EPM: Fusion Campus Solutions Intelligence for PeopleSoft. Understanding the Data Validation Mechanism The following graphic represents the data validation-error handling process in the PeopleSoft delivered J_DIM_PS_D_DET_BUDGET job: Image: Data validation in the J_DIM_PS_D_DET_BUDGET job This example illustrates the Data validation in the J_DIM_PS_D_DET_BUDGET job. N
Server 2016 SQL Server 2014 SQL Server 2012 SQL Server 2008 AdministrationBackup and Recovery Cloud High Availability Performance Tuning PowerShell Security Storage Virtualization http://sqlmag.com/business-intelligence/dts-error-handling-revealed DevelopmentASP.NET Entity Framework T-SQL Visual Studio Business IntelligencePower BI SQL Server Analysis Services SQL Server Integration Services SQL Server Reporting Services InfoCenters Advertisement Home > Business Intelligence > DTS: Error Handling Revealed DTS: Error Handling Revealed Oct 31, 1999 Brian Lawton and Don Awalt | SQL Server Pro EMAIL Tweet Comments 2 Advertisement Downloads 6196.zip Errors got error handling you down? Here's help! If you've programmed with Data Transformation Services (DTS), you'll probably agree that error handling is one of the most confusing and challenging problems the DTS developer faces. In our July 1999 article, "The DTS Development Guide," we looked briefly at the built-in error-handling options the Package Designer offers. This month, we discuss in error handling in depth some programmatic opportunities for using the DTS Object Model to handle errors. We also examine DTS event handling. The DTS Perspective on Errors DTS doesn't consider an error to be a definitively right or wrong result, but rather the status of work accomplished. DTS leaves the interpretation of that status to the developer. Thus, developers can control the execution of a package, task, step, or transformation by monitoring the state or return value of the executed operation. For example, when transforming data via an ActiveX script, developers can control the processing by setting the return code in the script to any one of the values in the DTSTransformStatus enumeration list, which Table 1, page 46, shows. Table 2, page 46, lists other DTS status codes and result constants (their specific values are available in Books Online—BOL). Each of these lists of predefined constants lets developers determine whether, and in what manner, transformation processing will continue. If you've previously developed only with the Package Designer, you haven't seen these v
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