Error Handling/logging Mechanism For Dw/bi
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Logging and Handling Dimension Overview Create Dimensions Viewing Dimension Validating Dimensions Altering Dimensions Mviews Need Schema Design Design Guidelines Types Manage Create Enable Logs Refresh Refresh Options Parallelism Overview Types Parallel SQL Rules Partitioning Overview Methods error handling and logging mechanism in informatica Performance Issues Table Compression Pruning Pruning Techniques Tips Joins and Partitions Partitition-wise Joins Partial
Error Handling And Logging Mechanism In Data Warehouse
Partitition-wise Joins Benefits Performance Index Partitioning Global Local Guidelines Refresh CDC Change Data Capture Advantages Modes Other Constraints Cost-Based Optimization
Error Handling And Logging Mechanism In Datastage
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 where the error identification for all records would become too expensive and the business rule can be enforced as a data rule violation, for example, testing hundreds of columns to see if they are NOT NULL, programmers often choose to handle even known possible logical error cases more generically. Incorporating logical rules can be as easy as applying filter conditions on the datainput stream or as complex as feeding the dirty data into a different transformationworkflow. Some examples are as follows:■ Filtering of logical data errors using SQL. Data that does not adhere to certainconditions will be filtered out prior to being processed.■ Identifying and separating logical data errors.Data Rule Violations (Data Errors)Unlike logical errors, data rule violations are not usual
- PL/SQL - DML error logging Oracle - PL/SQL - DML error logging Table of Contents 1 - Introduction 2 - Articles Related 3 - The Error Table 4 - Type of error handled 5 - Statement 6 - Documentation / Reference 1 - Introduction LOG ERRORS handles errors quickly and simplifies batch loading. When you need to load millions of rows of data into a table, the most efficient way is usually to use an INSERT, UPDATE, or MERGE statement to process your data in bulk. Similarly, if you want to delete thousands of rows, using a DELETE statement is usually faster than using procedural code. But what if the data you intend to http://oracle-datawarehousing.blogspot.com/2011/02/error-logging-and-handling-mechanisms.html load contains values that might cause an integrity or check constraint to be violated, or what if some values are too big for the column they are to be loaded into? You may well have loaded 999,999 rows into your table, but that last row, which violates a check constraint, causes the whole statement to fail and roll back. In situations such as this, you have to use an alternative approach to loading your data. http://gerardnico.com/wiki/language/plsql/techniques/error_handling For example, if your data is held in a file, you can use SQL*Loader to automatically handle data that raises an error, but then you have to put together a control file, run SQL*Loader from the command line, and check the output file and the bad datafile to detect any errors. If, however, your data is held in a table or another object, you can write a procedure or an anonymous block to process your data row by row, loading the valid rows and using exception handling to process those rows that raise an error. You might even use BULK COLLECT and FORALL to handle data in your PL/SQL routine more efficiently, but even with these improvements, handling your data in this manner is still much slower than performing a bulk load by using a direct-path INSERT DML statement. Until now, you could take advantage of the set-based performance of INSERT, UPDATE, MERGE, and DELETE statements only if you knew that your data was free from errors; in all other circumstances, you needed to resort to slower alternatives. All of this changes with the release of Oracle Database 10g Release 2, which introduces a new SQL feature called DML error logging. Efficient Error Handling DML error logging enables you to write INSERT, UPDATE, MERGE, or DELETE statements that automatically deal with certa
| Related Tips: 1 | 2 | More > Integration Services Error Handling Problem In an ETL solution, error logging is a fundamental requirement for various aspects of the application and is very useful at various stages of the ETL execution life-cycle. Let's consider https://www.mssqltips.com/sqlservertip/2149/capturing-and-logging-data-load-errors-for-an-ssis-package/ a scenario where our requirement is to insert records into a SQL Server table. The package should attempt loading all the records and whichever records fail, error details reported by the database engine should be reported. We will look at how to implement this in a SSIS package. Solution From a high level view for data loads, there are three main phases in a ETL execution life-cycle. When data is being extracted (i.e. read) from source systems When data is error handling being transformed When data is loaded to the target systems In the first phase, there can be errors while establishing a connection with the source systems, or the data read from the source might not match the mappings defined in the SSIS package. Similarly, there can be different kinds of errors which can occur during this phase. In the second phase, when data is being transformed, the only major category of error that can occur is while manipulating the data. We error handling and are not considering any errors caused by hardware failures or memory as these category of errors can occur at any phase of the ETL life-cycle. In the final phase, when data is being loaded into the target system, error logging is required at a very detailed level as there can be many reasons why loading of a particular record failed. After data crosses the SSIS boundary, and is handed over to the database driver for loading into the target system, the database engine takes control of loading the data. And if the data violates the criteria defined by the entity that stores the data, an error message is generated and returned back. Each target system has their own mechanism and translation of reporting the error. For example, if one attempts to insert a record in a table which would violates the primary / foreign key constraint, that record would definitely fail. Support teams who maintain the ETL solution, would like to know the cause of each and every record failure with the supporting details that can help them clearly understand the reason of failure. One of the ways to deal with this is to log the error message reported by the database engine itself into the error log. Most of the time, the reason why the data manipulation failed becomes very apparent from the error message reported by the target system. Follow the steps below to develop a solution
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