Error Handling Strategy Data Warehouse
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Partitioning Strategy In Data Warehouse
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Data Mart Strategy
Information ManagementUltra MessagingUltra Messaging OptionsUltra Messaging Persistence EditionUltra Messaging Queuing EditionUltra Messaging Streaming EditionVibe Data StreamDocumentationKnowledge BaseResourcesPAM (Product Availability Matrices)Support TVInformatica Expert AssistantVelocity (Best Practices)Mapping TemplatesDebugging ToolsUser Groupsglobal.search.communityLog inSign Up Informatica Network > Mapping Templates > Transformation Techniques > Documents Currently Being Moderated data mining strategy Error Handling Strategy Created by Vidya Madabhushi on Jun 25, 2012 10:51 PM. Last modified by Vidya Madabhushi on Mar 18, 2015 7:33 AM. Version 7 PurposeImplement an error handling strategy while demonstrating the usage of a joiner transformation and mapplet.UsageUse this template as a guide for trapping errors in a mapping based on business requirements, sending errors to an error table so that they can be reviewed and corrected, and reloading fixed errors from the error table into the target system.OverviewThis mapping template provides two mappings (which contain a shared mapplet) that, taken together, illustrate a simple approach to utilizing PowerCenter objects in handling known types of errors (based on identified business requirements).The essential transformation objects shown and utilized are expression transformations that provide error validation, lookup transformation
Error Logging and Handling Mechanisms Loading and Transformation Scenarios Overview of Loading and Transformation in Data Warehouses Data transformations are often the most complex and, in terms
Error Handling In Datastage Jobs
of processing time, the most costly part of the extraction, transformation, and loading
Etl Error Handling Best Practice
(ETL) process. They can range from simple data conversions to extremely complex data scrubbing techniques. Many, if not all, etl data validation data transformations can occur within an Oracle database, although transformations are often implemented outside of the database (for example, on flat files) as well. This chapter introduces techniques for implementing scalable https://network.informatica.com/docs/DOC-7290 and efficient data transformations within the Oracle Database. The examples in this chapter are relatively simple. Real-world data transformations are often considerably more complex. However, the transformation techniques introduced in this chapter meet the majority of real-world data transformation requirements, often with more scalability and less programming than alternative approaches. This chapter does not seek to illustrate all of the typical transformations https://docs.oracle.com/database/121/DWHSG/transform.htm that would be encountered in a data warehouse, but to demonstrate the types of fundamental technology that can be applied to implement these transformations and to provide guidance in how to choose the best techniques. Because ETL can become complex and suffer from poor performance, Oracle Database provides a user interface that enables you to monitor and report on database operations that are part of an ETL plan. A database operation is a set of related database tasks defined by end users or application code. Database operation monitoring is extremely useful for troubleshooting a suboptimally performing job and helps to identify where and how much resources are being consumed at any given step. Thus, database operations enable you to track related information, identify performance bottlenecks and also reduce time to tune database performance problems. Data Warehouses: Transformation Flow From an architectural perspective, you can transform your data in two ways: Multistage Data Transformation Pipelined Data Transformation Staging Area Multistage Data Transformation The data transformation logic for most data warehouses consists of multiple steps. For example, in transforming new records to be inserted into a sales ta
Implementing SSIS ETL for Data Vault The Logical Data Warehouse: implementing DV virtualisation Data Vault modelling standards Data Vault ETL automation patterns EDW Virtualisation Software http://roelantvos.com/blog/?p=14 Installation and setup Examples Frequently Asked Questions Publications New frontiers – Data Warehouse and Data virtualisation About Contact Architecture 3 Ideas for general error handling, why need error tables? http://www.rittmanmead.com/blog/2010/01/data-rules-and-error-handling-in-warehouse-builder-10gr211gr2/ by Roelant Vos · February 8, 2010 The possible scenarios regarding error and exception handling are limited. You can either: - Detect an error, stop the process and error handling present the error code. - Detect an error and write the record in an error table with the corresponding code. - Detect an error and write the record in both the target and the error table with the error code. - Detect an error, flag the record but write it to the DWH table anyway including the error in data warehouse code. This type of error handling is determined in the general (project) architecture and the functional design. The information requirement is very dependant of the situation. For instance, some financial systems require completeness in records so that the total sums show a number that matches the reality. Even if the details / reference data is dodgy! On the other hand some systems require data with 100% quality so no (detected) errors may pass to the target table. This is why the type of error handling (= business requirement) should be determined in the general (data) architecture and the functional design. Using error tables has its impact on ETL architecture and this is why the general concept of error handling should be examined early on. Error records should be updated if the errors in the record change and should be deleted (or flagged) if the record does not contain errors anymore. I would always try to allow an error-flagged record to be written to the DWH tables, regardless of using separ
consistency issues in your data. Data Rules act as virtual constraints on your data and add error handling tables to your mappings to allow you to divert off, or reprocess data that fails one or more consistency checks. Let's take a look at an example to see how they work. To create an example, we have a table called CUSTOMERS_TGT that has a column called GENDER. We want to enforce a data rule that says this column cannot be null. However we want our Warehouse Builder mapping to catch rows coming in that have null in this column, and replace the nulls with an "Unknown" value. To start this process we open up the target table in the Data Object Editor (I'm using version 11gR1 of Warehouse Builder, but the feature works the same in 10gR2 and 11gR2), click on the Data Rules tab at the bottom, and click on the Apply Rule button. I am then presented with the first page of a wizard, and then I'm shown a list of pre-defined data quality checks that I can select from. I select the NOT NULL check and press Next to continue, I then name the data quality check and are then asked which table column the check should be "bound" to. I select the GENDER column and press Next to continue.
Once the wizard is complete, my table now has an entry under Data Rules, with this Not Null rule shown as applying and bound to the GENDER column. I then use the Control Center Manager to redeploy this table, not because the new data rule has caused a physical NOT NULL constraint to be added to the target table (all of these constraints are "virtual" and handled within the Warehouse Builder mapping), but because of a "Shadow Table" that needs to be deployed alongside the target table to handle the errors. You don't see this table listed in the set of tables within the OWB database module, it only appears as a script when you go to redeploy the table, but it's important you deploy this (or create your own of the same definition) otherwise the mappings later on will fail. Note also that your target table shouldn't have a real NOT NULL constraint on it , as this can cause your mapping to fail