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  • error handeling in informatca power center

    - by user223541
    i want to devlop a mapping for followinfg scenerio . I have a 1 source and 1 target and 1 error table.Target and Error tables have all fields that are present in source tables.But the data type o of all fieds for error table are varchar .Error table dont have integirty or foreign key and other constraints . Error table also have2 more fileds .Error no and error msg. Now when the workflow is executed if there is erro while inserting any record then that recored shold be moved to error table.Also the data base error code and error message should be logged in error no and error message in error tables fields as mentioned. How can i devlop such a mappng?Where can i find exaples of such mapping ?

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  • What to name column in database table that holds versioning number

    - by rwmnau
    I'm trying to figure out what to call the column in my database table that holds an INT to specific "record version". I'm currently using "RecordOrder", but I don't like that, because people think higher=newer, but the way I'm using it, lower=newer (with "1" being the current record, "2" being the second most current, "3" older still, and so on). I've considered "RecordVersion", but I'm afraid that would have the same problem. Any other suggestions? "RecordAge"? I'm doing this because when I insert into the table, instead of having to find out what version is next, then run the risk of having that number stolen from me before I write, I just insert insert with a "RecordOrder" of 0. There's a trigger on the table AFTER INSERT that increments all the "RecordOrder" numbers for that key by 1, so the record I just inserted becomes "1", and all others are increased by 1. That way, you can get a person's current record by selection RecordOrder=1, instead of getting the MAX(RecordOrder) and then selecting that. PS - I'm also open to criticism about why this is a terrible idea and I should be incrementing this index instead. This just seemed to make lookups much easier, but if it's a bad idea, please enlighten me! Some details about the data, as an example: I have the following database table: CREATE TABLE AmountDue ( CustomerNumber INT, AmountDue DECIMAL(14,2), RecordOrder SMALLINT, RecordCreated DATETIME ) A subset of my data looks like this: CustomerNumber Amountdue RecordOrder RecordCreated 100 0 1 2009-12-19 05:10:10.123 100 10.05 2 2009-12-15 06:12:10.123 100 100.00 3 2009-12-14 14:19:10.123 101 5.00 1 2009-11-14 05:16:10.123 In this example, there are three rows for customer 100 - they owed $100, then $10.05, and now they owe nothing. Let me know if I need to clarify it some more. UPDATE: The "RecordOrder" and "RecordCreated" columns are not available to the user - they're only there for internal use, and to help figure out which is the current customer record. Also, I could use it to return an appropriately-ordered customer history, though I could just as easily do that with the date. I can accomplish the same thing as an incrementing "Record Version" with just the RecordCreated date, I suppose, but that removes the convenience of knowing that RecordOrder=1 is the current record, and I'm back to doing a sub-query with MAX or MIN on the DateTime to determine the most recent record.

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  • Need some information regarding data warehousing field

    - by Mirage
    I am a web developer and i would like to shift my field to data warehousing. Can anyone please give me some idea , which langauges or stuff i need to learn like cogonos , datastage, etl or IF anyone currently working can guide me how can i start , i will thankful to you. DO i nned to do oracle because i know mysql , sql. My basic understanding with databse is good. Any books

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  • Google analytics-style custom report builder UI

    - by gregmac
    I'm looking for a reporting engine/UI that can be integrated into a product, which has a UI along the lines of Google Analytics' Custom Reports builder. Is anyone aware of such a thing? The data is in our case is not page views/visitors/etc, but is similar in nature, in that there are limited entities or types of data, but each entity has many attributes/columns and many different ways of aggregating data (or in GA-style speak, metrics and dimensions). The analytics-style UI is very intuitive and allows many reports to be created in powerful ways, without having to know SQL. I have preference for a web-based tool (seeing that it is 2010 and this is a web app -- I mention only because it seems the vast majority of reporting tools still have only a non-web-based creation tool).

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  • Fact table with multiple facts

    - by Jeff Meatball Yang
    I have a dimension (SiteItem) has two important facts: perUserClicks perBrowserClicks however, within this dimension, I have groups of dimensions based on an attribute column (let's call the groups AboveFoldItems, LeftNavItems, OnTheFlyItems, etc.) each have more facts that are specific to that group: AboveFoldItems: eyeTime, loadTime LeftNavItems: mouseOverTime OnTheFlyItems: doesn't have any extra, but may in the future Is the following fact table schema ok? DateKey SessionKey SiteItemKey perUserClicks perBrowserClicks eyeTime loadTime mouseOverTime It seems a little wasteful since only some columns pertain to some dimension keys (the irrelevant facts are left NULL). But... this seems like it would be a common problem, so there should be a common solution for this, right?

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  • How to index a table with a Type 2 slowly changing dimension for optimal performance

    - by The Lazy DBA
    Suppose you have a table with a Type 2 slowly-changing dimension. Let's express this table as follows, with the following columns: * [Key] * [Value1] * ... * [ValueN] * [StartDate] * [ExpiryDate] In this example, let's suppose that [StartDate] is effectively the date in which the values for a given [Key] become known to the system. So our primary key would be composed of both [StartDate] and [Key]. When a new set of values arrives for a given [Key], we assign [ExpiryDate] to some pre-defined high surrogate value such as '12/31/9999'. We then set the existing "most recent" records for that [Key] to have an [ExpiryDate] that is equal to the [StartDate] of the new value. A simple update based on a join. So if we always wanted to get the most recent records for a given [Key], we know we could create a clustered index that is: * [ExpiryDate] ASC * [Key] ASC Although the keyspace may be very wide (say, a million keys), we can minimize the number of pages between reads by initially ordering them by [ExpiryDate]. And since we know the most recent record for a given key will always have an [ExpiryDate] of '12/31/9999', we can use that to our advantage. However... what if we want to get a point-in-time snapshot of all [Key]s at a given time? Theoretically, the entirety of the keyspace isn't all being updated at the same time. Therefore for a given point-in-time, the window between [StartDate] and [ExpiryDate] is variable, so ordering by either [StartDate] or [ExpiryDate] would never yield a result in which all the records you're looking for are contiguous. Granted, you can immediately throw out all records in which the [StartDate] is greater than your defined point-in-time. In essence, in a typical RDBMS, what indexing strategy affords the best way to minimize the number of reads to retrieve the values for all keys for a given point-in-time? I realize I can at least maximize IO by partitioning the table by [Key], however this certainly isn't ideal. Alternatively, is there a different type of slowly-changing-dimension that solves this problem in a more performant manner?

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  • In a star schema, are foreign key constraints between facts and dimensions neccessary?

    - by Garett
    I'm getting my first exposure to data warehousing, and I’m wondering is it necessary to have foreign key constraints between facts and dimensions. Are there any major downsides for not having them? I’m currently working with a relational star schema. In traditional applications I’m used to having them, but I started to wonder if they were needed in this case. I’m currently working in a SQL Server 2005 environment.

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  • True or False: Good design calls for every table to have a primary key, if nothing else, a running i

    - by Velika
    Consider a grocery store scenario (I'm making this up) where you have FACT records that represent a sale transaction, where the columns of the Fact table include SaleItemFact Table ------------------ CustomerID ProductID Price DistributorID DateOfSale Etc Etc Etc Even if there are duplicates in the table when you consider ALL the keys, I would contend that a surrogate running numeric key (i.e. identity column) should be made up, e.g., TransactionNumber of type Integer. I can see someone arguing that a Fact table might not have a unique key (though I'd invent one and waste the 4 bytes, but how about a dimension table?

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  • Loading Dimension Tables - Methodologies

    - by Nev_Rahd
    Hello, Recently I been working on project, where need to populated Dim Tables from EDW Tables. EDW Tables are of type II which does maintain historical data. When comes to load Dim Table, for which source may be multiple EDW Tables or would be single table with multi level pivoting (on attributes). Mean: There would be 10 records - one for each attribute which need to be pivoted on domain_code to make a single row in Dim. Out of these 10 records there would be some attributes with same domain_code but with different sub_domain_code, which needs further pivoting on subdomain code. Ex: if i got domain code: 01,02, 03 = which are straight pivot on domain code I would also have domain code: 10 with subdomain code / version as 2006,2007,2008,2009 That means I need to split my source table with above attributes into two = one for domain code and other for domain_code + version. so far so good. When it comes to load Dim Table: As per design specs for Dimensions (originally written by third party), what they want is: for every single change in EDW (attribute), it should assemble all the related records (for that NK) mean new one with other attribute values which are current = process them to create a new dim record and insert it. That mean if a single extract contains 100 records updated (one for each NK), it should assemble 100 + (100*9) records to insert / update dim table. How good is this approach. Other way I tried to do is just do a lookup into dim table for that NK get the value's of recent records (attributes which not changed) and insert it and update the current one. What would be the better approach assembling records at source side for one attribute change or looking into dim table's recent record and process it. If this doesn't make sense, would like to elaborate it further. Thanks

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  • Count of products NOT sold...per store, per day over the past month

    - by user1893510
    I'm struggling with an interview question. 3 dimension tables (Product, Store and Date) and 1 fact table (Sales). The question asks for a T-SQL solution that will return the count of products not sold, per store, per day over the past month. At this point, my answer is futile but I've spent significant time trying to back into a solution, to no avail, and would like to close the loop. Any guidance is greatly appreciated.

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  • Infor PM (Business Intelligence solution)

    - by Andrew
    We are currently implementing the commercial Infor PM (Performance Management) package as a business intelligence tool. Infor PM website It is apparently used by over 1,000 companies around the world, but I have found scant information about it on the net except for what's on their own website. It covers the whole range of data warehousing and BI functions with: an OLAP environment an ETL tool a report writer (called Application Studio) an add-on to Excel to connect to the data in the cubes through a pivot table etc Does anyone have any experience with using this package? How does it compare to the big players in BI (Cognos, Microsoft SSAS, Business Objects, etc). Any pitfalls I should know about? On the other hand, does it do anything better than its competitors?

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  • Getting a count of users each day in Mondrian MDX

    - by user1874144
    I'm trying to write a query to give me the total number of users for each customer per day. Here is what I have so far, which for each customer/day combination is giving the total number of user dimension entries without splitting them up by customer/day. WITH MEMBER [Measures].[MyUserCount] AS COUNT(Descendants([User].CurrentMember, [User].[User Name]), INCLUDEEMPTY) SELECT NON EMPTY CrossJoin([Date].[Date].Members, [Customer].[Customer Name].Members) ON ROWS, {[Measures].[MyUserCount]} on COLUMNS FROM [Users]

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  • Handling nulls in Datawarehouse

    - by rrydman
    I'd like to ask your input on what the best practice is for handling null or empty data values when it pertains to data warehousing and SSIS/SSAS. I have several fact and dimension tables that contain null values in different rows. Specifics: 1) What is the best way to handle null date/times values? Should I make a 'default' row in my time or date dimensions and point SSIS to the default row when there is a null found? 2) What is the best way to handle nulls/empty values inside of dimension data. Ex: I have some rows in an 'Accounts' dimensions that have empty (not NULL) values in the Account Name column. Should I convert these empty or null values inside the column to a specific default value? 3) Similar to point 1 above - What should I do if I end up with a Facttable row that has no record in one of the dimension columns? Do I need default dimension records for each dimension in case this happens? 4) Any suggestion or tips in regards to how to handle these operation in Sql server integration services (SSIS)? Best data flow configurations or best transformation objects to use would be helpful. Thanks :-)

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  • Database design: one huge table or separate tables?

    - by littlegreen
    Currently I am designing a database for use in our company. We are using SQL Server 2008. The database will hold data gathered from several customers. The goal of the database is to acquire aggregate benchmark numbers over several customers. Recently, I have become worried with the fact that one table in particular will be getting very big. Each customer has approximately 20.000.000 rows of data, and there will soon be 30 customers in the database (if not more). A lot of queries will be done on this table. I am already noticing performance issues and users being temporarily locked out. My question, will we be able to handle this table in the future, or is it better to split this table up into smaller tables for each customer?

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  • Check constraint on table lookup

    - by bzamfir
    Hi, I have a table, department , with several bit fields to indicate department types One is Warehouse (when true, indicate the department is warehouse) And I have another table, ManagersForWarehouses with following structure: ID autoinc WarehouseID int (foreign key reference DepartmentID from departments) ManagerID int (foreign key reference EmployeeID from employees) StartDate EndDate To set new manager for warehouse, I insert in this table with EndDate null, and I have a trigger that sets EndDate for previous record for that warehouse = StartDate for new manager, so a single manager appears for a warehouse at a certain time. I want to add two check constraints as follows, but not sure how to do this do not allow to insert into ManagersForWarehouses if WarehouseID is not marked as warehouse Do not allow to uncheck Warehouse if there are records in ManagersForWarehouses Thanks

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  • Hardware recommendation for Solaris 10 + ZFS data warehouse server.

    - by Justin
    The server would run a 2 drive (mirrored root pool for OS and master database segment). And would run individual zpools for each remaining drive (loss of data is acceptable). Initial requirements would be: 2x 7540 xeons (6 core) 32gig memory. 12 drives. A 4U/2U server (6/8 core and 2/4 sockets cpu support) with internal disks / or external JBOD. Capacity to house a disk per CPU core is important.

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  • New Communications Industry Data Model with "Factory Installed" Predictive Analytics using Oracle Da

    - by charlie.berger
    Oracle Introduces Oracle Communications Data Model to Provide Actionable Insight for Communications Service Providers   We've integrated pre-installed analytical methodologies with the new Oracle Communications Data Model to deliver automated, simple, yet powerful predictive analytics solutions for customers.  Churn, sentiment analysis, identifying customer segments - all things that can be anticipated and hence, preconcieved and implemented inside an applications.  Read on for more information! TM Forum Management World, Nice, France - 18 May 2010 News Facts To help communications service providers (CSPs) manage and analyze rapidly growing data volumes cost effectively, Oracle today introduced the Oracle Communications Data Model. With the Oracle Communications Data Model, CSPs can achieve rapid time to value by quickly implementing a standards-based enterprise data warehouse that features communications industry-specific reporting, analytics and data mining. The combination of the Oracle Communications Data Model, Oracle Exadata and the Oracle Business Intelligence (BI) Foundation represents the most comprehensive data warehouse and BI solution for the communications industry. Also announced today, Hong Kong Broadband Network enhanced their data warehouse system, going live on Oracle Communications Data Model in three months. The leading provider increased its subscriber base by 37 percent in six months and reduced customer churn to less than one percent. Product Details Oracle Communications Data Model provides industry-specific schema and embedded analytics that address key areas such as customer management, marketing segmentation, product development and network health. CSPs can efficiently capture and monitor critical data and transform it into actionable information to support development and delivery of next-generation services using: More than 1,300 industry-specific measurements and key performance indicators (KPIs) such as network reliability statistics, provisioning metrics and customer churn propensity. Embedded OLAP cubes for extremely fast dimensional analysis of business information. Embedded data mining models for sophisticated trending and predictive analysis. Support for multiple lines of business, such as cable, mobile, wireline and Internet, which can be easily extended to support future requirements. With Oracle Communications Data Model, CSPs can jump start the implementation of a communications data warehouse in line with communications-industry standards including the TM Forum Information Framework (SID), formerly known as the Shared Information Model. Oracle Communications Data Model is optimized for any Oracle Database 11g platform, including Oracle Exadata, which can improve call data record query performance by 10x or more. Supporting Quotes "Oracle Communications Data Model covers a wide range of business areas that are relevant to modern communications service providers and is a comprehensive solution - with its data model and pre-packaged templates including BI dashboards, KPIs, OLAP cubes and mining models. It helps us save a great deal of time in building and implementing a customized data warehouse and enables us to leverage the advanced analytics quickly and more effectively," said Yasuki Hayashi, executive manager, NTT Comware Corporation. "Data volumes will only continue to grow as communications service providers expand next-generation networks, deploy new services and adopt new business models. They will increasingly need efficient, reliable data warehouses to capture key insights on data such as customer value, network value and churn probability. With the Oracle Communications Data Model, Oracle has demonstrated its commitment to meeting these needs by delivering data warehouse tools designed to fill communications industry-specific needs," said Elisabeth Rainge, program director, Network Software, IDC. "The TM Forum Conformance Mark provides reassurance to customers seeking standards-based, and therefore, cost-effective and flexible solutions. TM Forum is extremely pleased to work with Oracle to certify its Oracle Communications Data Model solution. Upon successful completion, this certification will represent the broadest and most complete implementation of the TM Forum Information Framework to date, with more than 130 aggregate business entities," said Keith Willetts, chairman and chief executive officer, TM Forum. Supporting Resources Oracle Communications Oracle Communications Data Model Data Sheet Oracle Communications Data Model Podcast Oracle Data Warehousing Oracle Communications on YouTube Oracle Communications on Delicious Oracle Communications on Facebook Oracle Communications on Twitter Oracle Communications on LinkedIn Oracle Database on Twitter The Data Warehouse Insider Blog

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  • OWB 11gR2 - Early Arriving Facts

    - by Dawei Sun
    A common challenge when building ETL components for a data warehouse is how to handle early arriving facts. OWB 11gR2 introduced a new feature to address this for dimensional objects entitled Orphan Management. An orphan record is one that does not have a corresponding existing parent record. Orphan management automates the process of handling source rows that do not meet the requirements necessary to form a valid dimension or cube record. In this article, a simple example will be provided to show you how to use Orphan Management in OWB. We first import a sample MDL file that contains all the objects we need. Then we take some time to examine all the objects. After that, we prepare the source data, deploy the target table and dimension/cube loading map. Finally, we run the loading maps, and check the data in target dimension/cube tables. OK, let’s start… 1. Import MDL file and examine sample project First, download zip file from here, which includes a MDL file and three source data files. Then we open OWB design center, import orphan_management.mdl by using the menu File->Import->Warehouse Builder Metadata. Now we have several objects in BI_DEMO project as below: Mapping LOAD_CHANNELS_OM: The mapping for dimension loading. Mapping LOAD_SALES_OM: The mapping for cube loading. Dimension CHANNELS_OM: The dimension that contains channels data. Cube SALES_OM: The cube that contains sales data. Table CHANNELS_OM: The star implementation table of dimension CHANNELS_OM. Table SALES_OM: The star implementation table of cube SALES_OM. Table SRC_CHANNELS: The source table of channels data, that will be loaded into dimension CHANNELS_OM. Table SRC_ORDERS and SRC_ORDER_ITEMS: The source tables of sales data that will be loaded into cube SALES_OM. Sequence CLASS_OM_DIM_SEQ: The sequence used for loading dimension CHANNELS_OM. Dimension CHANNELS_OM This dimension has a hierarchy with three levels: TOTAL, CLASS and CHANNEL. Each level has three attributes: ID (surrogate key), NAME and SOURCE_ID (business key). It has a standard star implementation. The orphan management policy and the default parent setting are shown in the following screenshots: The orphan management policy options that you can set for loading are: Reject Orphan: The record is not inserted. Default Parent: You can specify a default parent record. This default record is used as the parent record for any record that does not have an existing parent record. If the default parent record does not exist, Warehouse Builder creates the default parent record. You specify the attribute values of the default parent record at the time of defining the dimensional object. If any ancestor of the default parent does not exist, Warehouse Builder also creates this record. No Maintenance: This is the default behavior. Warehouse Builder does not actively detect, reject, or fix orphan records. While removing data from a dimension, you can select one of the following orphan management policies: Reject Removal: Warehouse Builder does not allow you to delete the record if it has existing child records. No Maintenance: This is the default behavior. Warehouse Builder does not actively detect, reject, or fix orphan records. (More details are at http://download.oracle.com/docs/cd/E11882_01/owb.112/e10935/dim_objects.htm#insertedID1) Cube SALES_OM This cube is references to dimension CHANNELS_OM. It has three measures: AMOUNT, QUANTITY and COST. The orphan management policy setting are shown as following screenshot: The orphan management policy options that you can set for loading are: No Maintenance: Warehouse Builder does not actively detect, reject, or fix orphan rows. Default Dimension Record: Warehouse Builder assigns a default dimension record for any row that has an invalid or null dimension key value. Use the Settings button to define the default parent row. Reject Orphan: Warehouse Builder does not insert the row if it does not have an existing dimension record. (More details are at http://download.oracle.com/docs/cd/E11882_01/owb.112/e10935/dim_objects.htm#BABEACDG) Mapping LOAD_CHANNELS_OM This mapping loads source data from table SRC_CHANNELS to dimension CHANNELS_OM. The operator CHANNELS_IN is bound to table SRC_CHANNELS; CHANNELS_OUT is bound to dimension CHANNELS_OM. The TOTALS operator is used for generating a constant value for the top level in the dimension. The CLASS_FILTER operator is used to filter out the “invalid” class name, so then we can see what will happen when those channel records with an “invalid” parent are loading into dimension. Some properties of the dimension operator in this mapping are important to orphan management. See the screenshot below: Create Default Level Records: If YES, then default level records will be created. This property must be set to YES for dimensions and cubes if one of their orphan management policies is “Default Parent” or “Default Dimension Record”. This property is set to NO by default, so the user may need to set this to YES manually. LOAD policy for INVALID keys/ LOAD policy for NULL keys: These two properties have the same meaning as in the dimension editor. The values are set to the same as the dimension value when user drops the dimension into the mapping. The user does not need to modify these properties. Record Error Rows: If YES, error rows will be inserted into error table when loading the dimension. REMOVE Orphan Policy: This property is used when removing data from a dimension. Since the dimension loading type is set to LOAD in this example, this property is disabled. Mapping LOAD_SALES_OM This mapping loads source data from table SRC_ORDERS and SRC_ORDER_ITEMS to cube SALES_OM. This mapping seems a little bit complicated, but operators in the red rectangle are used to filter out and generate the records with “invalid” or “null” dimension keys. Some properties of the cube operator in a mapping are important to orphan management. See the screenshot below: Enable Source Aggregation: Should be checked in this example. If the default dimension record orphan policy is set for the cube operator, then it is recommended that source aggregation also be enabled. Otherwise, the orphan management processing may produce multiple fact rows with the same default dimension references, which will cause an “unstable rowset” execution error in the database, since the dimension refs are used as update match attributes for updating the fact table. LOAD policy for INVALID keys/ LOAD policy for NULL keys: These two properties have the same meaning as in the cube editor. The values are set to the same as in the cube editor when the user drops the cube into the mapping. The user does not need to modify these properties. Record Error Rows: If YES, error rows will be inserted into error table when loading the cube. 2. Deploy objects and mappings We now can deploy the objects. First, make sure location SALES_WH_LOCAL has been correctly configured. Then open Control Center Manager by using the menu Tools->Control Center Manager. Expand BI_DEMO->SALES_WH_LOCAL, click SALES_WH node on the project tree. We can see the following objects: Deploy all the objects in the following order: Sequence CLASS_OM_DIM_SEQ Table CHANNELS_OM, SALES_OM, SRC_CHANNELS, SRC_ORDERS, SRC_ORDER_ITEMS Dimension CHANNELS_OM Cube SALES_OM Mapping LOAD_CHANNELS_OM, LOAD_SALES_OM Note that we deployed source tables as well. Normally, we import source table from database instead of deploying them to target schema. However, in this example, we designed the source tables in OWB and deployed them to database for the purpose of this demonstration. 3. Prepare and examine source data Before running the mappings, we need to populate and examine the source data first. Run SRC_CHANNELS.sql, SRC_ORDERS.sql and SRC_ORDER_ITEMS.sql as target user. Then we check the data in these three tables. Table SRC_CHANNELS SQL> select rownum, id, class, name from src_channels; Records 1~5 are correct; they should be loaded into dimension without error. Records 6,7 and 8 have null parents; they should be loaded into dimension with a default parent value, and should be inserted into error table at the same time. Records 9, 10 and 11 have “invalid” parents; they should be rejected by dimension, and inserted into error table. Table SRC_ORDERS and SRC_ORDER_ITEMS SQL> select rownum, a.id, a.channel, b.amount, b.quantity, b.cost from src_orders a, src_order_items b where a.id = b.order_id; Record 178 has null dimension reference; it should be loaded into cube with a default dimension reference, and should be inserted into error table at the same time. Record 179 has “invalid” dimension reference; it should be rejected by cube, and inserted into error table. Other records should be aggregated and loaded into cube correctly. 4. Run the mappings and examine the target data In the Control Center Manager, expand BI_DEMO-> SALES_WH_LOCAL-> SALES_WH-> Mappings, right click on LOAD_CHANNELS_OM node, click Start. Use the same way to run mapping LOAD_SALES_OM. When they successfully finished, we can check the data in target tables. Table CHANNELS_OM SQL> select rownum, total_id, total_name, total_source_id, class_id,class_name, class_source_id, channel_id, channel_name,channel_source_id from channels_om order by abs(dimension_key); Records 1,2 and 3 are the default dimension records for the three levels. Records 8, 10 and 15 are the loaded records that originally have null parents. We see their parents name (class_name) is set to DEF_CLASS_NAME. Those records whose CHANNEL_NAME are Special_4, Special_5 and Special_6 are not loaded to this table because of the invalid parent. Error Table CHANNELS_OM_ERR SQL> select rownum, class_source_id, channel_id, channel_name,channel_source_id, err$$$_error_reason from channels_om_err order by channel_name; We can see all the record with null parent or invalid parent are inserted into this error table. Error reason is “Default parent used for record” for the first three records, and “No parent found for record” for the last three. Table SALES_OM SQL> select a.*, b.channel_name from sales_om a, channels_om b where a.channels=b.channel_id; We can see the order record with null channel_name has been loaded into target table with a default channel_name. The one with “invalid” channel_name are not loaded. Error Table SALES_OM_ERR SQL> select a.amount, a.cost, a.quantity, a.channels, b.channel_name, a.err$$$_error_reason from sales_om_err a, channels_om b where a.channels=b.channel_id(+); We can see the order records with null or invalid channel_name are inserted into error table. If the dimension reference column is null, the error reason is “Default dimension record used for fact”. If it is invalid, the error reason is “Dimension record not found for fact”. Summary In summary, this article illustrated the Orphan Management feature in OWB 11gR2. Automated orphan management policies improve ETL developer and administrator productivity by addressing an important cause of cube and dimension load failures, without requiring developers to explicitly build logic to handle these orphan rows.

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  • When Less is More

    - by aditya.agarkar
    How do you reconcile the fact that while the overall warehouse volume is down you still need more workers in the warehouse to ship all the orders? A WMS customer recently pointed out this seemingly perplexing fact in a customer conference. So what is going on? Didn't we tell you before that for a warehouse the customer is really the "king"? In this case customers are merely responding to a low overall low demand and uncertainty. They do not want to hold down inventory and one of the ways to do that is by decreasing the order size and ordering more frequently. Overall impact to the warehouse? Two words: "More work!!" This is not all. Smaller order sizes also mean challenges from a transportation perspective including a rise in costlier parcel or LTL shipments instead of cheaper TL shipments. Here is a hypothetical scenario where a customer reduces the order size by 10% and increases the order frequency by 10%. As you can see in the following table, the overall volume declines by 1% but the warehouse has to ship roughly 10% more lines. Order Frequency (Line Count)Order Size (Units)Total VolumeChange (%)10010010,000 -110909,900-1% If you want to see how "Less is More" in graphical terms, this is how it appears: Even though the volume is down, there is going to be more work in the warehouse in terms of number of lines shipped. The operators need to pick more discrete orders, pack them into more shipping containers and ship more deliveries. What do you do differently if you are facing this situation?In this case here are some obvious steps to take:Uno: Change your pick methods. If you are used to doing order picks, it needs to go out the door. You need to evaluate batch picking and grouping techniques. Go for cluster picking, go for zone picking, pick and pass...anything that improves your picker productivity. More than anything, cluster picking works like a charm and above all, its simple and very effective. Dos: Are you minimize "touch" points in your pick process? Consider doing one step pick, pack and confirm i.e. pick and pack stuff directly into shipping cartons. Done correctly the container will not require any more "touch" points all the way to the trailer loading. Use cartonization!Tres: Are the being picked from an optimized pick face? Are the items slotted correctly? This needs to be looked into. Consider automated "pull" or "push" replenishment into your pick face and also make sure that high demand items are occupying the golden zones.  Cuatro: Are you tracking labor productivity? If not there needs to be a concerted push for having labor standards in place. Hope you found these ideas useful.

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  • Combination of Operating Mode and Commit Strategy

    - by Kevin Yang
    If you want to populate a source into multiple targets, you may also want to ensure that every row from the source affects all targets uniformly (or separately). Let’s consider the Example Mapping below. If a row from SOURCE causes different changes in multiple targets (TARGET_1, TARGET_2 and TARGET_3), for example, it can be successfully inserted into TARGET_1 and TARGET_3, but failed to be inserted into TARGET_2, and the current Mapping Property TLO (target load order) is “TARGET_1 -> TARGET_2 -> TARGET_3”. What should Oracle Warehouse Builder do, in order to commit the appropriate data to all affected targets at the same time? If it doesn’t behave as you intended, the data could become inaccurate and possibly unusable.                                               Example Mapping In OWB, we can use Mapping Configuration Commit Strategies and Operating Modes together to achieve this kind of requirements. Below we will explore the combination of these two features and how they affect the results in the target tables Before going to the example, let’s review some of the terms we will be using (Details can be found in white paper Oracle® Warehouse Builder Data Modeling, ETL, and Data Quality Guide11g Release 2): Operating Modes: Set-Based Mode: Warehouse Builder generates a single SQL statement that processes all data and performs all operations. Row-Based Mode: Warehouse Builder generates statements that process data row by row. The select statement is in a SQL cursor. All subsequent statements are PL/SQL. Row-Based (Target Only) Mode: Warehouse Builder generates a cursor select statement and attempts to include as many operations as possible in the cursor. For each target, Warehouse Builder inserts each row into the target separately. Commit Strategies: Automatic: Warehouse Builder loads and then automatically commits data based on the mapping design. If the mapping has multiple targets, Warehouse Builder commits and rolls back each target separately and independently of other targets. Use the automatic commit when the consequences of multiple targets being loaded unequally are not great or are irrelevant. Automatic correlated: It is a specialized type of automatic commit that applies to PL/SQL mappings with multiple targets only. Warehouse Builder considers all targets collectively and commits or rolls back data uniformly across all targets. Use the correlated commit when it is important to ensure that every row in the source affects all affected targets uniformly. Manual: select manual commit control for PL/SQL mappings when you want to interject complex business logic, perform validations, or run other mappings before committing data. Combination of the commit strategy and operating mode To understand the effects of each combination of operating mode and commit strategy, I’ll illustrate using the following example Mapping. Firstly we insert 100 rows into the SOURCE table and make sure that the 99th row and 100th row have the same ID value. And then we create a unique key constraint on ID column for TARGET_2 table. So while running the example mapping, OWB tries to load all 100 rows to each of the targets. But the mapping should fail to load the 100th row to TARGET_2, because it will violate the unique key constraint of table TARGET_2. With different combinations of Commit Strategy and Operating Mode, here are the results ¦ Set-based/ Correlated Commit: Configuration of Example mapping:                                                     Result:                                                      What’s happening: A single error anywhere in the mapping triggers the rollback of all data. OWB encounters the error inserting into Target_2, it reports an error for the table and does not load the row. OWB rolls back all the rows inserted into Target_1 and does not attempt to load rows to Target_3. No rows are added to any of the target tables. ¦ Row-based/ Correlated Commit: Configuration of Example mapping:                                                   Result:                                                  What’s happening: OWB evaluates each row separately and loads it to all three targets. Loading continues in this way until OWB encounters an error loading row 100th to Target_2. OWB reports the error and does not load the row. It rolls back the row 100th previously inserted into Target_1 and does not attempt to load row 100 to Target_3. Then, if there are remaining rows, OWB will continue loading them, resuming with loading rows to Target_1. The mapping completes with 99 rows inserted into each target. ¦ Set-based/ Automatic Commit: Configuration of Example mapping: Result: What’s happening: When OWB encounters the error inserting into Target_2, it does not load any rows and reports an error for the table. It does, however, continue to insert rows into Target_3 and does not roll back the rows previously inserted into Target_1. The mapping completes with one error message for Target_2, no rows inserted into Target_2, and 100 rows inserted into Target_1 and Target_3 separately. ¦ Row-based/Automatic Commit: Configuration of Example mapping: Result: What’s happening: OWB evaluates each row separately for loading into the targets. Loading continues in this way until OWB encounters an error loading row 100 to Target_2 and reports the error. OWB does not roll back row 100th from Target_1, does insert it into Target_3. If there are remaining rows, it will continue to load them. The mapping completes with 99 rows inserted into Target_2 and 100 rows inserted into each of the other targets. Note: Automatic Correlated commit is not applicable for row-based (target only). If you design a mapping with the row-based (target only) and correlated commit combination, OWB runs the mapping but does not perform the correlated commit. In set-based mode, correlated commit may impact the size of your rollback segments. Space for rollback segments may be a concern when you merge data (insert/update or update/insert). Correlated commit operates transparently with PL/SQL bulk processing code. The correlated commit strategy is not available for mappings run in any mode that are configured for Partition Exchange Loading or that include a Queue, Match Merge, or Table Function operator. If you want to practice in your own environment, you can follow the steps: 1. Import the MDL file: commit_operating_mode.mdl 2. Fix the location for oracle module ORCL and deploy all tables under it. 3. Insert sample records into SOURCE table, using below plsql code: begin     for i in 1..99     loop         insert into source values(i, 'col_'||i);     end loop;     insert into source values(99, 'col_99'); end; 4. Configure MAPPING_1 to any combinations of operating mode and commit strategy you want to test. And make sure feature TLO of mapping is open. 5. Deploy Mapping “MAPPING_1”. 6. Run the mapping and check the result.

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  • Heterogén adatelérés OWB-vel: ODI EE Enterprise ETL

    - by Fekete Zoltán
    Az elozo ketto blogbejegyzéshez kapcsolódva felmerül a kérdés: Hogyan lehet az Oracle Warehouse Builderrel heterogén adatforrásokat elérni? Ajánlott olvasmány: Oracle Warehouse Builder 11gR2: OWB ETL Using ODI Knowledge Modules Természetesen az OWB az Oracle Database Heterogeneous Services-zel ODBC-vel illetve Oracle Gateway-k alkalmazásával eddig is lehetett mindenféle ODBC kompatibilis továbbá mainframe-es adatbázisokat elérni. Oracle Database Gateways: MS SQL Server, Sybase, Teradata, Informix, ODBC, DRDA, APPC, WebSphere MQ, DB2, DB2/400. A megfelelo Application Adapters megvásárlásával lehet csatlakozni az OWB-vel például a következo forrásokhoz: SAP, Oracle E-Business Suite, Peoplesoft, Siebel, Oracle Customer Data Hub (CDH), Universal Customer Master (UCM), Product Information Management (PIM). Az OWB 11gR2-tol kezdve az OWB tudja használni az Oracle Data Integrator Knowledge moduljait a heterogén adatelérésre, ez JDBC-vel illetve más heterogén elérési módokkal. Ajánlott olvasmány: Oracle Warehouse Builder 11gR2: OWB ETL Using ODI Knowledge Modules Letöltés: Oracle Warehouse Builder. BTW az OWB Java-s kliens szoftver Linux-on és Windows-on is használható. A szerver oldal pedig természetesen az Oracle adatbázisban fut: Solaris, Linux, HP-UX, AIX, Windows operációs rendszereken.

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