Friday Jun 21, 2013

What Comes Next After You Decide on Using Oracle Exadata

As Oracle Exadata continues to expand its footprint for both transaction and analytical processing, moving existing systems to Exadata and feeding it with enterprise data on an ongoing basis have become important discussion topics for Exadata customers. Consolidation and migration is the first step of this powerful journey with Exadata, and I'd like to start there in today's blog post.   

The systems that benefit from Exadata's extreme performance and reliability are typically business-critical systems that carry major risks when it comes to migration. Any downtime or data loss can have significant impact to the business in terms of revenue generation, customer loyalty, and productivity. As Oracle GoldenGate user community knows well, GoldenGate's heteregenous, real-time, and bidirectional replication capabilities enable very strong zero downtime migration and consolidation solutions for major databases and platforms including Oracle, IBM DB2 (zOS, iSeries, and LUW), HP NonStop, SQL Server, Sybase ASE, MySQL, and Teradata.

We discussed GoldenGate's zero downtime migration to Exadata offering and best practices with our customer IQNavigator in a webcast that is now available on demand:

Zero-Downtime Migration to Oracle Exadata Using Oracle GoldenGate: A Customer Case Study

If you have not watched it, I highly recommend listening to the discussion, as it clearly explains there should be no concerns around causing business interruption when moving to Oracle Exadata using GoldenGate.  GoldenGate's failback option to the old environment is a great tool for minimizing risk and many organizations adopt that approach for their business-critical systems.  

In addition to migration to Oracle Exadata, customers use GoldenGate, and Oracle Data Integrator, with Exadata in a variety ways leveraging the natural fit between these technologies:

  • Active-active database synchronization across the globe for data distribution, continuous availability, and zero downtime maintenance purposes.
  • Real-time or near real-time data loading to data warehouse, or consolidated database, on Oracle Exadata from heterogeneous sources. Oracle Data Integrator plays the major role in this use case as it integrates with GoldenGate and loads data warehouse in near real-time after performing transformations within the Exadata machine. This use case will be another blog topic soon as it is a strong best practice for performing ETL/ E-LT for Exadata.
  • Moving change data from an OLTP application running on Exadata in real time, for downstream consumption by other systems including supporting service integration.

As additional resources on best practices for migrating to Exadata I'd like to point you to couple of great white papers: Zero-Downtime Migration to Oracle Exadata Using Oracle GoldenGate and Oracle GoldenGate on Exadata Database Machine.

Wednesday Oct 17, 2012

And the Winners of Fusion Middleware Innovation Awards in Data Integration are…

At OpenWorld, we announced the winners of Fusion Middleware Innovation Awards 2012. Raymond James and Morrison Supermarkets were selected for the data integration category for their innovative use of Oracle’s data integration products and the great results they have achieved.

In this blog I would like to briefly introduce you to these award winning projects.

Raymond James is a diversified financial services company, which provides financial planning, wealth management, investment banking, and asset management. They are using Oracle GoldenGate and Oracle Data Integrator to feed their operational data store (ODS), which supports application services across the enterprise. A major requirement for their project was low data latency, as key decisions are made based on the data in the ODS. They were able to fulfill this requirement due to the Oracle Data Integrator’s integrated solution with Oracle GoldenGate.

Oracle GoldenGate captures changed data from different systems including Oracle Database, HP NonStop and Microsoft SQL Server into a single data store on SQL Server 2008. Oracle Data Integrator provides data transformations for the ODS. Leveraging ODI’s integration with GoldenGate, Raymond James now sees a 9 second median latency (from source commit to ODS target commit).

The ODS solution delivers high quality, accurate data for consuming applications such as Raymond James’ next generation client and portfolio management systems as well as real-time operational reporting. It enables timely information for making better decisions.

There are more benefits Raymond James achieved with this implementation of Oracle’s data integration solution. The software developers and architects of this solution, Tim Garrod and Ryan Fonnett, have told us during their presentation at OpenWorld that they also reduced application complexity significantly while improving developer productivity through trusted operational services. They were able to utilize CDC to generate alerts for business users, and for applications (for example for cache hydration mechanisms).

One cool innovation example among many in this project is that using ODI's flexible architecture, Tim and Ryan could build 24/7 self-healing processes. And these processes have hardly failed. Integration process fixes the errors itself. Pretty amazing; and a great solution for environments that need such reliability and availability. (You can see Tim and Ryan’s photo with the Innovation Awards trophy above.)

The other winner of this year in the data integration category, Morrison Supermarkets, is the UK’s 4th largest grocery retailer. The company has been migrating all their legacy applications on to a new-world application set based on Oracle and consolidating all BI on to a single Oracle platform. The company recently implemented Oracle Exadata as the data warehouse engine and uses Oracle Business Intelligence EE.

Their goal with deploying GoldenGate and ODI was to provide BI data to the enterprise in a way that it also supports operational decision making requirements from a wide range of Oracle based ERP applications such as E-Business Suite, PeopleSoft, Oracle Retail Suite. They use GoldenGate’s log-based change data capture capabilities and Oracle Data Integrator to populate the Oracle Retail Data Model. The electronic point of sale (EPOS) integration solution they built processes over 80 million transactions/day at busy periods in near real time (15 mins). It provides valuable insight to Retail and Commercial teams for both intra-day and historical trend analysis.

As I mentioned in yesterday’s blog, the right data integration platform can transform the business. Here is another example: The point-of-sale integration enabled the grocery chain to optimize its stock management, leading to another award: Morrisons won the Grocer 33 award in 2012 - beating all other major UK supermarkets in product availability. Congratulations, Morrisons,on another award!

Celebrating the innovation and the success of our customers with Oracle’s data integration products was definitely a highlight of Oracle OpenWorld for me. I look forward to hearing more from Raymond James, Morrisons, and the other customers that presented their data integration projects at OpenWorld, on how they are creating more value for their organizations.

Monday Oct 08, 2012

The most challenging part of blogging about OpenWorld is…

...not knowing where to start. Do I talk about the great presentations from our partners and executives in our keynote sessions; do I write about the music festival, or many great sessions we had in the Data integration track? A short blog can never do justice. For now I will stick to our data integration sessions for those who could not attend with so many other sessions running concurrently. And in the coming weeks we will be writing more about what we talked in our sessions and what we learned from our customers and partners.

For today, I will give some of the key highlights from Data Integration sessions that took place on Wednesday and Thursday of last week  On Wednesday, GoldenGate was highlighted in multiple Database and Data Integration sessions. I found particularly the session about Oracle’s own use of GoldenGate for its large E-Business Suite implementation for supply chain management and service contract management very interesting. In 2011, Oracle implemented a new operational reporting system using GoldenGate real-time data replication to an operational data store that leverages data from E-Business Suite.The results are very impressive. Mark Field, VP of Applications Development in the IT organization shared with us that data freshness improved by 2,210X while report run performance improved by 60X. For more information on this implementation and its results please see the white paper: Real-Time Operational Reporting for E-Business Suite via GoldenGate Replication to an Operational Data Store

Other sessions that provided very rich content were: "Best Practices for Conflict Detection and Resolution in Oracle GoldenGate for Active/Active", "Tuning and Troubleshooting Oracle GoldenGate on Oracle Database", "Next-Generation Data Integration on Oracle Exadata" and "Accelerate Oracle Data Integrator with Advanced Features, SOA, Groovy, SDK, and XML". Below is a slide presented by Stephan Haisley in the Tuning and Troubleshooting Oracle GoldenGate session. If you missed them during OpenWorld, I highly recommend downloading the slides. We will continue to blog about these topics and related resources.


Wednesday Sep 26, 2012

Data-Driven SOA with Oracle Data Integrator

By Mike Eisterer,

Data integration is more than simply moving data in bulk or in real-time, it is also about unifying information for improved business agility and integrating it in today’s service-oriented architectures. SOA enables organizations to easily define services which may then be discovered and leveraged by varying consumers. These consumers may be applications, customer facing portals, or complex business rules which are assembling services to automate process. Data as a foundational service provider is a key component of today’s successful SOA implementations.

Oracle offers the broadest and most integrated portfolio of products to help you define, organize, orchestrate and consume data services.

If you are attending Oracle OpenWorld next week, you will have ample opportunity to see the latest Oracle Data Integrator live in action and work with it yourself in two offered Hands-on Labs. Visit the hands-on lab to gain experience firsthand:



Oracle Data Integrator and Oracle SOA Suite: Hands-on- Lab (HOL10480)

Wed Oct 3rd 11:45AM Marriott Marquis- Salon 1/2


To learn more about Oracle Data Integrator, please visit our Introduction Hands-on LAB:

Introduction to Oracle Data Integrator (HOL10481)

Mon Oct 1st 3:15PM, Marriott Marquis- Salon 1/2

If you are not able to attend OpenWorld, please check out our latest resources for Data Integration.

Tuesday Sep 11, 2012

Oracle Data Integrator at Oracle OpenWorld 2012: Sessions, Demos and Hands-On Labs

 By Mike Eisterer

 Oracle OpenWorld is just a few weeks away and the Oracle Data Integrator team would like to introduce you to the sessions, demos and hands-on labs we will be offering this year. We will be out in force at the show with four demo pods and two hands-on labs, plus numerous speaking sessions.

Sessions this year will provide valuable information towards the use and direction of Oracle Data Integration solutions, including:

  • Tackling Big Data Analytics with Oracle Data Integrator, October 1, 2012 - 12:15 PM, at Moscone West – 3005
  • Real-Time Data Integration with Oracle Data Integrator, October 1, 2012 - 4:45 PM, at Raymond James Moscone West – 3005
  • Future Strategy, Direction, and Roadmap of Oracle’s Data Integration Platform, October 1, 2012 - 10:45 AM, at Moscone West – 3005
  • Customer Perspectives: Oracle Data Integrator Marriott Marquis, October 3, 2012 - 1:15 PM, at Marriot Marquis GoldenGate C3
 To see the full list of sessions for data integration topic please check out our Focus-on for Data Integration.

Demos this year will be running Monday through Wednesday in Moscone South and we will be showcasing:

· Oracle Data Integrator for Big Data (Moscone South, S-236)

· Oracle Data Integrator for Enterprise Data Warehousing (Moscone South, S-238)

· Oracle Data Integrator and Service Integration (Moscone South, S-235)

· Oracle Data Integrator and Oracle GoldenGate for Oracle Applications (Moscone South, S-240)

Hands-on labs will feature instructor lead exercises providing direct experience with Oracle Data Integrator, including:

· “Introduction to Oracle Data Integrator” where students will learn how to define sources and create mappings to extract, load and transform data.

· “Oracle Data Integrator and Oracle SOA Suite” where students will define integration flows as web services, access web services as a transformation and integrate ODI sessions into a BPEL process.

If you are not able to attend OpenWorld, please check out our latest resources for Data Integration.

In the coming weeks you will see more blogs about our products’ new capabilities and what to expect at OpenWorld.

 I hope to see you at OpenWorld and stay in touch via our future blogs.

Friday Jun 01, 2012

Looking for the latest Data in Integration Reading? Read on..

Looking for some exciting reading on data integration?  With the hundreds of books in the market on data integration, there really isn't a book that is heads-down, focused on Oracle Data Integrator.  Not any more.  Recently published is the first book truly dedicated to Oracle Data Integrator.  The title of the book, which is published by Packt Publishing is:

Getting Started with Oracle Data Integrator 11g – A Hands-On Tutorial
Authors: Peter C. Boyd-Bowman, Christophe Dupupet, Denis Gray, David Hecksel,  Julien Testut, Bernard Wheeler

I would like to extend my hearty Congratulations to everyone who contributed to this book!

You can get more information about 'Getting Started with Oracle Data Integrator 11g – A Hands-On Tutorial' including the table of contents and a sample chapter at

The book is now available on,, Barnes & Noble and Safari Books Online. Order your copy today!

Monday Apr 02, 2012

ODI SDK: Retrieving Information From the Logs

sample code that illustrates using the ODI SDK to retrieve execution details from the logs stored in the repository.[Read More]

Friday Jun 03, 2011

Oracle Data Integrator - Key to Success on Data Integration Projects

Data is the lifeline of any enterprise.  Now, figuring out how to integrate that data so that it is available to you in the systems that require it -- well, that's a different story.  Data integration projects are notoriously known for being challenging due to:  multiple siloed systems, plethora of home-grown systems and interfaces that were created eons ago, mismatched data formats, different souce and target database and this is just a few of the issues.

Oracle Data Integrator was designed to to help tackle these issues and help maximize success on data integration projects. Built and designed using an ELT architecture, ODI is the most complete solution for bulk data movement and data transformation.  High performance, heterogeneity and developer productivity, reduced cost and faster time-to-value are just a sampling of what ODI has to offer.  Take a look at the latest whitepaper on what makes data integration projects successful and how Oracle Data Integrator is integral to helping organizations trust that their data is where it needs to be while saving time, money and extending their existing IT investments. Read here

Friday May 20, 2011

Real-Time Data Integration for Operational Data Warehousing

Gone are the days when data warehouses were just for reporting, strategic analytics, and forecasting. Today, more companies are using their data warehouses for operational decision making – and thus more critical to the business. To be able to influence operational decisions the analytical environment needs to be able to stay current with the business events happening right now. Therefore an important requirement is to enable lowest possible latency in which new data is delivered to the data warehouse, ideally in real time.

For example, when a snowstorm hits a certain area, the operational data warehouse can help monitor snow shovel sales and then provide information to help determine whether other stores in the affected areas should move more merchandise to the shelves and also offer other related products at a discounted price. There is a lot less value in reacting to data for a snowstorm that occurred 24 hours ago – or even 6 hours ago.

There are many data integration technologies that serve the data acquisition needs of a data warehouse, however only a few offer real-time data delivery with no impact on source systems’ performance. The challenge for the IT group is to determine what solution or combination of data integration solutions will meet their data delivery and performance needs to help propel the move to operational data warehousing. Such data integration evaluations are aided with the understanding of:

  • Selection Criteria – This should include considerations for acceptable latency, data quantity/volumes, data integrity, transformation requirements, and processing overhead/impact on availability.
  • “Right Time” vs. "Real-Time" – When evaluating solutions, the technology should deliver real-time data capabilities and let the user choose the “right time” as a business decision. Right-time should be a component of decision latency – a user preference, not a technical constraint.
  • Transformations – As the data warehouse approaches real time, transformations ideally should take place within the data warehouse in order to reduce data and analysis latency. This eliminates the need for additional steps for aggregating changed data on a middle-tier server until it is batch processed-- not to mention the TCO savings of not acquiring or maintaining a middle-tier transformation server.

Oracle offers a complete and certified data integration solution for implementing operational data warehouse on Oracle Exadata. Oracle GoldenGate provides low-impact, real-time change data capture and delivery, while Oracle Data Integrator EE provides high performance transformations within Oracle Exadata. Oracle Data Integrator also offers integrated solution for data profiling and data quality to enable analysis with trusted data. Here is a great customer example how Oracle’s products enable the move to operational data warehousing. You can read more about Oracle's data integration solution for operational data warehousing in our white paper. If you would like to read more about how to use Oracle Data Integration products for Oracle Exadata please check out our recent data sheet.

Friday Nov 20, 2009

Parallel Processing in ODI

This post assumes that you have some level of familiarity with ODI. The concepts of Packages, Interfaces, Procedures and Scenarios are used here assuming that you understand them in the context of ODI. If you need more details on these elements, please refer to the ODI Tutorial for a quick introduction, or to the complete ODI documentation for detailed information.

ODI: Parallel Processing

A common question in ODI is how to run processes in parallel. When you look at a typical ODI package, all steps are described in a serial fashion and will be executed in sequence.


However, this same package can parallelize and synchronize processes if needed.


The first piece of the puzzle if you want to parallelize your executions is that a package can invoke other packages once they have been compiled into scenarios (the process of generation of scenarios is described later in this post). You can then have a master package that will orchestrate other scenarios. There is no limit as to how many levels of nesting you will have, as long as your processes are making sense: Your master package invokes a seconday package which, in turn invokes another package...

When you invoke these scenarios, you have two possible execution modes: synchronous and asynchronous.


A synchronous execution will serialize the scenario execution with other steps in the package: ODI executes the scenario, and only after its execution is completed, runs the next step.

An asynchronous execution will only invoke the scenario but will immediately execute the next step in the calling package: the scenario will then run in parallel with the next step. You can use this option to start multiple scenarios concurrently: they will all run in parallel, independently of one another.


Once we have started multiple processes in parallel, a common requirement is to synchronize these processes: some steps may run in parallel, but at times we will need all separate threads to be completed before we proceed with a final series of steps. ODI provides a tool for this: OdiWaitForChildSession.


An interesting feature is that as you start your different processes in parallel, they can each be assigned a keyword (this is just one of the parameters you can set when you start a scenario). When you synchronize the processes, you can select which processes will be synchronized based on a selection of keywords.


To add a scenario to your package, simply drag and drop the generated scenario in the package, and edit the execution parameters as needed. In particular, remember to set the execution mode to Asynchronous.

You can generate a scenario from a package, from an interface, or from a procedure. The last two will be more atomic (one interface or one procedure only per execution unit). The typical way to generate a scenario is to right-click on one of these objects and to select Generate Scenario.

The generation of scenarios can also be automated with ODI processes that would invoke the ODI tool OdiGenerateAllScen. The parameters of this tool will let you define which scenarios are being generated automatically.

In all cases, scenarios can be found in the object tree, under the object they were generated from - or in the Operator interface, in the Scenarios tab.

While you are developing your different objects, keep in mind that you can Regenerate existing scenarios. This is faster than deleting existing ones only to re-create them with the same version number. To re-generate a scenario, simply right-click on the existing version and select Regenerate ... .

From an execution perspective, you can specify that the scenario you will execute is version -1 (negative one) to ensure that the latest version number is always the one executed. This is a lot easier than editing the parameters with each new release.


You will notice that as of, ODI does not graphically differentiate between serialized and parallelized executions: all are represented in a serial manner. One way to make parallel executions more visible is stack up the objects vertically, versus the more natural horizontal execution for serialized objects. (If we have electricians reading this, the layout will be very familiar to them, but this is only a coincidence...)



Scenarios are not the only objects that will allow for parallel (or Asynchronous) execution. If you look at the ODI tool OdiOSCommand, you will notice a Synchronous option that will allow you to define if the external component you are executing will run in parallel with the current process, or if it will be serialized in your process. The same is true for the Data Quality tool OdiDataQuality.


As you will start running more processes in parallel, be ready to see more processes being executed concurrently in the Operator interface. If you are only interested in seing the master processes though, the Hierarchy tab will allow you to limit your view to parent processes. Children processes will be listed under the entry Childres Sessions under each session.

Likewise, when you access the logs from the web front end, you can view the Parent processes only.


Screenshots were taken using version of ODI. Actual icons and graphical representations may vary with other versions of ODI.

Monday Nov 09, 2009

The Benefits of ODI Knowledge Modules: a Template Approach for Better Data Integration

This post assumes that you have some level of familiarity with ODI. The concepts of Knowledge Module are used here assuming that you understand them in the context of ODI. If you need more details on these elements, please refer to the ODI Tutorial for a quick introduction, or to the complete ODI documentation for detailed information..

At the core, ODI knowledge modules are templates of code that will be leveraged for data integration tasks: they pre-define data integration steps that are required to extract, load, stage - if needed - and integrate data.

Several types of Knowledge Modules are available, and are grouped in families for Loading operations (LKMs), Integration operations (IKMs), Data Control operations (CKMs), and more.

For a more detailed description of what a knowledge module is, simply picture the multiple steps required to load data from a flat file into a database. You can connect to the file using JDBC, or leverage the database native loading utility (sqlldr for Oracle, bcp for SQL Server or Sybase, load for db2, etc.). External tables are another alternative for databases that support this feature.
As you use one or the other technique, you may first want to stage the data before loading your actual target table; in other cases, staging will only slow down your data load.

As far as the integration in the target system is concerned, again multiple strategies are available: simple inserts, inserts and updates, upserts, slowly changing dimension... these techniques may be as simple as one step, or be a complex series of commands that must be issued to your database for proper execution.

The Knowledge Modules will basically list these steps so that a developer who needs to repeat the same integration pattern only has to select the appropriate templates, versus re-developing the same logic over and over again.

The immediate benefits of this approach are well known and well documented:
- All developers use the same approach, and code development is consistent across the company, hence guarantying the quality of the code
- Productivity is greatly improved, as proven path are re-used versus being re-developed
- Code improvement and modification can be centralized and has a much broader impact: optimization and regulatory changes are done once and inherited by all processes
- Maintenance is greatly simplified

To fully appreciate all the benefits of using knowledge Modules, there is a lot more that needs to be exposed and understood about the technology. This post is a modest attempt at addressing this need.


Most tools today will offer the ability to generate SQL code (or some other type of code, such as scripts) on your source or target system. As most products come with a transformation engine, they will also generate proprietary code for this engine where data is staged (I'll skip the debate here as to whether a transformation engine is a staging area or not - the point being that code can be generated on either source, "middle-tier" or target).

However, real life requirements are rarely either/or. Often times, it makes sense to leverage all systems to optimize the processing: spread out the load for the transformations, reduce the amount of data to be transferred over the network, process the data where it is versus moving the data around solely for the purpose of transformations.

To achieve this, Data Integration tools must be able to distribute the transformation logic across the different systems.


Only ODI will effectively generate code and transformations on all systems. This feature is only possible thanks to the KM technology.

Beyond the ability to generate code, you have to make sure that the generated code is the best possible code for the selected technology. Too often, tools first generate code that is then translated for the appropriate database. With the KMs technology, no translation is required: the generated code was initially conceived explicitly for a given technology, hence taking advantage of all the specifics of this technology.

And since the KMs are technology specific, there is no limit to what can be leveraged on the databases, including user defined functions or stored procedures.



Whenever a tool generates code, the most common complaint is that there is very little (if any) control over the generated result. What if a simple modification of the code could provide dramatic performance improvements? Basic examples would include index management, statistics generation, joins management, and a lot more.

The KM technology is open and expansible so that developers have complete control over the code generation process. Beyond the ability to optimize the code, they can extend their solution to define and enforce in house best practices, and comply with corporate, industry or regulatory requirements. KMs Modifications are done directly from the developers graphical interface.

One point that can easily be adapted is whether data have to be materialized throughout the integration process. Some out-of-the-box KMs will explicitly land data in a physical file or tables. Others will avoid I/Os by leveraging pipes instead of files, views and synonyms instead of tables. Again, developers can adapt the behavior to their actual requirements.


How much time does it take to adapt your code to a new release of your database? How much time does it take to add a new technology altogether? In both cases, KMs will provide a quick and easy answer.

Let us start with the case of a new version of the database. While our engineering teams will release new KMs as quickly as possible to take advantage of the latest releases of any new database, you do not have to wait for them. A new release typically means new parameters for your DDL and DML, as well as new functions for your existing transformations. Adapt the existing KMs with the features you need, and in minutes your code is ready to leverage the latest and greatest of your database.

Likewise, if you ever need to define a new technology that would not be listed by ODI (in spite of the already extensive list we provide), simply define the behavior of this technology in the Topology interface, and design technology specific KMs to take advantage of the specific features of this database. I can guaranty you that 80% of the code you need (at least!) is already available in an existing KM... Thus dramatically reducing the amount of effort required to generate code for your own technology.


I am a strong advocate of the customization of KMs: I like to get the best I can out of what I am given. But often times, good enough is more than enough. I will always remember trying to optimize performance for a customer: we did not know initially what our processing window would be - other than "give us your best possible performance". The first out-of-the-box KM we tried processed the required 30,000,000 records in 20 minutes. Due to IT limitations, we could only leverage lesser systems for faster KMs... but still reduced performance to 6 minutes for the same volume of data. We started modifying KMs to get even better results, when the customer admitted that we actually had 3 hour for the process to complete... At this point, spending time in KM modifications was clearly not needed anymore.

KMs are meant to give the best possible performance out of the box. But every environment is unique, and assuming that we can have the best possible code for you before knowing your own specific challenges would be an illusion - hence the ability to push the product and the code to the limit

Another common question is: do you have to leverage both source and target systems as part of your transformations? Clearly, the answer is no. But in most cases, it is crucial to have the flexibility to leverage all systems, versus being cornered in using only one of them. Over time, you will want to reduce the volume of data transferred over the network; you will want to distribute some of your processing... all more reasons to leverage all available engines in your environment.

Do not hesitate and share with us how you extend your KMs!

Screenshots were taken using version of ODI. Actual icons and graphical representations may vary with other versions of ODI.


Friday Oct 09, 2009

Did You Know that ODI Automatically Summarizes Data Errors?

Looking for Data Integration at OpenWorld 2009? Click here!

This post assumes that you have some level of familiarity with ODI. The concepts of Interface, Flow and Static Control, as well as Knowledge Module are used here assuming that you understand them in the context of ODI. If you need more details on these elements, please refer to the ODI Tutorial for a quick introduction, or to the complete ODI documentation for detailed information..


If you take advantage of either Flow Control or Static Control in your interfaces, you know that ODI will automatically trap errors for you as you run your interfaces.

When you select the Controls tab of your interface, where you will decide which Knowledge Module will be used to identify the errors, you have an option to drop the Error table and another one to drop the Check table. Have you ever wondered what these are?

Interface Controls Tab

The Error table is the table that will be created by ODI to store all errors trapped by the FLOW_CONTROL and STATIC_CONTROL of your interface. You have probably already used the error table. This table is structured after your target table, along with administrative information needed to re-cycle or re-process the invalid records. It is loaded by ODI with all records that fail to pass the validation of the rules defined on your Target table. This feature is often referred to as a Data Quality Firewall as only the "good" data will make it to the target table.

Once all errors have been identified for a given interface, ODI will summarize them into another table: the Check table. There will be only one such table per data server: all target tables in the server (irrespectively of their schema) will share the same summary table. The name of this table is defined by default by the CKMs as SNP_CHECK_TAB.


You will find the check table in the default work schema of your server. To locate this schema, you have to go back to topology, in the Physical Architecture tab. Expand your data server to list the different physical schemas. One of the schemas is your default schema and will be identified by a checkmark on the schema icon (see SALES_DWH in the example below).

Default Schema

When you edit the schema, it has an associated work schema. The work schema associated to your default schema is your default work schema: ODI_TMP is the following example.

Default Work Schema


Note that you can change your default schema by selecting/unselecting the default option in the schema definition. But remember that you will always need exactly one default schema for each server.


Now that we know where to find this table, let's look at its structure:

  • CATALOG_NAME, SCHEMA_NAME: location of the table that was being loaded (i.e. the target table)
  • RESOURCE_NAME, FULL_RES_NAME: name of the table that was being loaded
  • ERR_TYPE: type of control that was performed (Flow Control or Static Control)
  • ERR_MESS: plain English error message associated with the error
  • CHECK_DATE: date and time of the control
  • ORIGIN: name of the ODI process that identified the errors
  • CONS_NAME: name of the constraint (as defined in the ODI Models) that defines the rule that the record violated
  • CONS_TYPE: type of error (duplicate primary key, invalid reference, conditional check failed, Null Value)
  • ERR_COUNT: number of records identified by the process that failed to pass that specific control rule.



A sample of the data available in that summary table is show below (we split the content in 2 screenshots to make this more readable - this is one and only one table):

Errors Summary Data

Errors Summary Data2

There are many possible uses for this table: decision making in your ODI processes based on the number of errors identified or the type of errors identified, basic reporting on errors trapped by ODI, trend analysis or the evolution of errors over time...

Do not hesitate and share with us how you leverage this table!

Screenshots were taken using version of ODI. Actual icons and graphical representations may vary with other versions of ODI.


Thursday Oct 01, 2009

Creating a New Knowledge Module for Sample Data Sets Generation

Looking for Data Integration at OpenWorld 2009? Click here!

The posts in this series assume that you have some level of familiarity with ODI. The concepts of Interface and Knowledge Module are used here assuming that you understand them in the context of ODI. If you need more details on these elements, please refer to the ODI Tutorial for a quick introduction, or to the complete ODI documentation for detailed information..
In particular, to learn more on Knowledge Modules, I strongly recommend the Knowledge Module Developer's Guide - Fundamentals that comes with the product. You will have to download and install ODI to access this document in the Documentation Library.

This post will look into "when" and "how" to create a knowledge module. Then it will walk through some of the choices that can be made when designing a Knowledge Module.

To illustrate the descriptions, we are working on an example described previously in this post.


The first element to look into is what parts of the logic of your code are reusable. What you are typically looking for are the following:

  • Sequences of steps that are repeated commonly, even though some steps may be optional. For instance: creation of a staging table, creation of a script or parameter file for a utility, invoking an external program, extraction of data from a database, etc.
  • For each step, identification of the non variable parts vs. the variable parts. For instance, in a select statement, the body of the code remains the same. In the following example, the elements in brackets are variables, the others are fixed:
    • Insert into [TableName] ([ListOfColumns]) select ([ListOfColumns and Expressions]) from [List of Tables] where [conditions]

  • For your module to be re-usable, you want to make sure that no information that physically relates your code to any system or table structure is left out of the code. The idea behind the KMs is that as developers will build their transformations and mappings, ODI will "fill in the blanks" with the appropriate data.

The easiest way to get started with a knowledge module is actually to take an existing one and modify it. As the syntax has already been validated in existing KMs, the amount of work required to produce valid code will be greatly reduced.
In most cases, column names, mapping expressions do not belong to a knowledge module. The exception would be administrative columns that you add as part of the logic of your KM. For instance, most Incremental Update knowledge modules that ship with ODI create an IND_UPDATE column to differentiate records that will be updated from those that will be inserted. These columns definitely belong in the code of the KM.


Likewise, you may want to create your own tables (administrative tables, audit tables, etc.) with a very static name. These can be created by the Knowledge Module. But in general, it is better to dynamically generate the table name after the table being loaded, to prevent multiple processes running in parallel from trying to use the same intermediate table.


Any technique used to extract data out of a database (or file, or messaging system, or web service for that matter) can be a good opportunity to create a new KM. The same is true for loading techniques and integration techniques: inserts, updates, slowly changing dimension, etc.

In the scenario that we are contemplating, we want to insert data (albeit random data) into a table, so we probably have a good case for a knowledge module.

The first step is usually to look for available techniques, try the code independently of any knowledge module, and check out how it behaves: how is performance? How does the code behave when data volume grows? You want to make sure that the code you will integrate as a template is as good as it can be before you share it with the entire corporation!

Typically, extracting from a source system to stage data is done in an LKM. Loading data into a target table is done with an IKM. In our case, we will clearly create an IKM.


For our example, will start with a KM that works exclusively for Oracle Databases. Adaptations of the code will be possible later on to make similar processes run on other databases.

The Oracle database provides a fantastic feature that we can leverage to generate a large number of records: group by cube: it returns all the possible permutation for the selected columns. So the following code:

select NULL from dual group by cube(1,1,1)

returns 8 records (2 to the power 3). Add columns for the list for the permutations, and you are adding an exponential number of records.

Now when I played with this function on my (very) little installation of the database, I seemed to hit a limit for (very) large permutation numbers. I have to admit that I am not using the function in its expected fashion, no I cannot really complain. But at least I can easily generate 1024 records (2 to the power 10). Now from a usability perspective, I do not really want to use that number for the users of my KM (1024 has a geeky flavor to it, doesn't it?). How about generating a table with just 1,000 records?

The following code will do the trick:

select NULL from dual group by cube(1,1,1,1,1,1,1,1,1,1)
where rownum<=1000

Note that so far, all the instructions we have are hard-coded. We still do not have anything that would be dynamic in nature.

Now we need to use the above query to create some table with our 1,000 records. Again, we can hard-code the table name - but this does not make for very portable code. In particular, from one environment to the next, the database schema names will vary. We have three options to create our staging table, from the least portable to the most portable:

  • Hardcoded table name and schema name: myschema.SEED
  • Dynamic schema name, hardcoded table name: let ODI retrieve the proper schema name and automatically update the code at execution time:  (Generated code: myschema.SEED)
  • Fully dynamic table name and schema name (usually, dynamic tables are named after the target table with some sort of extension): _SEED (generated code: if you are loading TRG_CUSTOMERS, then the SEED table name is myschema.TRG_CUSTOMER_SEED)

Best practice is of course to use the last one of these options to allow for multiple processes to run in parallel. To keep our explanations simple, we will use the second option above - but keep in mind that best practice would be to use the fully dynamic one.


As we will use our KM over and over, it is important to make the developer's life easy. Steps have to be included here to create our seeding table, drop it when we are done, and make sure before we create it that it is not there from a previous run that could have failed.
The typical sequence of steps for a KM creating any type of staging table is:

  • Drop table (and ignore errors - if there is no table, we are fine)
  • Create table (re-create it to reflect any possible meta-data changes)
  • Load the table with staging data - in our case a sequence of numbers that we will be able to leverage later on for filtering... (please be patient: we will come back to this). Here a rownum will do the trick...

Now that we have the code to insert data into our staging table, we can put all the pieces together and have the first three steps of our knowledge module. Keep in mind that you have to be consistent from one step to the next as you name your table. The actual knowledge module with all the matching code is available here (look for KM_IKM Oracle - Build Sample Data - Gen II.xml).



So far our table only has 1,000 records. Not much in terms of volume. But all we need now to create a table with 1,000,000 records... is a Cartesian product (you know, the one thing your mother told you NOT to do with a database? It comes very handy here!):

insert into [Target] ([Columns]) Select * from SEED S1, SEED s2

And if we want to return less records, all we have to do is filter on the S2 table. For instance the clause:

where S2.SEED_ID<=10

will return 10,000 records!. Remember when we stored rownums in this table earlier? This is where it becomes very handy...

So far the only thing we have done is to generate a fairly large number of records. Where the exercise becomes even more interesting is if we can generate data for each record that matches our requirements for sample data. In a previous post we have seen how to generate User Functions in ODI to abstract this type of random generation. The code for the sample data generation typically does not belong to the Knowledge Module as it would not give us enough flexibility for all the possible combinations out there.

The User Function examples used before can generate numerics and strings. We could expand their design to generate random phone numbers, or random social security numbers... and work with random data that will now look like real data, instead of exposing sensitive information.


The fact that User Functions do not belong to the code of the Knowledge Module does not mean that there is no flexibility in the Knowledge Modules. Here, we are building a sample table out of thin air: it is very possible that the table does not exist in the first place. Or if want to run multiple tests with different amounts of records each time, we may want to truncate the table before each new run.

KM options are a very simple way to toggle such behaviors in a KM. We can create 2 options: CREATE_TABLE and TRUNCATE. Then create the appropriate steps in our KM:

  • Create Target Table
  • Truncate table

When you want to associate an option to a given step, edit the step itself; then click on the "option" tab and un-check "always execute". Select only the appropriate option in the list and click OK...



As we define the options, it is also good to think of the most common usage for the KM. In our case, chances are we will often want to create the table and truncate it for successive runs: we can then define that these steps will be executed by default (set the default for the variables to "Yes". More conventional KMs would typically have these options, but their defaults would be set to "No".

We now have a complete Knowledge Module that can be used to generate between 1,000 and 1,000,000 records in any table of your choice, complete with options that will let the users of the KM adapt the behavior to their actual needs...

Again, if you want to review all the code in details, it is available here (look for KM_IKM Oracle - Build Sample Data - Gen II.xml).


Screenshots were taken using version of ODI. Actual icons and graphical representations may vary with other versions of ODI.

Sunday Sep 20, 2009

ODI User Functions: A Case Study

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The posts in this series assume that you have some level of familiarity with ODI. The concepts of Interface and User Function are used here assuming that you understand them in the context of ODI. If you need more details on these elements, please refer to the ODI Tutorial for a quick introduction, or to the complete ODI documentation for detailed information..

This post will give some examples of how and where user functions can be used in ODI. We will look back at a previous post and see why and how user functions were created for this case.


As I was trying to design a simple way to generate random data, I simple approach to specify the type of data to be generated. An example would be to easily generate a random string.

Working on Oracle for this example, I could leverage the database function DBMS_RANDOM.STRING. It takes 2 parameters: one for the type of characters (uppercase, lowercase, mixed case), and on for the length of the string. But I want a little more than this: I also want the ability to force my generation to have a minimum number of characters. For this, I now need to generate a random number. The database function DBMS_RANDOM.VALUE does this, but returns a decimal value. Well, the TRUNC function can take care of this... but my mapping expression becomes somewhat complex:


Imagine now using this formula over and over again in your mappings - not the easiest and most readable portion of code to handle. And maintenance will easily become a nightmare if you simply cut and paste...

A user function makes sense at this point, and will provide the following benefits:

  • A readable name that makes the usage and understanding of the transformation formula a lot easier
  • A central place to maintain the transformation code
  • The ability to share the transformation logic with other developers who do not have to come up with the code for this anymore.


From a code generation perspective, if you use the User Function in your interfaces, ODI will replace the function name with the associated code and send that code to the database. Databases will never see the User Function name - the substitution is part of the code generation.


You need to provide 3 elements when you build a user function:

  • A name (and a group name to organize the functions)
  • A syntax
  • The actual code to be generated when the developers will use the functions


2.1 Naming the User Function

The name of the user function is yours to choose, but make sure that it properly describes what this function is doing: this will make the function all the more usable for others. You will also notice a drop down menu that will let you select a group for this function. If you want to create a new group, you can directly type the group name in the drop down itself.

For our example, we will name the user Function RandomString and create a new group called RandomGenerators.

User Function Name

2.2 The User Function Syntax

The next step will be to define the syntax for the function. Parameters are defined with a starting dollarsign:
and enclosed in parenthesis:
You can name your parameters as you want: these names will be used when you put together the code for the user functions, along with the $ and (). You can have no parameters or as many parameters as you want...

In our case, we need 3 parameters: One for the string format, one for the minimum length of the generated string, one for the maximum length. Our syntax will hence be:

RandomString($(Format), $(MinLen), $(MaxLen))

If you want to force the data types for the parameters, you can do so by adding the appropriate character after the parameter name: s for String, n for Numeric and d for Date. In that case our User Function syntax would be:

RandomString($(Format)s, $(MinLen)n, $(MaxLen)n)

User Function Syntax

2.3 Code of the User Function

The last part in the definition of the user function will be to define the associated SQL code. To do this, click on the Implementation tab and click the Add button. A window will pop up with two parts: the upper part is for the code per se, the bottom part is for the selection of the technologies where this syntax can be used. Whenever you will use the User Function in your mappings, if the underlying technology is one of the ones you have selected, then ODI will substitute the function name with the code you enter here.

For our example, select Oracle in the list of Linked Technologies and type the following code in the upper part:


Note that we are replacing here the column names with the names of the parameters we have defined in the syntax field previously.

User Function Code

Click on the Ok button to save your syntax and on the Ok or Apply button to save your User Function.


Once we are done with the previous step, the user function is ready to be used. One downside with our design so far: it can only be used on one technology. Chances are we will need a more flexibility.

One key feature of the User Functions is that they will give you the opportunity to enter the matching syntax for any other database of your choice. No matter which technology you will use later on, you will only have to provide the name of the user function, irrespectively of the underlying technology.

To add other implementations and technologies, simply go back to the Implementation tab of the User Function, click the Add button. Select the technology for which you are defining the code, and add the code.

Note that you can select multiple technologies for any given code implementation: these technologies will then be listed next to one antother.


To use the User Functions in your interfaces, simply enter them in your mappings, filters, joins and constraints the same way you would use SQL code.

Interface Mappings

When you will execute your interface, the generated code can be reviewed in the Operator interface. Note that you should never see the function names in the generated code. If you do, check out the following elements:

  • User Function names are case sensitive. Make sure that you are using the appropriate combination of uppercase and lowercase characters
  • Make sure that you are using the appropriate number of parameters, and that they have the appropriate type (string, number or date)
  • Make sure that there is a definition for the User Function for the technology in which it is running. This last case may be the easiest one to oversee, so try to keep it in mind!

As long as you see SQL code in place of the User Function name, the substitution happened successfully.



All Screenshots were taken using version of ODI. Actual icons and graphical representations may vary with other versions of ODI.

Sunday Sep 13, 2009

How to Define Multi Record Format Files in ODI

The posts in this series assume that you have some level of familiarity with ODI. The concepts of Datastore, Model and Logical Schema are used here assuming that you understand them in the context of ODI. If you need more details on these elements, please refer to the ODI Tutorial for a quick introduction, or to the complete ODI documentation for more details.

It is not unusual to have to load files containing various record formats. For example a company might store orders and order lines using distinct record formats in the same flat file or you might have one single file containing a header, some records and a footer.
In this post, we'll use the following source file as an example:

1,101,2009/09/01,Computer Parts
2,234,101,Motherboard, Asus P6T,239.99
2,235,101,CPU,Intel Celeron 430,40
1,102,2009/09/02,Computer Parts
2,301,102,CPU,AMD Phenom II X4,170
2,401,103,Inkjet Printer,Canon iP4600,69.99
2,402,103,Inkjet Printer,Epson WF30,39.99
2,403,103,Inkjet Printer,HP Deskjet D2660,49.99

As we can see the Order and Order Lines records have different formats (one has 4 fields, the other has 6 fields), they could also have a different field separator.

Identifying the Record Codes

The first step in order to handle such a file in ODI is to identify a record code, this record code should be unique for a particular record type. In our example the record code will be used by ODI to identify if the record is an Order or an Order Line. All the Order records should have the same record code, this also applies to the Order Lines records.
In our example the first field indicates the record code:
- 1 for Orders records.
- 2 for Order Lines records.

Define the Datastores

We assume that you have already created a Model using a Logical Schema that points to the directory containing your source file.

We will start by defining a datastore for the Order records.

Right-click on the File model and select Insert Datastore.
In the Definition tab, enter a name and specify the flat file resource name in the Resource Name field. 




In the Files tab, specify your flat file settings (delimiter, field separator etc.)

Refer to the ODI documentation for additional information regarding how to define a flat file datastore.



In our example the Order records have 4 fields:

Go to the columns tab and add those 4 columns to your datastore.

Now specify the Record Code in the 'Rec. Code' field of the RECORD_CODE column.




Click OK.



In the Models view, right-click on the datastore and select View Data to display the file content and make sure it is defined correctly. 



The data is filtered based on the record code value, we only see the Order records.


We will now apply the same approach to the Order Lines record.

Right-click on the File model and select Insert Datastore to add a second datastore for the Order Lines record.

In the Definition tab, enter a name and specify the flat file resource name in the Resource Name field. We are pointing this datastore to the same file we used for the Order records. 



In the Files tab, specify your flat file settings (delimiter, field separator etc.).
Refer to the ODI documentation for additional information regarding how to define a flat file datastore.



In our example the Order Lines records have 6 fields:

Go to the columns tab and add those 6 columns to your datastore.

Now specify the Record Code in the 'Rec. Code' field of the RECORD_CODE column.



Click OK.


Right-click on the datastore and select View Data to display the file content and make sure it is defined correctly.



The data is filtered based on the record code value, we only see the Order Lines records.

You can now use those 2 datastores in your interfaces.


All Screenshots were taken using version of ODI. Actual icons and graphical representations may vary with other versions of ODI.


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