The purpose of ETL (Extract, Load, Transform) tools is to help with the consolidation of data that is dispersed throughout the information system. Data is stored in disparate applications, databases, files, operating systems, and in incompatible formats. The consequences of such a dispersal of the information can be dire, for example, different business units operating on different data will show conflicting results and information cannot be shared across different entities of the same business.
Imagine the marketing department reporting on the success of their latest campaign while the finance department complains about its lack of efficiency. Both have numbers to back up their assertions, but the numbers do not match!
What could be worse than a shipping department that struggles to understand customer orders, or a support department that cannot confirm whether a customer is current with his/her payment and should indeed receive support? The examples are endless.
The only way to have a centralized view of the information is to consolidate the data—whether it is in a data warehouse, a series of data marts, or by normalizing the data across applications with master data management (MDM) solutions. ETL tools usually come into play when a large volume of data has to be exchanged (as opposed to Service-Oriented Architecture infrastructures for instance, which would be more transaction based).
In the early days of ETL, databases had very weak transformation functions. Apart from using an insert or a select statement, SQL was a relatively limited language. To perform heavy duty, complex transformations, vendors put together transformation platforms—the ETL tools.
Over time, the SQL language has evolved to include more and more transformation capabilities. You can now go as far as handling hierarchies, manipulating XML formats, using analytical functions, and so on. It is not by chance that 50 percent of the ETL implementations in existence today are done in plain SQL scripts—SQL makes it possible.
This is where the ODI ELT architecture (Extract-Load-Transform—the inversion in the acronym is not a mistake) comes into play. The concept with ELT is that instead of extracting the data from a source, transforming it with a dedicated platform, and then loading into the target database, you will extract from the source, load into the target, then transform into the target database, leveraging SQL for the transformations.
To some extent, ETL and ELT are marketing acronyms. When you look at ODI for instance, it can perform transformations on the source side as well as on the target side. You can also dedicate some database or schema for the staging and transformation of your data, and can have something more similar to an ETL architecture. Similarly, some ETL tools all have the ability to generate SQL code and to push some transformations at the database level.
The ability to dynamically manage a staging area (location, content, automatic management of table alterations)
The ability to generate code on source and target systems alike, in the same transformation
The ability to generate native SQL for any database on the market—most ETL tools will generate code for their own engines, and then translate that code for the databases—hence limiting their generation capacities to their ability to convert proprietary concepts
The ability to generate DML and DDL, and to orchestrate sequences of operations on the heterogeneous systems
In a way, the purpose of an ELT tool is to provide the comfort of a graphical interface with all the functionality of traditional ETL tools, to keep the efficiency of SQL coding with set-based processing of data in the database, and limiting the overhead of moving data from place to place.
In this chapter we will focus on the architecture of Oracle Data Integrator 11g, as well as the key concepts of the product. The topics we will cover are as follows:
The elements of the architecture, namely, the repository, the Studio, the Agents, the Console, and integration into Oracle Enterprise Manager
An introduction to key concepts, namely, Execution Contexts, Knowledge Modules, Models, Interfaces, Packages, Scenarios, and Load Plans
Since ODI is an ELT tool, it requires no other platform than the source and target systems. But there still are ODI components to be deployed: we will see in this section what these components are and where they should be installed.
The components of the ODI architecture are as follows:
Agents: The Agents can be seen as orchestrators for the data movement and transformations. They are very lightweight java components that do not require their own server—we will see in detail where they can be installed.
Console: The Console is a web tool that lets users browse the ODI repository, but it is not a tool used to develop new transformations. It can be used by operators though to review code execution, and start or restart processes as needed.
The Oracle Enterprise Manager plugin for ODI integrates the monitoring of ODI components directly into OEM so that administrators can consolidate the monitoring of all their Oracle products in one single graphical interface.
At a high level, here is how the different components of the architecture interact with one another. The administrators, developers, and operators typically work with the ODI Studio on their machine (operators also have the ability to use the Console for a more lightweight environment). All Studios typically connect to a shared repository where all the metadata is stored. At run time, the ODI Agent receives execution orders (from the Studio, or any external scheduler, or via a Web Service call). At this point it connects to the repository, retrieves the code to execute, adds last minute parameters where needed (elements like connection strings, schema names where the data resides, and so on), and sends the code to the databases for execution. Once the databases have executed the code, the agent updates the repository with the status of the execution (successful or not, along with any related error message) and the relevant statistics (number of rows, time to process, and so on).
Now let's look into the details of each component.
To store all its information, ODI requires a repository. The repository is by default a pair of schemas (called Master and Work repositories) stored in a database. Unless ODI is running in a near real time fashion, continuously generating SQL code for the databases to execute the code, there is no need to dedicate a database for the ODI repository. Most customers leverage existing database installations, even if they create a dedicated tablespace for ODI.
The only element you will never find in the repository is the actual data processed by ODI. The data will be in the source and target systems, and will be moved directly from source to target. This is a key element of the ELT architecture. All other elements that are handled through ODI are stored into the repository. An easy way to remember this is that everything that is visible in the ODI Studio is stored in the repository (except, of course, for the actual data), and everything that is saved in the ODI Studio is actually saved into the repository (again, except for the actual data).
The repository is made of two entities which can be separated into two separate database schemas, namely, the Master repository and the Work repository.
We will look at each one of these in more detail later, but for now you can consider that the Master repository will host sensitive data whereas the Work repository will host project-related data. A limited version of the Work repository can be used in production environments, where the source code is not needed for execution.
The repository is usually installed in an existing database, often in a separate tablespace. Even though ODI is an Oracle product, the repository does not have to be stored in an Oracle database (but who would not use the best database in the world?). Generally speaking, the databases supported for the ODI repository are Oracle, Microsoft SQL Server, IBM/DB2 (LUW and iSeries), Hypersonic SQL, and Sybase ASE. Specific versions and platforms for each database are published by Oracle and are available at:
It is usual to see the repository share the same system as the target database.
We will now look into the specifics of Master and Work repositories.
All the information that pertains to ODI users privileges will be saved here. This information is controlled by administrators through the Security Navigator of the ODI Studio. We will learn more about this navigator when we look into the details of the Studio.
All the information that pertains to connectivity to the different systems (sources and targets), and in particular the requisite usernames and passwords, will be stored here. This information will be managed by administrators through the Topology Navigator.
In addition, whenever a developer creates several versions of the same object, the subsequent versions of the objects are stored in the Master repository. Versioning is typically accessed from the Designer Navigator.
Work repositories will store all the data that is required for the developers to design their data transformations. All the information stored in the Work repository is managed through the Designer Navigator and the Operator Navigator. The Work repository contains the following components:
The Metadata that represents the source and target tables, files, applications, message buses. These will be organized in Models in the Designer Navigator.
The transformation rules and data movement rules. These will be organized in Interfaces in the Designer Navigator.
The workflows designed to orchestrate the transformations and data movement. These are organized in Packages and Load Plans in the Designer Navigator.
The jobs schedules, if the ODI Agent is used as the scheduler for the integration tasks. These can be defined either in the Designer Navigator or in the Operator Navigator.
The logs generated by ODI, where the generated code can be reviewed, along with execution statistics and statuses of the different executions (running, done successfully or in error, queued, and so on). The logs are accessed from the Operator Navigator.
In a production environment, most customers do not need to expose the source code for the processes that are running. Modifications to the processes that run in production will have to go through a testing cycle anyway, so why store the source code where one would never access it? For that purpose, ODI proposes an execution repository that only stores the operational metadata, namely, generated code, execution results, and statistics. The type of Work repository (execution or development) is selected at installation time. A Work repository cannot be converted from development to execution or execution to development—a new installation will be required if a conversion is needed.
We now know that there will be different types of repositories. All enterprise application development teams have more than one environment to consider. The code development itself occurs in a development environment, the validation of the quality of the code is typically done in a test environment, and the production environment itself will have to be separate from these two. Some companies will add additional layers in this lifecycle, with code consolidation (if remote developers have to combine code together), user acceptance (making sure that the code conforms to user expectations), and pre-production (making sure that everything works as expected in an environment that perfectly mimics the production environment).
In all cases, each environment will typically have a dedicated Work repository. The Master repository can be a shared resource as long as no network barrier prevents access from Master to Work repository. If the production environment is behind a firewall for instance, then a dedicated Master repository will be required for the production environment.
The exchange of metadata between repositories can be done in one of the following ways:
Metadata can be exchanged through versioning. All different versions of the objects are uploaded to the Master repository automatically by ODI as they are created. These versions can later be restored to a different Work repository attached to the same Master repository.
All objects can be exported as XML files, and XML files can be used to import the exported objects into the new repository. This will be the only option if a firewall prevents connectivity directly to a central Master repository.
In the graphical representations shown previously, the leftmost repository is obviously our development repository, and the rightmost repository is the production repository. Why are we using an execution for the test environment? There are two rationales for this. They are as follows:
There is no point in having the source code in the test repository, the source code can always be retrieved from the versioning mechanisms.
Testing should not be limited to the validation of the artifacts concocted by the developers; the process of migrating to production should also be validated. By having the same setup for our test and production environments, we ensure that the process of going from a development repository to an execution repository has been validated as well.
People who need to use the Studio usually install the software on their own machine and connect to a shared repository. The only exception would be when the repository is not on the same LAN as the Studio. In that case, most customers use Remote Terminal Service technologies to ensure that the Studio is local to the repository (same LAN). Only the actual display is then sent over the WAN.
The Studio includes four navigators that are typically used by different users who can share the same objects and the same repository. Some users may not have access to some navigators, depending on their security profiles. The navigators are as follows:
Security Navigator: This navigator is typically used by system administrators, security administrators, and DBAs. Through this interface, they can assign roles and privileges to the different users, making sure that they can only view and modify objects that they are allowed to handle.
Topology Navigator: This navigator is usually restricted to DBAs and System administrators. Through this interface, they declare the systems where the data resides (sources, targets, references, and so on), along with the credentials that ODI will use to connect to these systems. Developers and operators will leverage the information stored in the repository, but will not necessarily have the right to modify, or even view that information. They will be provided with a name for the connections and this is all they will need. We will see this in more detail when we address logical schemas.
Designer Navigator: This navigator is used by developers and data custodians alike. Metadata are imported and enriched through this navigator. The metadata is then used to define the transformations in objects called Interfaces. The Interfaces are finally orchestrated in workflows called Packages.
Operator Navigator: This navigator is used by developers and operators. In a development environment, developers will use the Operator views to check on the code generated by ODI, to debug their transformations, and to validate and understand performance of their developments. In a production environment, operators use this same navigator to view which processes are running, to check whether processes are successful or not, and to check on the performance of the processes that are running.
The ODI Agent is the component that will orchestrate all the operations. If SQL code must be executed by a database (source or target), the agent will connect to that database and will send the code (DDL and DML, as needed) for that database to perform the transformations. If utilities must be used as part of the transformations (or, more likely, as part of the data transfer) then the agent will generate whatever configuration files or parameter files are required for the utility, and will invoke this utility with the appropriate parameters—SQL Loader, BCP, Multiload, and NZload are just a small list of such utilities.
There are two types of ODI Agent, namely, the standalone agent (available in all releases of ODI) and the JEE agent (available with ODI 11g and after) that runs on top of WebLogic Server. Each type has its own benefits, and both types of agents can co-exist in the same environment:
A common configuration is to use the JEE agent as a "Master" agent, whose sole purpose it is to distribute execution requests across several child agents. These children can very well be standalone agents. The master agent will know at all times which children are up or down. The master agent will also balance the load across all child agents.
In a pure standalone environment, the Agent is often installed on the target server. Agents are also often installed on file servers, where they can leverage database loading utilities to bulk load data into the target systems. Load balancing can also be done with a standalone master agent. Multiple standalone agents can run on the same server, as long as they each have a dedicated port. This port number is defined in the Topology navigator, where the agent is defined.
Execution from a command line: In this case, ODI will start a dedicated session of the agent, limited to the execution of the process that is passed as a parameter. The script to be used to start a process from a command line is
startscen.baton Windows or
startscen.shon Unix. This script can be found under the
/bindirectory under the agent installation path.
Execution from a web service: ODI 10g offered this feature but required a dedicated setup. ODI 11g offers this feature as part of the agent deployment. All agents support web services to start processes. For a standalone agent, connect to the agent via HTTP to view the associated WSDL. For instance, if the agent is running on server odi_dev on port 20910, the wsdl can be found on this very machine at
ODI Schedules: If ODI processes are scheduled from within ODI (from the Operator navigator or the Designer navigator) then the schedule itself is associated with an agent. Either the schedules will be uploaded to the agent by an administrator, or the agent will refresh its list of schedules when it is restarted.
The Console can be used to browse the repository, but no new developments can be created through this interface.
The Console is useful for viewing lineage and impact analysis without having the full Studio installed on a machine. Operators can also perform most of the tasks they would perform with the Studio, including starting or restarting processes.
The exact information that is available in the Operator Navigator of the Studio will be found in the matching view of the Console: generated code, execution statistics, and statuses of executed processes are all available.
Understanding key concepts in ODI will help developers take advantage of the graphical interface and further improve their productivity. In no specific order, we will now review the notions of Execution Contexts, Knowledge Modules, Models, Interfaces, Packages, and Scenarios.
Everyone encounters the same issue. The parameters used to connect to the development server are different from the parameters used in the QA or production servers and there could be more than these few environments. Some companies add environments such as user acceptance, code consolidation, and pre-production; you name it and it is there! Maintaining the connection parameters is a cumbersome activity. Beyond the maintenance itself, there is a risk if these parameters are modified in the code after the code has been validated by the QA team. Ideally, connection parameters and environment-specific parameters should be maintained independently of the code. This is what ODI provides with the notion of Execution Contexts.
To make sure that developers are independent of the physical location of the different systems, ODI enforces the use of Logical Schemas. Logical Schemas are labels or aliases that represent the connections for the developers. At execution time, the agent will translate these logical names into physical ones based on the information stored in the repository. This way, the maintenance of the connection parameters, location of the databases, and schema names is entirely independent of the code itself.
In addition, whenever an ODI process is executed, a Context must be selected. The structure of the metadata will always be the same from one environment to the next (say for instance, development, QA, and production) but the connection information will be different. By selecting an execution context, the agent will know which connection definition to use when completing the code.
In the previous figure, the logical name Datawarehouse is actually pointing to the physical schema db_dwh in the development server as long as we execute the transformations in the development context. It will automatically point to the dwh schema in the QA server for the QA context.
The physical details are maintained in the Physical Architecture of the Topology Navigator and the logical names that point to the physical implementations are defined in the Logical Architecture of the Topology Navigator. The Context accordion lets administrators define as many contexts as needed. At run time, the selection of a Context will define which physical implementation to use for each logical name used in the code.
The physical architecture will contain the details for ODI to log in to the database, the IP address or name of the server on which the database runs, the port numbers to connect to the database, as well as the name of the actual schemas, catalogs, libraries, and other objects found in these databases.
A Knowledge Module (KM) is a template of code that defines what types of SQL queries (insert, select, and so on) and what scripts need to be generated to complete the data extraction, loading, and transformation operations.
Improve productivity: Integration tasks are very repetitive in nature. Once the extract/load techniques have been defined for a given project, they will be reused over and over again. Consolidating these practices in reusable templates helps improve developers' productivity.
Encapsulate integration best practices: Even if a team agrees on integration best practices, there is usually no guarantee that all developers will implement what has been decided. In other cases, only the best developers will come up with the best integration strategies. By encapsulating the integration strategies in a template, all developers can share the same quality of code.
Allow for further customizations: Oracle is doing its best to encapsulate industry best practices in all KMs no matter what database you are using—but your environment is unique, and at times only you can further optimize the code that will be generated. Again, to make sure that all developers will benefit from the ultimate optimizations, customized KMs will be shared by all developers.
Typically, an ETL developer will be able to use a Knowledge Module without any need to understand the underlying requirements (creation of staging tables, location of these tables, intermediate operations on these tables, cleanup of these tables, and so on).
The developer will control what code will be generated by setting yes/no options to insert records, perform updates, create the target table, and so on.
Loading Knowledge Modules (LKM): These modules determine how to extract and load data across two heterogeneous technologies. Different Knowledge Modules will offer different connection strategies such as JDBC connection, and database load/unload utilities.
Integration Knowledge Modules (IKM): These modules define the strategy used to integrate the data into the target. The different strategies include inserts only, update and inserts, slowly changing dimension, and so on.
Check Knowledge Modules (CKM): These modules implement in-line data quality control before insertion into the target such as validate business rules, control the uniqueness of primary keys, and validate the existence of parent records for foreign keys.
Reverse-engineering Knowledge Modules (RKM): These modules import the metadata for a given technology into the ODI repository. These KMs are usually used for enterprise applications rather than databases. Most reverse-engineering operations will not require any KM since the JDBC drivers leveraged by ODI usually support reverse-engineering operations.
Service Knowledge Modules (SKM): These modules automatically generate the code and expose the database table operations as web services. Insert, update, delete, select, or consumption of data changes can then be performed by using these web services.
Knowledge Modules are named after the specific database for which they have been optimized, the utilities that they leverage, and the technique that they implement. For instance, an IKM Teradata to File (TTU) will move data from Teradata into a flat file, and leverage the TTU utilities for that operation, or an LKM File to Oracle (EXTERNAL TABLE) will expose a flat file as an external table for Oracle. Similarly, an IKM Oracle Slowly Changing Dimension will generate code optimized for the Oracle database which implements a Slowly Changing Dimension (Type 2) type of integration.
Most developers will only use out of the box KMs—only the most advanced developers will modify the code templates. For that reason, we will keep KM modifications outside the scope of this book. But the examples we will go through in the rest of this book will help you understand how best to select a KM for the task at hand.
Models in ODI are used to store the metadata imported from the databases. When developers (or data custodian) create a model, they have the choice of importing only the relevant metadata (you may not want to import the definition of all tables and views in your environment). As we have seen earlier with Knowledge Modules for reverse-engineering, metadata can also be imported from applications, where objects can be a business representation of the data rather than an actual physical table.
Once the metadata has been imported, it can be organized and enhanced—Models can be grouped in folders. Submodels can be created within Models to organize tables in logical units.
When submodels are created, the users can define how tables will be organized; either they will be manually moved into the appropriate subfolders, or their location will be determined automatically by ODI based on their names.
Metadata can be enhanced by adding more constraints (such as referential integrity, check constraints) that would not exist in the database.
Metadata can also be directly created in ODI. Each model has a
Diagrams folder. Diagrams let users graphically design their table structures. More importantly, they can drag-and-drop object definitions from other technologies, and ODI will automatically translate the datatypes from one technology to the next.
If tables are derived from another technology, they can still be manually modified (by changing the column names, datatypes, adding or removing columns, and constraints). The hidden benefit of the use of diagrams to create tables from other Models is the ability to automate the generation of Interfaces.
ODI knows what table was used as a basis for the new one. If you right-click on the model name, and select the menu option Generate Interfaces IN, ODI will automatically create an interface with the original table as a source and the new table as a target with all columns properly mapped. You will be prompted to place the interface in the project and folder of your choice. We will take a detailed look at Interfaces in the next section. If you choose to select the menu option Generate Interfaces OUT, ODI will generate the reverse interface, with the new table as a source and the original one as a target.
Interfaces are where the transformations are defined. We will spend a lot of time in this book covering the details of Interfaces. Interfaces are created in projects, and the various components are organized in Packages.
There are multiple parts to an interface such as description, mappings, flow, controls, to name a few.
Often overlooked, descriptions are the documentation of the objects. Because the descriptions are stored with the objects in the repository, all documentation generated by ODI will contain the descriptions. It may sound trivial, but the descriptions are key to the success of your projects.
ODI adheres to a concept called "declarative design". The definition of the transformation logic is isolated from the definition of the data movement and integration logic for two reasons which are as follows:
Change to one should not alter the other: If I decide to add or remove columns in my mappings, it should have no impact on the type of technology I use (external tables, bulk load, JDBC, and so on) or the type of integration strategy I use (inserts only, updates, and so on). Similarly if I decide to change technology or integration strategy, this should have no impact on my transformations.
Productivity can be greatly improved: Mappings are often unique and must be handled one column at a time. Integration strategies and technologies can be selected once for a complete project with minimal changes and reviews. Separating the two allows developers to focus more on the moving parts (the transformations) than on the stable parts (the integration strategy).
Mappings are where the transformation logic is defined.
The flow tab of the interfaces is where the integration strategies are selected. The Loading Knowledge Modules (LKM) will define which techniques must be used to extract data from remote source systems and how to load the data in the target system. The Integration Knowledge Module (IKM) will define which integration strategy will be used to integrate the data in the target system.
Interfaces will only list the KMs that have been imported into the project where they are created (and starting with ODI 184.108.40.206, the Global KMs that are shared across projects). One key benefit is that it is possible to control which strategies will be used for a given project, hence limiting the choices of the developers to the choices made by the team.
This will ensure the following:
All developers implement the same best practice
If changes are required later in the project, they can be done extremely efficiently by changing the standard for a given project
One option that is available with some IKMs is the ability to control data integrity on the fly. There are two options called Flow Control and Static Control. Flow Control will check for data inconsistencies, remove invalid data from the staging tables, and write them to a separate error table. Static Control will check for inconsistencies in the target table after it has been loaded with all incoming records. Invalid records identified in the target table are then copied to the error table but they are not removed from the target table. When developers decide to activate one or both options in the Flow tab of the interface, they can then refine what will be done—the error table can be recreated automatically. Some constraints can be ignored while others are enforced.
All constraints listed here are defined at the metadata level, under the definition of the tables in the Models.
Packages are designed to orchestrate the individual objects created by the developers—interfaces, variables, procedures will be sequenced in Packages. If a step fails in a package, it is possible to branch out and immediately take action based on the errors that are detected. For execution purposes, Packages are compiled into Scenarios. Scenarios execution can then be organized with Load Plans. We will see all these concepts in more detail in a dedicated chapter.
In this chapter we started by exploring the core concepts behind ODI architecture and components.
In terms of architecture, you should now have a better understanding of what the different elements are, in particular the repository, Studio, and Agents. This will be important as you go into the next chapter where we cover the installation of the product.
In terms of concepts, we have covered the key elements that differentiate ODI from other products, namely, Execution Contexts, Knowledge Modules, Models, Interfaces, Packages and Load Plans. We will now illustrate these concepts with actual examples in the rest of the book, expanding on this quick introduction.