Tuesday Aug 04, 2015

Simplicity in Leveraging Oracle Data Integrator for Cloud Applications

Check out last week’s A-Team Blog post… A Universal Cloud Applications Adapter for ODI

Learn about the simplicity of leveraging Oracle Data Integrator (ODI) with all emerging technologies in the world of cloud computing!

For more A-Team reads on ODI, browse through the A-Team Chronicles.

Wednesday Jul 08, 2015

ODI KMs for Business Intelligence Cloud Service

In this article we will learn how to leverage Oracle Data Integrator’s extensible Knowledge Modules (KM) framework to create knowledge modules to load data into the Oracle Business Intelligence Cloud Service (BICS). The following instructions are targeted for BICS instances using Schema Cloud Service, if your BICS instance uses Database as a Service then you can directly load data into DBaaS tables as described in the blog post ODI 12c and DBaaS in the Oracle Public Cloud. More details on implementing Knowledge Modules can be found in Knowledge Module Developer Guide.

BICS exposes REST APIs that allows programmatically creating, managing, and loading schemas, tables, and data into Oracle BI Cloud Service. We will invoke these REST APIs using the Jersey client libraries providing wrapper implementation for invoking RESTful web services. The sample implementation of it is available on Java.net : RKM and IKM for Oracle BI Cloud Service
[Read More]

Tuesday Jul 07, 2015

Chalk Talk Video: Kick-Start Big Data Integration with Oracle

Next in the series for Oracle Data Integration chalk talk videos, we speak to Oracle Data Integrator (ODI) for big data. ODI allows you to become a big data developer without learning to code Java and Map Reduce! ODI generates the code and optimizes it with support for Hive, Spark, Oozie, and Pig.

View this video to learn more: Chalk Talk: Kick-Start Big Data Integration with Oracle.

For additional information on Oracle Data Integrator, visit the ODI homepage and the ODI for Big Data page. This blog can be very handy also: Announcing Oracle Data Integrator for Big Data.

Wednesday Jul 01, 2015

ODI - Integration with Oracle Storage Cloud Service

Oracle Data Integrator’s open tool framework can be leveraged to quickly get access to the Oracle Storage Cloud Service, which is gradually becoming an essential part for integrating on premise data to many cloud services. The reference implementation of an open tool for Oracle Storage Cloud is now available on the Data Integration project on Java.net: ODI OpenTool for Oracle Storage Cloud which can be used and modified as per your integration needs. [Read More]

Tuesday Jun 09, 2015

Oracle Data Integrator Journalizing Knowledge Module for GoldenGate Integrated Replicat Blog from the A-Team

As always, useful content from the A-Team…

Check out the most recent blog about how to modify the out-of-the-box Journalizing Knowledge Module for GoldenGate to support the Integrated Replicat apply mode.

An Oracle Data Integrator Journalizing Knowledge Module for GoldenGate Integrated Replicat

Enjoy!

Wednesday May 13, 2015

Looking for Cutting-Edge Data Integration: 2015 Excellence Awards

It is nomination time!!!

This year's Oracle Fusion Middleware Excellence Awards will honor customers and partners who are creatively using various products across Oracle Fusion Middleware. Think you have something unique and innovative with Oracle Data Integration products?

We'd love to hear from you! Please submit today in the Big Data and Analytics category.

The deadline for the nomination is July 31, 2015. Win a free pass to Oracle OpenWorld 2015!!

Let’s reminisce a little…

For details on the 2014 Data Integration Winners: NET Serviços and Griffith University, check out this blog post.

For details on the 2013 Data Integration Winners: Royal Bank of Scotland’s Market and International Banking and The Yalumba Wine Company, check out this blog post.

For details on the 2012 Data Integration Winners: Raymond James Financial and Morrisons Supermarkets, check out this blog post.

We hope to honor you!

Click here to submit your nomination today. And just a reminder: the deadline to submit a nomination is 5pm Pacific Time on July 31, 2015.

Tuesday May 12, 2015

ODI 12c - Improving Usability of KM recipes

This post is all about reducing user errors and improving usability surrounding definition of Knowledge Modules and their usage. Knowledge Modules are all about encapsulating recipes - every great cookbook has lots of recipes and some are based on common techniques and ingredients. ODI Knowledge Modules are data integration recipes - they define how to access, transform and store information based on the directions in the KM. There are a few usability improvements in the recent 12.1.3.0.1 release around both the KM definition and usage of the KM that make for an improved user experience. I've seen many KMs over the years where its many things to many people and there are a bundle of options that expose all facets for every path possible in the KM - the user has to read the description and follow the instructions.

The first improvement I'll mention is the KM (and procedure) option type of 'Choice'. Not exactly rocket science here I know, but an addition that greatly helps usage of a KM that may do more than one thing. Let's take the example of a KM that can make different .....pizzas. In the past you would have an option field which was a string based value where the user would type either margerita or pepperoni to drive a path within the KM implementation, users of the KM would have to know that those were the accepted option values and they'd have to type it in properly (otherwise things would go wrong). So now the options can be specified as the 'Choice' type, see below where in the IKM we capture the recipe type as a choice.

The choices can be defined in the default value field, below the recipe is going to either create margherita pizza or pepperoni- these are the only two choices and the default is margherita;

Then I can define all the rest of the options, let's say the pizza needs flour, oil, salt, yeast and pepperoni needs... pepperoni of course and margherita needs tomatoes and basil - so some of the options are applicable to both types and some are only applicable to the specific one. Prior to this release when the KM is used you would see all of these option values and you'd be reading the description 'only set basil if you are making margherita' and so on. Another feature has been added to improve this area. Below you can see all of the options....

One column was snipped out of the image - the condition expression. This is a groovy expression to determine whether the option is shown. So now we can say only display basil when margherita pizza is the recipe type or only display pepperoni when pepperoni is the recipe type. We see below the options only applicable to the recipe type are displayed - anything common has no condition expression.

The groovy snippet must return a string. The string must be of the format show=true|false

When you see the KM assigned in the mapping it becomes a little clearer. Below you can see the choice box, the user is constrained to pick one of those types;

 When margherita is selected above remember some options were for margherita and some were for pepperoni, we see a subset of options;

Above you can see tomatoes and basic, if you change the type to pepperoni the above options are hidden and pepperoni is displayed as below;

This helps guide the user into configuration options that are more applicable to a path within the KM. One of the other visual enhancements is the ability to group options together. We can add all of the options above into a group named 'Ingredients' that helps visually group related options together;

 Then when this is used you see the options related to ingredients from where the KM is assigned.

You can see how these help improve the usability of KMs in ODI and help reduce errors by further specializing how data is entered and related in the configuration options of the KM. The tasks within the KM can retrieve the option values and perform condition code based on those values. There are some other areas around this but that's all for now. The functionality described here is available in the 12.1.3.0.1.

Friday Apr 10, 2015

This Week's A-Team Blog Speaks to Automating Changes after Upgrading ODI or Migrating from Oracle Warehouse Builder

The A-Team not only provides great content, they are humorous too!

Check out this week’s post, the title says it all: Getting Groovy with Oracle Data Integrator: Automating Changes after Upgrading ODI or Migrating from Oracle Warehouse Builder

The article covers various scripts written in Groovy and leverage the ODI SDK that assist in automating massive changes to one’s repository. These initially came to be as a result of customer desire in enhancing their environment in their effort to move from Oracle Warehouse Builder (ODI) to Oracle Data Integrator (ODI), but in the end came the realization that these scripts could be used by any ODI user.

Happy reading!

Thursday Mar 26, 2015

Oracle Big Data Lite 4.1.0 is available with more on Oracle GoldenGate and Oracle Data Integrator

Oracle's big data team has announced the newest Oracle Big Data Lite Virtual Machine 4.1.0.  This newest Big Data Lite Virtual Machine contains great improvements from a data integration perspective with inclusion of the recently released Oracle GoldenGate for Big Data.  You will see this in an improved demonstration that highlights inserts, updates, and deletes into Hive using Oracle GoldenGate for Big Data with Oracle Data Integrator performing a merge of the new operations into a consolidated table.

Big Data Lite is a pre-built environment which includes many of the key capabilities for Oracle's big data platform.   The components have been configured to work together in this Virtual Machine, providing a simple way to get started in a big data environment.  The components include Oracle Database, Cloudera Distribution including Apache Hadoop, Oracle Data Integrator, Oracle GoldenGate amongst others. 

Big Data Lite also contains hands-on labs and demonstrations to help you get started using the system.  Tame Big Data with Oracle Data Integration is a hands-on lab that teaches you how to design Hadoop data integration using Oracle Data Integrator and Oracle GoldenGate. 

                Start here to learn more!  Enjoy!

Thursday Feb 19, 2015

Hive, Pig, Spark - Choose your Big Data Language with Oracle Data Integrator

The strength of Oracle Data Integrator (ODI) has always been the separation of logical design and physical implementation. Users can define a logical transformation flow that maps any sources to targets without being concerned what exact mechanisms would be used to realize such a job. In fact, ODI doesn’t have its own transformation engine but instead outsources all work to the native mechanisms of the underlying platforms, may it be relational databases, data warehouse appliances, or Hadoop clusters.

In the case of Big Data this philosophy of ODI gains even more importance. New Hadoop projects are incubated and released on a constant basis and introduce exciting new capabilities; the combined brain trust of the big data community conceives new technology that outdoes any proprietary ETL engine. ODI’s ability to separate your design from the implementation enables you to pick the ideal environment for your use case; and if the Hadoop landscape evolves, it is easy to retool an existing mapping with a new physical implementation. This way you don’t have to tie yourself to one language that is hyped this year, but might be legacy in the next.

ODI enables the generation from logical design into executed code through physical designs and Knowledge Modules. You can even define multiple physical designs for different languages based on the same logical design. For example, you could choose Hive as your transformation platform, and ODI would generate Hive SQL as the execution language. You could also pick Pig, and the generated code would be Pig Latin. If you choose Spark, ODI will generate PySpark code, which is Python with Spark APIs. Knowledge Modules will orchestrate the generation of code for the different languages and can be further configured to optimize the execution of the different implementation, for example parallelism in Pig or in-memory caching for Spark.

The example below shows an ODI mapping that reads from a log file in HDFS, registered in HCatalog. It gets filtered, aggregated, and then joined with another table, before being written into another HCatalog-based table. ODI can generate code for Hive, Pig, or Spark based on the Knowledge Modules chosen. 

 ODI provides developer productivity and can future-proof your investment by overcoming the need to manually code Hadoop transformations to a particular language.  You can logically design your mapping and then choose the implementation that best suits your use case.

Friday Jan 09, 2015

ODI 12c - Mapping SDK Overview

In this post I'll show some of the high level concepts in the physical design and the SDKs that go with it. To do this I'll cover some of the logical design area so that it all makes sense. The conceptual model for logical mapping in ODI 12c is shown below (it's quite a change from 11g), the model below allows us to build arbitrary flows. Each entity below you can find in the 12c SDK. Many of these have specialized classes - for example MapComponent has specializations for the many mapping components available from the designer - these classes may have specific business logic or specialized behavior. You can use the strongly typed, highly specialized classes like DatastoreComponent or you can write applications in a generic manner using the conceptual high level SDK - this is the technique I used in the mapping builder here.

The heart of the SDK for this area of the model can be found here;

If you need to see these types in action, take the mapping illustration below as an example, I have annotated the different items within the mapper. The connector points are shown in the property inspector, they are not shown in the graphical design. Some components have many input or output connector points (for example set component has many input connector points). Some components are simple expression based components (such as join and filter) we call these selector components, other components project a specific shape, we call those projectors - that's just how we classify them. 

In 12c we clearly separated the physical design from the logical, in 11g much of this was blended together. In separating them we also allow many physical designs for a logical mapping design. We also had to change the physical SDK and model so that we could support multiple targets and arbitrary flows. 11g was fairly rigid - if you look at the 'limitations' sections of the KMs you can see some of that. KMs are assigned on map physical nodes in the physical design, there are some helper methods on execution unit so you can set/get KMs.

The heart of the SDK for this logical mapping area of the model can be found here;

If we use the logical mapping shown earlier and look at the physical design we have for it, we can annotate the items below so you can envisage how each of the classes above is used in the design;

The MapPhysicalDesign class has all of the physical related information such as the ODI Optimization Context and Default Staging Location (there also exists a Staging Location Hint on the logical mapping design) - these are items that existed in ODI 11g and are carried forward.

To take an example if I want to change the LKMs or IKMs set on all physical designs, one approach would be to iterate through all of the nodes in a physical design and you can check whether an LKM or an IKM is assigned for that node - this then let;s you do all sorts - from get the current setting, to setting it with a new value. The snippet below gives a small illustration using groovy of the methods from the ODI SDK;

  1.         PhysicalDesignList=map.getPhysicalDesigns()
  2.          for (pd in PhysicalDesignList){
  3.             PhysicalNodesList=pd.getPhysicalNodes()
  4.             for (pn in PhysicalNodesList){
  5.                 if (pn.isLKMNode()){
  6.                     CurrentLKMName=pn.getLKMName()
  7. ...
  8.                          pn.setLKM(myLKM) 
  9.                 }else if (pn.isIKMNode()){
  10.                     CurrentIKMName=pn.getIKMName()
  11. ...
  12.                      pn.setIKM(myIKM)

There are many other methods within the SDK to do all sorts of useful stuff - first example is the getAllAPNodes method on a MapPhysicalDesign. This gives all of the nodes in a design which will have LKMs assigned - so you can quickly set or check. The second example is the getTargetNodes method on MapPhysicalDesign - this is handy to get all target nodes to set IKMs on, Final example is to find an AP node in the physical design for a logical component in your design - use the method findNode to achieve this.

Hopefully there are some useful pointers here, worth being aware of the ODI blog on Mapping SDK the ins and outs which provides an overview and cross reference to the primary ODI objects and the underpinning SDKs. If there are any other specific questions let us know.

Wednesday Dec 17, 2014

Oracle Partition Exchange Blog from the ODI A-Team

More great information from the ODI A-Team!

Check out the A-Team’s most recent blog about the Oracle Partition Exchange – it does come in two parts:

Using Oracle Partition Exchange with ODI

Configuring ODI with Oracle Partition Exchange

The knowledge module is on Java.Net, and it is called “IKM Oracle Partition Exchange Load”.  To search for it, enter “PEL” or “exchange” in the Search option of Java.Net.

A sample ODI 12.1.3 Repository is available as well.  The ODI sample repository has great examples of how to perform both initial and incremental data upload operations with Oracle Partition Exchange.  This repository will help users to understand how to use Oracle Partition Exchange with ODI.

Happy reading!

Wednesday Dec 10, 2014

Oracle Enterprise Metadata Management 12.1.3.0.1 is now available!

As a quick refresher, Metadata Management is essential to solve a wide variety of critical business and technical challenges which include how report figures are calculated, understanding the impact of changes to data upstream, providing reports in a business friendly way in the browser and providing reporting capabilities on the entire metadata of an enterprise for analysis and improvement. Oracle Enterprise Metadata Management is built to solve all these pressing needs for customers in a lightweight browser-based interface. Today, we announce the availability of Oracle Enterprise Metadata Management 12.1.3.0.1 as we continue to enhance this offering.

With Oracle Enterprise Metadata Management 12.1.3.0.1, you will find business glossary updates, updates for a better experience to the user interface as well as improved and new metadata harvesting bridges including Oracle SQL Server Data Modeler, Microsoft SQL Server Integration Services, SAP Sybase PowerDesigner, Tableau and more. There are also new dedicated web pages for tracing data lineage and impact! At a more granular level you will also find new customizable action menus per repository object type for more personalization. For a full read on new features, please read here. Additionally, view here for the certification matrix details.

Download Oracle Enterprise Metadata Management 12.1.3.0.1!

Wednesday Nov 12, 2014

ODI 12c - Spark SQL and Hive?

In this post I'll cover some new capabilities in the Apache Spark 1.1 release and show what they mean to ODI today. There's a nice slide shown below from the Databricks training for Spark SQL that pitches some of the Spark SQL capabilities now available. As well as programmatic access via Python, Scala, Java, the Hive QL compatibility within Spark SQL is particularly interesting for ODI...... today. The Spark 1.1 release supports a subset of the Hive QL features which in turn is a subset of ANSI SQL, there is already a lot there and it is only going to grow. The Hive engine today uses map-reduce which is not fast today, the Spark engine is fast, in-memory - you can read much more on that elsewhere.

Figure taken from from the Databricks training for Spark SQL, July 2014.

In the examples below I used the Oracle Big Data Lite VM, I downloaded the Spark 1.1 release and built using Maven (I was on CDH 5.2). To use Spark SQL in ODI, we need to create a Hive data server - the Hive data server masquerades as many things, it can can be used for Hive, for HCatalog or for Spark SQL. Below you can see my data server, note the Hive port is 10001, by default 10000 is the Hive server port - we aren't using Hive server to execute the query, here we are using the Spark SQL server. I will show later how I started the Spark SQL server on this port (Apache Spark doc for this is here).

I started the server using the Spark standalone cluster that I configured using the following command from my Spark 1.1 installation;

./sbin/start-thriftserver.sh --hiveconf hive.server2.thrift.bind.host bigdatalite --hiveconf hive.server2.thrift.port 10001 --master spark://192.168.122.1:7077

You can also specify local (for test), Yarn or other cluster information for the master. I could have just as easily started the server using Yarn by specify the master URI as something like --master yarn://192.168.122.1:8032 where 8032 is my Yarn resource manager port. I ran using the 10001 port so that I can run both Spark SQL and Hive engines in parallel whilst I do various tests. To reverse engineer I actually used the Hive engine to reverse engineer the table definitions in ODI (I hit some problems using the Spark SQL reversing, so worked around it) and then changed the model to use my newly created Spark SQL data server above.

Then I built my mappings just like normal - and used the KMs in ODI for Hive just like normal. For example the mapping below aggregates movie ratings and then joins with movie reference data to load movie rating data - the mapping uses the datastores from a model obtained from the Hive metastore;

If you look at the physical design the Hive KMs are assigned but we will execute this through the Spark SQL engine rather than through Hive. The switch from engine to engine was handled in the URL within our our Hive dataserver.

When the mapping is executed you can use the Spark monitoring API to check the status of the running application and Spark master/workers.

You can also monitor from the regular ODI operator and ODI console. Spark SQL support uses the Hive metastore for all the table definitions be they internally or externally managed data. 

There are other blogs from tools showing how to access and use Spark SQL, such as the one here from Antoine Amend using SQL Developer. Antoine has also another very cool blog worth checking out Processing GDELT Data Using Hadoop. In this post he shows a custom InputFormat class that produces records/columns. This is a very useful post for anyone wanting to see the Spark newAPIHadoopFile api in action. It has a pretty funky name, but is a key piece (along with its related methods) of the framework.

  1. // Read file from HDFS - Use GdeltInputFormat
  2. val input = sc.newAPIHadoopFile(
  3.    "hdfs://path/to/gdelt",
  4.    classOf[GdeltInputFormat],
  5.    classOf[Text],
  6.    classOf[Text]

Antoine also provides the source code to GdeltInputFormat so you can see the mechanics of his record reader, although the input data is delimited data (so could have been achieved in different ways) it's a useful resource to be aware of.

If you are looking at Spark SQL, this post was all about using Spark SQL via the JDBC route - there is another whole topic on transformations using the Spark framework alongside Spark SQL that is for future discussion. You should be aware of and check out the Hive QL compatibility documentation here, check what you can do can't do within Spark SQL today. Download the BDA Lite VM and give it a try.

Tuesday Oct 28, 2014

ODI 12c and DBaaS in the Oracle Public Cloud

This article illustrates how to connect ODI on premise to Oracle in the cloud (OPC), specifically the Database as a Service (DBaaS, see doc here) offering. You will see how easy it is to configure connectivity from within ODI and the use of familiar tools gives you the same consistency from on premise use to the cloud. A big concern for cloud computing is security and ensuring access is restricted and as secure as possible. For ODI on premise the challenge is how to connect to such a secure service. ODI provides tasks for transferring files to and from locations - databases are generally accessed via JDBC.

The initial state of an Oracle DBaaS service restricts remote access to SSL - so you can't just remotely connect by default to an Oracle database listener for example (it is possible to open that up by configuring this within DBaaS). File transfer to the cloud can be done out of the box using sftp capabilities, access to the database in order to load it with data, to transform data within it and to extract data from it can be done with a small bit of SSL tunneling - let's see how. The examples discussed in this article have been developed with ODI 12.1.3, a copy of the driver which performs the SSL tunneling can be found on java.net here. With this driver it is effortless to work with an on premise ODI and an Oracle database in the cloud.

Before we get to the ODI parts let's look at the basics, this is mentioned in the DBaaS documentation but sometimes it's simpler to read what someone has done than follow the doc.....

If you want to be using ODI or other remote capabilities such as ssh, sftp then before creating the Oracle database instance in the cloud you should generate a secure private key-public key pair. The public key gets used when you create the Oracle database instance in the cloud and the private key is used by SSL tools (such as sftp, ssh or the driver used) to securely connect to the cloud. 

When you create the key using something like PUTTY, then ensure you save the public key, private key and export the key using the OpenSSH key option also. ODI actually needs the OpenSSH format right now as the version of a library it depends on supports this.


You can see where the public key is provided below in the Instance Configuration section.....


The great news about the DBaaS capabilities is that it is all very familiar for Oracle database folks also the service itself can be managed from the command line - so as well as the web pages console and EM etc, you can use the command line and work the way you are used to.

Anyway, back on course... when you have the instance up and running it's time to have some fun with it!

File Transfer to the Oracle Public Cloud

In ODI you can use the OdiSftpPut/Get tool in a package, procedure or KM to transfer data to/from the cloud. You can see in the example below the OdiSftpPut tools is being used to transfer a file 'm.csv' from the local filesystem (d:\data) to the directory (/home/oracle) in the cloud. The private key is specified in the property 'SSH Identity File' and the key file password is specified in 'Remote User Password'. The OS user to use for the ftp is specified as 'oracle' in the property 'Remote User Name'.

Very simple. The DBaaS instance has OS users created when initialized you can read more about the users 'opc' and 'oracle' in the DBaaS documentation.

Transforming Data in the Oracle Public Cloud

Mappings are used to transform data from one representation to another. In this example you will see how the file staged in the Oracle Public Cloud is integrated with an Oracle target - just like standard ODI on premise use cases. It is no different. Below you can see the image has the logical mapping at the top, with the file being mapped to an Oracle target table, then the middle part of the image shows the physical design, the map uses the LKM File to Oracle (External Table) KM to create an external table on top of the file in the cloud and then the target is integrated with the Oracle Insert KM. 

When I execute the mapping all of the statements to transform are executed in the OPC - in this particular design everything is executed inside the Oracle database.

The ODI data server definition is using a custom driver (here) which extends the Oracle JDBC driver. The driver creates a SSH tunnel between the host executing the connect and the instance in the cloud. This means all ODI objects such as procedures, mappings and so forth that execute statements on regular Oracle systems can execute them on the cloud instances too. I actually created my demonstration schemas and granted all the permissions using a procedure in ODI. The procedure is shown below, see the snippet of the statements creating the users - the target logical schema was my DBAAS_INSTANCE.

Let's dig under the covers and have a look at how the physical schema is defined. You'll need the driver, and have it copied into your oracledi/userlib directory (or wherever your agent is installed if using an agent). You can then define the connection, specify the database user name and password for that database user;

Then you specify the driver, the driver you need to download and mentioned above. The URL is of the form of the Oracle JDBC driver. The difference is in how you specify the host, port and sid/service. The sid/service are your actual cloud service details. Since we are using the SSH tunnel technique, we actually specify the local host and a port number (default 5656) on the local host.

The properties to configure the SSH tunnel are defined either in the properties panel or in a file specified in the properties panel. I've decided here to use the file approach, so can specify the file name in the property propertiesFile.

In my example, this file contains;

  • sslUser=oracle
  • sslHost=<my_cloud_ip_address>
  • sslRHost=<my_cloud_ip_address>
  • sslRPort=1521
  • sslPassword=my_private_key_password
  • sslPrivateKey=D:\\credentials\\dbcloud12c_private_key_openssh.ppk

That is all that is needed and you can be very creative using all the powers of Oracle in the cloud and ODI for integration. Here is a summary of the properties supported by the driver.

Property Name Property Description
sslUser The OS user to connect via SSL with.
sslHost The address of the host to SSL to.
sslRHost The tunnel can be made between the client through the SSL host to this host. In my case this was the same as the SSL host.
sslRPort The port to tunnel to. The Oracle listener is often run on 1521, so this is the default if this property is not specified.
sslPassword The password for the private key file. In ODI you must use OpenSSH formatted private key file.
sslPrivateKey The SSL private key file location.
sslLPort By default the local port used is 5656, it can be changed with this property. You must reference this port number in the URL also.

The driver is a fairly simple wrapper around the Oracle JDBC driver, it leverages SSL tunneling to forward requests on a secure port to the Oracle TNS listener. This technique enables a very familiar way of connecting and interacting with the Oracle database, the driver is on java.net and is available to try and get feedback on. So try it and let us know what you think. Familiarity and consistency are very important both from the stance of the tooling and leveraging existing knowledge (including modules). This allows ODI users to work with the Oracle Public Cloud DBaaS instance just as they do with their on premise systems. Have fun!

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