Setting up Oracle Database on Docker

A couple of days ago it was announced that several Oracle images were available on the Docker Store.

This is by far the easiest Oracle Database install I have every done !

You simply have no excuse now for not installing and using an Oracle Database. Just go and do it now!

The following steps outlines what I did you get an Oracle 12.1c Database.

1. Download and Install Docker

There isn't much to say here. Just go to the Docker website, select the version docker for your OS, and just install it.

You will probably need to create an account with Docker.

After Docker is installed it will automatically start and and will be placed in your system tray etc so that it will automatically start each time you restart your laptop/PC.

2. Adjust the memory allocation

From the system tray open the Docker application. In the Advanced section allocate a bit more memory. This will just make things run a bit smoother. Be a bit careful on how much to allocate.

In the General section check the tick-box for automatically backing up Docker VMs. This is assuming you have back-ups setup, for example with Time Machine or something similar.

3. Download & Edit the Oracle Docker environment File

On the Oracle Database download Docker webpage, click on the the Get Content button.

You will have to enter some details like your name, company, job title and phone number, then click on the check-box, before clicking on the Get Content button. All of this is necessary for the Oracle License agreement.

The next screen lists the Docker Services and Partner Services that you have signed up for.

Click on the Setup button to go to the webpage that contains some of the setup instructions.

The first thing you need to do is to copy the sample Environment File. Create a new file on your laptop/desktop and paste the environment file contents into the file. There are a few edits you need to make to this file. The following is the edited/modified Environment file that I created and used. The changes are for DB_SID, DB_PASSWD and DB_DOMAIN.

####################################################################
## Copyright(c) Oracle Corporation 1998,2016. All rights reserved.##
##                                                                ##
##                   Docker OL7 db12c dat file                    ##
##                                                                ##
####################################################################

##------------------------------------------------------------------
## Specify the basic DB parameters
##------------------------------------------------------------------

## db sid (name)
## default : ORCL
## cannot be longer than 8 characters

DB_SID=ORCL

## db passwd
## default : Oracle

DB_PASSWD=oracle

## db domain
## default : localdomain

DB_DOMAIN=localdomain

## db bundle
## default : basic
## valid : basic / high / extreme
## (high and extreme are only available for enterprise edition)

DB_BUNDLE=basic

## end

I called this file 'docker_ora_db.txt'

4. Download and Configure Oracle Database for Docker

The following command will download and configure the docker image

$ docker run -d --env-file ./docker_ora_db.txt -p 1527:1521 -p 5507:5500 -it --name dockerDB121 --shm-size="8g" store/oracle/database-enterprise:12.1.0.2

This command will create a container called 'dockerDB121'. The 121 at the end indicate the version number of the Oracle Database. If you end up with a number of containers containing different versions of the Oracle Database then you need some way of distinguishing them.

Take note of the port mapping in the above command, as you will need this information later.

When you run this command, the docker image will be downloaded from the docker website, will be unzipped and the container setup and ready to run.

5. Log-in and Finish the configuration

Although the docker container has been setup, there is still a database configuration to complete. The following images shows that the new containers is there.

To complete the Database setup, you will need to log into the Docker container.

docker exec -it dockerDB121 /bin/bash

Then run the Oracle Database setup and startup script (as the root user).

/bin/bash /home/oracle/setup/dockerInit.sh

This script can take a few minutes to run. On my laptop it took about 2 minutes.

When this is finished the terminal session will open as this script goes into a look.

To run any other commands in the container you will need to open another terminal session and connect to the Docker container. So go open one now.

6. Log into the Database in Docker

In a new terminal window, connect to the Docker container and then switch to the oracle user.

su - oracle

Check that the Oracle Database processes are running (ps -ef) and then connect as SYSDBA.

sqlplus / as sysdba

Let's check out the Database.

SQL> select name,DB_UNIQUE_NAME from v$database;

NAME	  DB_UNIQUE_NAME
--------- ------------------------------
ORCL	  ORCL


SQL> SELECT v.name, v.open_mode, NVL(v.restricted, 'n/a') "RESTRICTED", d.status
     FROM v$pdbs v, dba_pdbs d
     WHERE v.guid = d.guid
     ORDER BY v.create_scn;


NAME			       OPEN_MODE  RES STATUS
------------------------------ ---------- --- ---------
PDB$SEED		       READ ONLY  NO  NORMAL
PDB1			       READ WRITE NO  NORMAL

And the tnsnames.ora file contains the following:

ORCL =   (DESCRIPTION = 
    (ADDRESS = (PROTOCOL = TCP)(HOST = 0.0.0.0)(PORT = 1521))
     (CONNECT_DATA = 
      (SERVER = DEDICATED)
       (SERVICE_NAME = ORCL.localdomain)     )   )

PDB1 =   (DESCRIPTION =
     (ADDRESS = (PROTOCOL = TCP)(HOST = 0.0.0.0)(PORT = 1521))
      (CONNECT_DATA =
       (SERVER = DEDICATED)
       (SERVICE_NAME = PDB1.localdomain)     )   )

You are now up an running with an Docker container running an Oracle 12.1 Databases.

7. Configure SQL Developer (on Client) to

access the Oracle Database on Docker

You can not use your client tools to connect to the Oracle Database in a Docker Container. Here is a connection setup in SQL Developer.

Remember that port number mapping I mentioned in step 4 above. See in this SQL Developer connection that the port number is 1527.

Thats it. How easy is that. You now have a fully configured Oracle 12.1c Enterprise Edition Database to play with, to have fun and to explore all the wonderful features of the Oracle Database.

ODM Model View Details Views in Oracle 12.2

A new feature for Oracle Data Mining in Oracle 12.2 is the new Model Details views.

In Oracle 11.2.0.3 and up to Oracle 12.1 you needed to use a range of PL/SQL functions (in DBMS_DATA_MINING package) to inspect the details of a data mining/machine learning model using SQL.

Check out these previous blog posts for some examples of how to use and extract model details in Oracle 12.1 and earlier versions of the database

Association Rules in ODM-Part 3

Extracting the rules from an ODM Decision Tree model

Cluster Details

Viewing Decision Tree Details

Instead of these functions there are now a lot of DB views available to inspect the details of a model. The following table summarises these various DB Views. Check out the DB views I've listed after the table, as these views might some some of the ones you might end up using most often.

I've now chance of remembering all of these and this table is a quick reference for me to find the DB views I need to use. The naming method used is very confusing but I'm sure in time I'll get the hang of them.

NOTE: For the DB Views I've listed in the following table, you will need to append the name of the ODM model to the view prefix that is listed in the table.

Data Mining Type	Algorithm & Model Details	12.2 DB View	Description
Association	Association Rules	DM$VR	generated rules for Association Rules
	Frequent Itemsets	DM$VI	describes the frequent itemsets
	Transaction Itemsets	DM$VT	describes the transactional itemsets view
	Transactional Rules	DM$VA	describes the transactional rule view and transactional itemsets
Classification	(General views for Classification models)	DM$VT DM$VC	describes the target distribution for Classification models describes the scoring cost matrix for Classification models
	Decision Tree	DM$VP DM$VI DM$VO DM$VM	describes the DT hierarchy & the split info for each level in DT describes the statistics associated with individual tree nodes Higher level node description describes the cost matrix used by the Decision Tree build
	Generalized Linear Model	DM$VD DM$VA	describes model info for Linear Regres & Logistic Regres describes row level info for Linear Regres & Logistic Regres
	Naive Bayes	DM$VP DM$VV	describes the priors of the targets for Naïve Bayes describes the conditional probabilities of Naïve Bayes model
	Support Vector Machine	DM$VL	describes the coefficients of a linear SVM algorithm
Regression ???	Doe	80	50
Clustering	(General views for Clustering models)	DM$VD DM$VA DM$VH DM$VR	Cluster model description Cluster attribute statistics Cluster historgram statistics Cluster Rule statistics
	k-Means	DM$VD DM$VA DM$VH DM$VR	k-Means model description k-Means attribute statistics k-Means historgram statistics k-Means Rule statistics
	O-Cluster	DM$VD DM$VA DM$VH DM$VR	O-Cluster model description O-Cluster attribute statistics O-Cluster historgram statistics O-Cluster Rule statistics
	Expectation Minimization	DM$VO DM$VB DM$VI DM$VF DM$VM DM$VP	describes the EM components the pairwise Kullback–Leibler divergence attribute ranking similar to that of Attribute Importance parameters of multi-valued Bernoulli distributions mean & variance parameters for attributes by Gaussian distribution the coefficients used by random projections to map nested columns to a lower dimensional space
Feature Extraction	Non-negative Matrix Factorization	DM$VE DM$VI	Encoding (H) of a NNMF model H inverse matrix for NNMF model
	Singular Value Decomposition	DM$VE DM$VV DM$VU	Associated PCA information for both classes of models describes the right-singular vectors of SVD model describes the left-singular vectors of a SVD model
	Explicit Semantic Analysis	DM$VA DM$VF	ESA attribute statistics ESA model features
Feature Section	Minimum Description Length	DM$VA	describes the Attribute Importance as well as the Attribute Importance rank

Normalizing and Error Handling views created by ODM Automatic Data Processing (ADP)

• DM$VN : Normalization and Missing Value Handling
• DM$VB : Binning

Global Model Views

• DM$VG : Model global statistics
• DM$VS : Computed model settings
• DM$VW :Alerts issued during model creation

Each one of these new DB views needs their own blog post to explain what informations is being explained in each. I'm sure over time I will get round to most of these.

Managing memory allocation for Oracle R Enterprise Embedded Execution

When working with Oracle R Enterprise and particularly when you are using the ORE functions that can spawn multiple R processes, on the DB Server, you need to be very aware of the amount of memory that will be consumed for each call of the ORE function.

ORE has two sets of parallel functions for running your user defined R scripts stored in the database, as part of the Embedded R Execution feature of ORE. The R functions are called ore.groupApply, ore.rowApply and ore.indexApply. When using SQL there are "rqGroupApply" and rqRowApply. (There is no SQL function equivalent of the R function ore.indexApply)

For each parallel R process that is spawned on the DB server a certain amount of memory (RAM) will be allocated to this R process. The default size of memory to be allocated can be found by using the following query.

select name, value from sys.rq_config;

NAME                                VALUE
----------------------------------- -----------------------------------
VERSION                             1.5
MIN_VSIZE                           32M
MAX_VSIZE                           4G
MIN_NSIZE                           2M
MAX_NSIZE                           20M

The memory allocation is broken out into the amount of memory allocated for Cells and NCells for each R process.

If your parallel ORE function create a large number of parallel R processes then you can see that the amount of overall memory consumed can be significant. I've seen a few customers who very quickly run out of memory on their DB servers. Now that is something you do not want to happen.

How can you prevent this from happening ?

There are a few things you need to keep in mind when using the parallel enabled ORE functions. The first one is, how many R processes will be spawned. For most cases this can be estimated or calculated to a high degree of accuracy. Secondly, how much memory will be used to process each of the R processes. Thirdly, how memory do you have available on the DB server. Fourthly, how many other people will be running parallel R processes at the same time?

Examining and answering each of these may look to be a relatively trivial task, but the complexity behind these can increase dramatically depending on the answer to the fourth point/question above.

To calculate the amount of memory used during the ORE user defined R script, you can use the R garbage function to calculate the memory usage at the start and at the end of the R script, and then return the calculated amount. Yes you need to add this extra code to your R script and then remove it when you have calculated the memory usage.

gc.start <- gc(reset=TRUE)
...
gc.end <- gc()
gc.used <- gc.end[,7] - gc.start[,7] # amount consumed by the processing

Using this information and the answers to the points/questions I listed above you can now look at calculating how much memory you need to allocated to the R processes. You can set this to be static for all R processes or you can use some code to allocate the amount of memory that is needed for each R process. But this starts to become messy. The following gives some examples (using R) of changing the R memory allocations in the Oracle Database. Similar commands can be issued using SQL.

> sys.rqconfigset('MIN_VSIZE', '10M') -- min heap 10MB, default 32MB
> sys.rqconfigset('MAX_VSIZE', '100M') -- max heap 100MB, default 4GB
> sys.rqconfigset('MIN_NSIZE', '500K') -- min number cons cells 500x1024, default 1M
> sys.rqconfigset('MAX_NSIZE', '2M') -- max number cons cells 2M, default 20M

Some guidelines - as with all guidelines you have to consider all the other requirements for the Database, and in reality you will have to try to find a balance between what is listed here and what is actually possible.

• Set parallel_degree_policy to MANUAL.
• Set parallel_min_servers to the number of parallel slave processes to be started when the database instances start, this avoids start up time for the R processes. This is not a problem for long running processes. But can save time with processes running for 10s seconds
• To avoid overloading the CPUs if the parallel_max_servers limit is reached, set the hidden parameter _parallel_statement_queuing to TRUE. Avoids overloading and lets processes wait.
• Set application tables and their indexes to DOP 1 to reinforce the ability of ORE to determine when to use parallelism.

Understanding the memory requirements for your ORE processes can be tricky business and can take some time to work out the right balance between what is needed by the spawned parallel R processes and everything else that is going on in the Database. There will be a lot of trial and error in working this out and it is always good to reach out for some help. If you have a similar scenario and need some help or guidance let me know.

OUG Ireland 2017 Presentation

Here are the slides from my presentation at OUG Ireland 2017. All about running R using SQL.

Embedded R Execution using SQL from Brendan Tierney

Presentations from OUGN17

Here are the presentations I gave at OUG Norway last week. These are also available on SlideShare

SQL: The one language to rule all your data from Brendan Tierney

Predictive analytics: Mining gold and creating valuable product from Brendan Tierney

Blog posts on Oracle Advanced Analytics features in 12.2

A couple of days ago Oracle finally provided us with an on-premises download for Oracle 12.2 Database.

Go and download load it from here

or

Download the Database App Development VM with 12.2 (This is what I did)

Over the past couple of months I've been using the DBaaS of 12.2, trying out some of the new Advanced Analytics option new features, and other new features. Here are the links to the blog posts on these new 12.2 new features. There will be more coming over the next few months.

New OAA features in Oracle 12.2 Database

Explicit Semantic Analysis in Oracle 12.2c Database

Explicit Semantic Analysis setup using SQL and PL/SQL

and slightly related is the new SQL Developer 4.2

Oracle Data Miner 4.2 New Features

Join the Oracle Scene Editorial Team

Are you a member of UKOUG?

How would you like to join the editorial team of Oracle Scene magazine as a deputy editor?

If you are interested we are looking to recruit 1 deputy editor to cover the Applications area and 2 deputy editors to cover the Tech area (DBA, Developer, BA, etc)

How much time is required? about 4 hours per edition, or maybe less.

What does a deputy editor do?

As part of the editorial team you will be expected to:

- Article Review

Articles submitted are uploaded to the review panel on Basecamp. During this time the editors should become familiar with the articles and have an idea of which ones would be appropriate for publication. Time approx 1.5hrs over a 2 week period.

- Editorial Call

After the review period has closed the editors come together for an editorial call (approx 1hr) to go through the feedback received on the articles, it is the editors job to validate any comments and select which articles should be chosen for publication. Time approx 1hr.

Some articles may need further rework by the authors and the editors provide comments & instructions as to the amends needed, in some cases the editors will take on the amends themselves or if they hold the relationship with the author they may wish to approach them direct. If any articles have been held over from the previous edition, the editors will relook at the articles and if any of the content needs to be updated they will advise. If we do not have articles submitted at this stage so the editors may need to source some additional content.

- Editorial Review

Once the selected articles are edited they are passed to the designer for layout, editors will then receive a first copy of the magazine where they will read the articles relevant to them (Apps or Tech) marking up on the pdf any errors in the text or images found. We try to build in time over a weekend for this with the comments due by 9am on the Monday. This is generally the last time the editors see the magazine, the next time being the digital version. Time approx 2hrs.

- Promotion

When the digital version is ready to be sent out – the editors & review panel are notified to help raise awareness of the magazine among their network.

- Article Sourcing

Call for articles is open all year as we will just hold those submitted in between the planning timeline over to the next edition. If there are particular topics that we feel would make good articles the editors are expected to help source potential authors and of course if they see good presentations again encourage those speakers to turn their presentation in to text.

- Flying the flag

Throughout the year the editors are expected to positively “fly the flag” of Oracle Scene, as a volunteer this will include, at the annual conference, taking part in the community networking to encourage future authors amongst the community.

If you are interested in a deputy editor role then submit your application now.

Check out UKOUG webpage for more details.

Slides from the OUG Ireland meet-ups

I've finally gotten the time (and the permissions from the presenters) to make the slides from the first two OUG Ireland meet-ups available.

I've posted them on SlideShare and I've embedded them in this blog post too.

OUG Ireland Meet-up 12th January - DevOps and Oracle Cloud from Brendan Tierney

20th Oct. 2016 OUG Ireland Meet-up - Updates from Oracle Open World 2016 from Brendan Tierney

Formatting results from ORE script in a SELECT statement

This blog post looks at how to format the output or the returned returns from an Oracle R Enterprise (ORE), user defined R function, that is run using a SELECT statement in SQL.

Sometimes this can be a bit of a challenge to work out, but it can be relatively easy once you have figured out how to do it. The following examples works through some scenarios of different results sets from a user defined R function that is stored in the Oracle Database.

To run that user defined R function using a SELECT statement I can use one of the following ORE SQL functions.

• rqEval
• rqTableEval
• "rqGroupEval"
• rqRowEval

For simplicity we will just use the first of these ORE SQL functions to illustrate the problem and how to go about solving it. The rqEval ORE SQL function is a generate purpose function to call a user defined R script stored in the database. The function does not require any input data set and but it will return some data. You could use this to generate some dummy/test data or to find some information in the database. Here is noddy example that returns my name.

BEGIN
   --sys.rqScriptDrop('GET_NAME');
   sys.rqScriptCreate('GET_NAME',
      'function() {
         res<-data.frame("Brendan")
         res
         } ');
END;

To call this user defined R function I can use the following SQL.

select *
from table(rqEval(null,
                  'select cast(''a'' as varchar2(50))  from dual',
                  'GET_NAME') );  

For text strings returned you need to cast the returned value giving a size.

If we have a numeric value being returned we can don't have to use the cast and instead use '1' as shown in the following example. This second example extends our user defined R function to return my name and a number.

BEGIN
   sys.rqScriptDrop('GET_NAME');
   sys.rqScriptCreate('GET_NAME',
      'function() {
         res<-data.frame(NAME="Brendan", YEAR=2017)
         res
         } ');
END;

To call the updated GET_NAME function we now have to process two returned columns. The first is the character string and the second is a numeric.

select *
from table(rqEval(null,
                  'select cast(''a'' as varchar2(50)) as "NAME", 1 AS YEAR  from dual',
                  'GET_NAME') );                  

These example illustrate how you can process character strings and numerics being returned by the user defined R script.

The key to setting up the format of the returned values is knowing the structure of the data frame being returned by the user defined R script. Once you know that the rest is (in theory) easy.

Oracle Data Miner 4.2 New Features

Oracle Data Miner 4.2 (part of SQL Developer 4.2) got released as an Early Adopter versions (EA) a few weeks ago.

I had an earlier blog post that looked that the new Oracle Advanced Analytics in-database new features with the Oracle 12.2 Database.

With the new/updated Oracle Data Miner (ODMr) there are a number of new features. These can be categories as 1) features all ODMr users can use now, 2) New features that are only usable when using Oracle 12.2 Database, and 3) Updates to existing algorithms that have been exposed via the ODMr tool.

The following is a round up of the main new features you can enjoy as part of ODMr 4.2 (mainly covering points 1 and 2 above)

• You can now schedule workflows to run based on a defined schedule
• Support for additional data types (RAW, ROWID, UROWID, URITYPE)
• Better support for processing JSON data in the JSON Query node
• Additional insights are displayed as part of the Model Details View
• Additional alert monitoring and reporting
• Better support for processing in-memory data
• A new R Model node that allows you to include in-database ORE user defined R function to support model build, model testing and applying of new model.
• New Explicit Semantic Analysis node (Explicit Feature Extraction)
• New Feature Compare and Test nodes
• New workflow status profiling perfoance improvements
• Refresh the input data definition in nodes
• Attribute Filter node now allows for unsupervised attribute importance ranking
• The ability to build Partitioned data mining models

Look out for the blog posts on most of these new features over the coming months.

WARNING: Most of these new features requires an Oracle 12.2 Database.

Explicit Semantic Analysis setup using SQL and PL/SQL

In my previous blog post I introduced the new Explicit Semantic Analysis (ESA) algorithm and gave an example of how you can build an ESA model and use it. Check out this link for that blog post.

In this blog post I will show you how you can manually create an ESA model. The reason that I'm showing you this way is that the workflow (in ODMr and it's scheduler) may not be for everyone. You may want to automate the creation or recreation of the ESA model from time to time based on certain business requirements.

In my previous blog post I showed how you can setup a training data set. This comes with ODMr 4.2 but you may need to expand this data set or to use an alternative data set that is more in keeping with your domain.

Setup the ODM Settings table

As with all ODM algorithms we need to create a settings table. This settings table allows us to store the various parameters and their values, that will be used by the algorithm.

-- Create the settings table
CREATE TABLE ESA_settings (
    setting_name VARCHAR2(30),
    setting_value VARCHAR2(30));

-- Populate the settings table
-- Specify ESA. By default, Naive Bayes is used for classification.
-- Specify ADP. By default, ADP is not used. Need to turn this on.
BEGIN
    INSERT INTO ESA_settings (setting_name, setting_value)
    VALUES (dbms_data_mining.algo_name,       
           dbms_data_mining.algo_explicit_semantic_analys);
   
    INSERT INTO ESA_settings (setting_name, setting_value)
    VALUES (dbms_data_mining.prep_auto,dbms_data_mining.prep_auto_on);
  
    INSERT INTO ESA_settings (setting_name, setting_value)
    VALUES (odms_sampling,odms_sampling_disable);
  
    commit;
END; 

These are the minimum number of parameter setting needed to run the ESA algorithm. The other ESA algorithm setting include:

Setup the Oracle Text Policy

You also need to setup an Oracle Text Policy and a lexer for the Stopwords.

DECLARE
   v_policy_name  varchar2(30);
   v_lexer_name   varchar2(3)
BEGIN
    v_policy_name  := 'ESA_TEXT_POLICY';
    v_lexer_name   := 'ESA_LEXER';
    ctx_ddl.create_preference(v_lexer_name, 'BASIC_LEXER');
    v_stoplist_name := 'CTXSYS.DEFAULT_STOPLIST';  -- default stop list
    ctx_ddl.create_policy(policy_name => v_policy_name, lexer => v_lexer_name, stoplist => v_stoplist_name);
END;

Create the ESA model

Once we have the settings table created with the parameter values set for the algorithm and the Oracle Text policy created, we can now create the model.

To ensure that the Oracle Text Policy is applied to the text we want to analyse we need to create a transformation list and add the Text Policy to it.

We can then pass the text transformation list as a parameter to the CREATE_MODEL, procedure.

DECLARE
   v_xlst              dbms_data_mining_transform.TRANSFORM_LIST;
   v_policy_name       VARCHAR2(130) := 'ESA_TEXT_POLICY';
   v_model_name        varchar2(50) := 'ESA_MODEL_DEMO_2';
BEGIN
   v_xlst := dbms_data_mining_transform.TRANSFORM_LIST();
   DBMS_DATA_MINING_TRANSFORM.SET_TRANSFORM(v_xlst, '"TEXT"', NULL, '"TEXT"', '"TEXT"', 'TEXT(POLICY_NAME:'||v_policy_name||')(MAX_FEATURES:3000)(MIN_DOCUMENTS:1)(TOKEN_TYPE:NORMAL)');

    DBMS_DATA_MINING.DROP_MODEL(v_model_name, TRUE);
    DBMS_DATA_MINING.CREATE_MODEL(
        model_name          => v_model_name,
        mining_function     => DBMS_DATA_MINING.FEATURE_EXTRACTION,
        data_table_name     => 'WIKISAMPLE',
        case_id_column_name => 'TITLE',
        target_column_name  => NULL,
        settings_table_name => 'ESA_SETTINGS',
        xform_list          => v_xlst);
END;

NOTE: Yes we could have merged all of the above code into one PL/SQL block.

Use the ESA model

We can now use the FEATURE_COMPARE function to use the model we just created, just like I did in my previous blog post.

SELECT FEATURE_COMPARE(ESA_MODEL_DEMO_2
               USING 'Oracle Database is the best available for managing your data' text 
               AND USING 'The SQL language is the one language that all databases have in common' text) similarity 
FROM DUAL;

Go give the ESA algorithm a go and see where you could apply it within your applications.

next OUG Ireland Meet-up on 12th January

Our next OUG Ireland Meet-up with be on Thursday 12th January, 2017.
The theme for this meet up is DevOps and How to Migrate to the Cloud.
Come along on the night here about these topics and how companies in Ireland are doing these things.
Venue : Bank of Ireland, Grand Canal Dock, Dublin.
The agenda for the meet-up is:
18:00-18:20 Sign-in, meet and greet, networking, grab some refreshments, etc
18:20-18:30 : Introductions & Welcome, Agenda, what is OUG Ireland, etc.
18:30-19:00 : Dev Ops and Oracle PL/SQL development - Alan McClean
Abstract
In recent years the need to deliver changes to production as soon as possible has led to the rise of continuous delivery; continuous integration and continuous deployment. These issues have become standards in the application development, particularly for code developed in languages such as Java. However, database development has lagged behind in supporting this paradigm. There are a number of steps that can be taken to address this. This presentation examines how database changes can be delivered in a similar manner to other languages. The presentation will look at unit testing frameworks, code reviews and code quality as well as tools for managing database deployment.
19:00-1930 : Simplifying the journey to Oracle Cloud : Decision makers across Managers, DBA’s and Cloud Architects who need to progress an Oracle Cloud Engagement in the organization - Ken MacMahon, Head of Oracle Cloud Services at Version1
Abstract
The presentation will cover the 5 steps that Version 1 use to try and help customers with Oracle Cloud adoption in the organisation. By attending you will hear, how to deal with cloud adoption concerns, choose candidates for cloud migration, how to design the cloud architecture, how to use automation and agility in your Cloud adoption plans, and finally how to manage your Cloud environment.

This event is open to all, you don't have to be a member of the user group and best of all it is a free event. So spread the word with all your Oracle developer, DBAs, architects, data warehousing, data vizualisations, etc people.
We hope you can make it! and don't forget to register for the event.