Tag Archives: Obiee

Looker for OBIEE Experts: Introduction and Concepts

Looker for OBIEE Experts: Introduction and Concepts

Recently I've been doing some personal study around various areas including streaming, machine learning and data visualization and one of the tools that got my attention is Looker. I've initially heard about Looker from a Drill to Detail podcast and increasingly been hearing about it in conferences and use cases together with other cloud solutions like BigQuery, Snowflake and Fivetran.

I decided to give it a try myself and, since most of my career was based on Oracle Business Intelligence (OBI) writing down a comparison between the tools that could help others sharing my experience getting introduced to Looker.

OBIEE's Golden Feature: The Semantic Model

As you probably know if you have been working with OBIEE for some time the centrepiece of its architecture is the Semantic Model contained in the Repository (RPD)

Looker for OBIEE Experts: Introduction and Concepts

In the three layers of the RPD, we model our source data (e.g. database tables) into attributes, metrics, hierarchies which can then be easily dragged and dropped by the end-user in the analysis or data visualization.

I called the RPD "OBIEE's Golden Feature" because to me it's the main benefit of the platform: abstracting the data complexity from end-users and, at the same time, optimizing the query definition to take care of all the features that could be set in the datasource. The importance of the RPD is also its centrality: within the traditional OBIEE all Analysis and Dashboard had to be based on Subject Areas exposed by the RPD meaning that the definition of the metrics was done in a unique place in a consistent manner and then spread across all the reporting providing the unique source of truth for the important KPIs in the company typical of what Gartner calls the Mode 1 Analytics.

RPD Development Speed Limitation and Mode 2 Analytics

The RPD is a centralized binary object within the OBIEE infrastructure: in order to develop and test a full OBIEE instance is required, and the merges between different streams are natively performed via the RPD's admin tool.

This complexity unified to the deep knowledge required to correctly build a valid semantic model limits the number of people being able to create and publish new content thus slowing down the process from data to insights typical of the centralized Mode 1 Analytic platform provided centrally by IT teams. Moreover, RPD development is entirely point-and-click within the admintool which is somehow considered slow and old fashion in a world of scripting, code versioning and git merging. Several solutions are out in the market (including Rittman Mead Developer Toolkit) to enhance the agility of the development but still, the skills and the toolset required to develop new content makes it a purely IT manageable solution.

In order to overcome this limitation several tools like Tableau, QlikView or Oracle's Data Visualization (included in OAC or in the Desktop version) give all the power in the ends of the end-user: from data-sources to graphing, the tools allow an end-to-end data discovery to visualization journey. The problem with those tools (called Mode 2 Analytics by Gartner) is that there is no central definition of the KPI since it's demanded to every analyst. All those tools are addressing the problem by providing some sort of datasource certification allowing a datasource to be visible and reusable publicly only when it's validated centrally. Again, for most of those tools, the modelling is done in a visual format, which makes it difficult to debug, version control and automate. I've been speaking about this subject in my presentation "DevOps and OBIEE do it before it's too late".

What if we could provide the same centralized source of truth data modelling with an easily scriptable syntax that can be developed from business users without any deep knowledge of SQL or source tables? Well, what we just described is LookML!

LookML

LookerML takes the best part of OBIEE: the idea of a modelling layer and democratizes it in order to be available to all business user with a simple language and set of concepts. Moreover, the code versioning is embedded in the tool, so there's no need to teach git branch, commit, push or pull to non-IT people.

So, what are the concepts behing LookerML and how can you get familiar with it when comparing it to the medatada modelling in the RPD?

LookML Concepts

Let's start from the basic of the RPD modelling: a database table. In LookerML each table is represented by an object called View (naming is a bit confusing). Moreover, LookerML's Views can be used not only to map existing database tables but also to create new tables based on existing content and a SQL definition, like the opaque views in OBIEE. On top of this LookML allows the phisicalization of those objects (into a table) and the definition of a schedule for the refresh. This concept is very useful when aggregates are needed, the aggregate definition (SQL) is defined within the LookML View together with the related refresh schedule.

Looker for OBIEE Experts: Introduction and Concepts

The View itself defines only the source, a bit like the RPD's physical layer, the next step is defining how multiple Views interact within each other, or, in OBIEE terms, the Business Layer. In LookML there is an entity called Explores and is the place where we can define which Views we want to group together, and what's the linkage between them. Multiple Explores are defined in a Model, which should be unique per database. So, in OBIEE words, a Model can be compared to a Business Model with Explores being a subset of Facts and Dimensions grouped in a Subject Area.

Looker for OBIEE Experts: Introduction and Concepts

Ok, all "easy" so far, but where do we map the columns? and where do we set the aggregations? As you might expect both are mapped within a LookML View into Fields. Fields is a generic term which includes in both metrics and attributes, LookML naming is the below:

  • Dimension: in OBIEE's terms attributes of a dimension. The terminology is confusing since in LookML the Dimension is the column itself while in OBIEE terms is the table. A Dimension can be a column value or a combination of multiple values (like OBIEE's BM Logical Sources formulas). A Dimension in LookML can't have any aggregation (as in OBIEE).
  • Measures: in OBIEE's terms a metric. The definition includes, the source formula in SQL syntax, the type of aggregation (min/max/count...) and the drill fields.
    Filters: this is not something usually defined in OBIEE's RPD, filters are a way of passing a user choice based on a column value back to an RPD calculation formula, a bit like, for the OBIEE experts, overriding session variables with dashboard prompt values.
  • Parameters: again this is not something usually defined in OBIEE's RPD, you can think a Parameter as a way of setting up variables function. E.g. a Parameter with values SUM, AVG, MIN, MAX could be used to change how a certain Measure is aggregated

All good so far? Stick with me and in the future we'll explore more about LookML syntax and Looker in general!

ChitChat for OBIEE – Now Available as Open Source!

ChitChat is the Rittman Mead commentary tool for OBIEE. ChitChat enhances the BI experience by bridging conversational capabilities into the BI dashboard, increasing ease-of-use and seamlessly joining current workflows. From tracking the history behind analytical results to commenting on specific reports, ChitChat provides a multi-tiered platform built into the BI dashboard that creates a more collaborative and dynamic environment for discussion.

Today we're pleased to announce the release into open-source of ChitChat! You can find the github repository here: https://github.com/RittmanMead/ChitChat

Highlights of the features that ChitChat provides includes:

  • Annotate - ChitChat's multi-tiered annotation capabilities allow BI users to leave comments where they belong, at the source of the conversation inside the BI ecosystem.

  • Document - ChitChat introduces the ability to include documentation inside your BI environment for when you need more that a comment. Keeping key materials contained inside the dashboard gives the right people access to key information without searching.

  • Share - ChitChat allows to bring attention to important information on the dashboard using the channel or workflow manager you prefer.

  • Verified Compatibility - ChitChat has been tested against popular browsers, operating systems, and database platforms for maximum compatibility.

Getting Started

In order to use ChitChat you will need OBIEE 11.1.1.7.x, 11.1.1.9.x or 12.2.1.x.

First, download the application and unzip it to a convenient access location in the OBIEE server, such as a home directory or the desktop.

See the Installation Guide for full detail on how to install ChitChat.

Database Setup

Build the required database tables using the installer:

cd /home/federico/ChitChatInstaller
java -jar SocializeInstaller.jar -Method:BuildDatabase -DatabasePath:/app/oracle/oradata/ORCLDB/ORCLPDB1/ -JDBC:"jdbc:oracle:thin:@192.168.0.2:1521/ORCLPDB1" -DatabaseUser:"sys as sysdba" -DatabasePassword:password -NewDBUserPassword:password1

The installer will create a new user (RMREP), and tables required for the application to operate correctly. -DatabasePath flag tells the installer where to place the datafiles for ChitChat in your database server. -JDBC indicates what JDBC driver to use, followed by a colon and the JDBC string to connect to your database. -DatabaseUser specifies the user to access the database with. -DatabasePassword specifies the password for the user previously given. -NewDBUserPassword indicates the password for the new user (RMREP) being created.

WebLogic Data Source Setup

Add a Data Source object to WebLogic using WLST:

cd /home/federico/ChitChatInstaller/jndiInstaller
$ORACLE_HOME/oracle_common/common/bin/wlst.sh ./create-ds.py

To use this script, modify the ds.properties file using the method of your choice. The following parameters must be updated to reflect your installation: domain.name, admin.url, admin.userName, admin.password, datasource.target, datasource.url and datasource.password.

Deploying the Application on WebLogic

Deploy the application to WebLogic using WLST:

cd /home/federico/ChitChatInstaller
$ORACLE_HOME/oracle_common/common/bin/wlst.sh ./deploySocialize.py

To use this script, modify the deploySocialize.py file using the method of your choice. The first line must be updated with username, password and url to connect to your Weblogic Server instance. The second parameter in deploy command must be updated to reflect your ChitChat access location.

Configuring the Application

ChitChat requires several several configuration parameters to allow the application to operate successfully. To change the configuration, you must log in to the database schema as the RMREP user, and update the values manually into the APPLICATION_CONSTANT table.

See the Installation Guide for full detail on the available configuration and integration options.

Enabling the Application

To use ChitChat, you must add a small block of code on any given dashboard (in a new column on the right-side of the dashboard) where you want to have the application enabled:

<rm id="socializePageParams"
user="@{biServer.variables['NQ_SESSION.USER']}"
tab="@{dashboard.currentPage.name}"
page="@{dashboard.name}">
</rm>
<script src="/Socialize/js/dashboard.js"></script>

Congratulations! You have successfully installed the Rittman Mead commentary tool. To use the application to its fullest capabilities, please refer to the User Guide.

Problems?

Please raise any issues on the github issue tracker. This is open source, so bear in mind that it's no-one's "job" to maintain the code - it's open to the community to use, benefit from, and maintain.

If you'd like specific help with an implementation, Rittman Mead would be delighted to assist - please do get in touch with Jon Mead or DM us on Twitter @rittmanmead to get access to our Slack channel for support about ChitChat.

Please contact us on the same channels to request a demo.

ChitChat for OBIEE – Now Available as Open Source!

ChitChat is the Rittman Mead commentary tool for OBIEE. ChitChat enhances the BI experience by bridging conversational capabilities into the BI dashboard, increasing ease-of-use and seamlessly joining current workflows. From tracking the history behind analytical results to commenting on specific reports, ChitChat provides a multi-tiered platform built into the BI dashboard that creates a more collaborative and dynamic environment for discussion.

Today we're pleased to announce the release into open-source of ChitChat! You can find the github repository here: https://github.com/RittmanMead/ChitChat

Highlights of the features that ChitChat provides includes:

  • Annotate - ChitChat's multi-tiered annotation capabilities allow BI users to leave comments where they belong, at the source of the conversation inside the BI ecosystem.

  • Document - ChitChat introduces the ability to include documentation inside your BI environment for when you need more that a comment. Keeping key materials contained inside the dashboard gives the right people access to key information without searching.

  • Share - ChitChat allows to bring attention to important information on the dashboard using the channel or workflow manager you prefer.

  • Verified Compatibility - ChitChat has been tested against popular browsers, operating systems, and database platforms for maximum compatibility.

Getting Started

In order to use ChitChat you will need OBIEE 11.1.1.7.x, 11.1.1.9.x or 12.2.1.x.

First, download the application and unzip it to a convenient access location in the OBIEE server, such as a home directory or the desktop.

See the Installation Guide for full detail on how to install ChitChat.

Database Setup

Build the required database tables using the installer:

cd /home/federico/ChitChatInstaller  
java -jar SocializeInstaller.jar -Method:BuildDatabase -DatabasePath:/app/oracle/oradata/ORCLDB/ORCLPDB1/ -JDBC:"jdbc:oracle:thin:@192.168.0.2:1521/ORCLPDB1" -DatabaseUser:"sys as sysdba" -DatabasePassword:password -NewDBUserPassword:password1  

The installer will create a new user (RMREP), and tables required for the application to operate correctly. -DatabasePath flag tells the installer where to place the datafiles for ChitChat in your database server. -JDBC indicates what JDBC driver to use, followed by a colon and the JDBC string to connect to your database. -DatabaseUser specifies the user to access the database with. -DatabasePassword specifies the password for the user previously given. -NewDBUserPassword indicates the password for the new user (RMREP) being created.

WebLogic Data Source Setup

Add a Data Source object to WebLogic using WLST:

cd /home/federico/ChitChatInstaller/jndiInstaller  
$ORACLE_HOME/oracle_common/common/bin/wlst.sh ./create-ds.py

To use this script, modify the ds.properties file using the method of your choice. The following parameters must be updated to reflect your installation: domain.name, admin.url, admin.userName, admin.password, datasource.target, datasource.url and datasource.password.

Deploying the Application on WebLogic

Deploy the application to WebLogic using WLST:

cd /home/federico/ChitChatInstaller  
$ORACLE_HOME/oracle_common/common/bin/wlst.sh ./deploySocialize.py

To use this script, modify the deploySocialize.py file using the method of your choice. The first line must be updated with username, password and url to connect to your Weblogic Server instance. The second parameter in deploy command must be updated to reflect your ChitChat access location.

Configuring the Application

ChitChat requires several several configuration parameters to allow the application to operate successfully. To change the configuration, you must log in to the database schema as the RMREP user, and update the values manually into the APPLICATION_CONSTANT table.

See the Installation Guide for full detail on the available configuration and integration options.

Enabling the Application

To use ChitChat, you must add a small block of code on any given dashboard (in a new column on the right-side of the dashboard) where you want to have the application enabled:

<rm id="socializePageParams"  
user="@{biServer.variables['NQ_SESSION.USER']}"  
tab="@{dashboard.currentPage.name}"  
page="@{dashboard.name}">  
</rm>  
<script src="/Socialize/js/dashboard.js"></script>  

Congratulations! You have successfully installed the Rittman Mead commentary tool. To use the application to its fullest capabilities, please refer to the User Guide.

Problems?

Please raise any issues on the github issue tracker. This is open source, so bear in mind that it's no-one's "job" to maintain the code - it's open to the community to use, benefit from, and maintain.

If you'd like specific help with an implementation, Rittman Mead would be delighted to assist - please do get in touch with Jon Mead or DM us on Twitter @rittmanmead to get access to our Slack channel for support about ChitChat.

Please contact us on the same channels to request a demo.

Rittman Mead at OUG Norway 2018

Rittman Mead at OUG Norway 2018

This week I am very pleased to represent Rittman Mead by presenting at the Oracle User Group Norway Spring Seminar 2018 delivering two sessions about Oracle Analytics, Kafka, Apache Drill and Data Visualization both on-premises and cloud. The OUGN conference it's unique due to both the really high level of presentations (see related agenda) and the fascinating location being the Color Fantasy Cruiseferry going from Oslo to Kiev and back.

Rittman Mead at OUG Norway 2018

I'll be speaking on Friday 9th at 9:30AM in Auditorium 2 about Visualizing Streams on how the world of Business Analytics has changed in recent years and how to successfully build a Modern Analytical Platform including Apache Kafka, Confluent's recently announced KSQL and Oracle's Data Visualization.

Rittman Mead at OUG Norway 2018

On the same day at 5PM, always in Auditorium 2, I'll be delivering the session OBIEE: Going Down the Rabbit Hole: providing details, built on experience, on how diagnostic tools, non standard configuration and well defined processes can enhance, secure and accelerate any analytical project.

If you’re at the event and you see me in sessions, around the conference or during my talks, I’d be pleased to speak with you about your projects and answer any questions you might have.

Rittman Mead at OUG Norway 2018

Rittman Mead at OUG Norway 2018

This week I am very pleased to represent Rittman Mead by presenting at the Oracle User Group Norway Spring Seminar 2018 delivering two sessions about Oracle Analytics, Kafka, Apache Drill and Data Visualization both on-premises and cloud. The OUGN conference it's unique due to both the really high level of presentations (see related agenda) and the fascinating location being the Color Fantasy Cruiseferry going from Oslo to Kiev and back.

Rittman Mead at OUG Norway 2018

I'll be speaking on Friday 9th at 9:30AM in Auditorium 2 about Visualizing Streams on how the world of Business Analytics has changed in recent years and how to successfully build a Modern Analytical Platform including Apache Kafka, Confluent's recently announced KSQL and Oracle's Data Visualization.

Rittman Mead at OUG Norway 2018

On the same day at 5PM, always in Auditorium 2, I'll be delivering the session OBIEE: Going Down the Rabbit Hole: providing details, built on experience, on how diagnostic tools, non standard configuration and well defined processes can enhance, secure and accelerate any analytical project.

If you’re at the event and you see me in sessions, around the conference or during my talks, I’d be pleased to speak with you about your projects and answer any questions you might have.