Category Archives: Rittman Mead

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.

OAC – Thoughts on Moving to the Cloud

Last week, I spent a couple of days with Oracle at Thames Valley Park and this presented me with a perfect opportunity to sit down and get to grips with the full extent of the Oracle Analytics Cloud (OAC) suite...without having to worry about client requirements or project deadlines!

As a company, Rittman Mead already has solid experience of OAC, but my personal exposure has been limited to presentations, product demonstrations, reading the various postings in the blog community and my existing experiences of Data Visualisation and BI cloud services (DVCS and BICS respectively). You’ll find Francesco’s post a good starting place if you need an overview of OAC and how it differs (or aligns) to Data Visualisation and BI Cloud Services.

So, having spent some time looking at the overall suite and, more importantly, trying to interpret what it could mean for organisations thinking about making a move to the cloud, here are my top three takeaways:

Clouds Come In Different Shapes and Flavours

Two of the main benefits that a move to the cloud offers are simplification in platform provisioning and an increase in flexibility, being able to ramp up or scale down resources at will. These both comes with a potential cost benefit, depending on your given scenario and requirement. The first step is understanding the different options in the OAC licensing and feature matrix.

First, we need to draw a distinction between Analytics Cloud and the Autonomous Analytics Cloud (interestingly, both options point to the same page on cloud.oracle.com, which makes things immediately confusing!). In a nutshell though, the distinction comes down to who takes responsibility for the service management: Autonomous Analytics Cloud is managed by Oracle, whilst Analytics Cloud is managed by yourself. It’s interesting to note that the Autonomous offering is marginally cheaper.

Next, Oracle have chosen to extend their BYOL (Bring Your Own License) option from their IaaS services to now incorporate PaaS services. This means that if you have existing licenses for the on-premise software, then you are able to take advantage of what appears to be a significantly discounted cost. Clearly, this is targeted to incentivise existing Oracle customers to make the leap into the Cloud, and should be considered against your ongoing annual support fees.

Since the start of the year, Analytics Cloud now comes in three different versions, with the Standard and Enterprise editions now being separated by the new Data Lake edition. The important things to note are that (possibly confusingly) Essbase is now incorporated into the Data Lake edition of the Autonomous Analytics Cloud and that for the full enterprise capability you have with OBIEE, you will need the Enterprise edition. Each version inherits the functionality of its preceding version: Enterprise edition gives you everything in the Data Lake edition; Data Lake edition incorporates everything in the Standard edition.

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Finally, it’s worth noting that OAC aligns to the Universal Credit consumption model, whereby the cost is determined based on the size and shape of the cloud that you need. Services can be purchased as Pay as You Go or Monthly Flex options (with differential costing to match). The PAYG model is based on hourly consumption and is paid for in arrears, making it the obvious choice for short term prototyping or POC activities. Conversely, the Monthly Flex model is paid in advance and requires a minimum 12 month investment and therefore makes sense for full scale implementations. Then, the final piece of the jigsaw comes with the shape of the service you consume. This is measured in OCPU’s (Oracle Compute Units) and the larger your memory requirements, the more OCPU’s you consume.

Where You Put Your Data Will Always Matter

Moving your analytics platform into the cloud may make a lot of sense and could therefore be a relatively simple decision to make. However, the question of where your data resides is a more challenging subject, given the sensitivities and increasing legislative constraints that exist around where your data can or should be stored. The answer to that question will influence the performance and data latency you can expect from your analytics platform.

OAC is architected to be flexible when it comes to its data sources and consequently the options available for data access are pretty broad. At a high level, your choices are similar to those you would have when implementing on-premise, namely:

  • perform ELT processing to transform and move the data (into the cloud);
  • replicate data from source to target (in the cloud) or;
  • query data sources via direct access.

These are supplemented by a fourth option to use the inbuilt Data Connectors available in OAC to connect to cloud or on-premise databases, other proprietary platforms or any other source accessible via JDBC. This is probably a decent path for exploratory data usage within DV, but I’m not sure it would always make the best long term option.

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Unsurprisingly, with the breadth of options comes a spectrum of tooling that can be used for shifting your data around and it is important to note that depending on your approach, additional cloud services may or may not be required.

For accessing data directly at its source, the preferred route seems to be to use RDC (Remote Data Connector), although it is worth noting that support is limited to Oracle (including OLAP), SQL Server, Teredata or DB2 databases. Also, be aware that RDC operates within WebLogic Server and so this will be needed within the on-premise network.

Data replication is typically achieved using Data Sync (the reincarnation of the DAC, which OBIA implementers will already be familiar with), although it is worth mentioning that there are other routes that could be taken, such as APEX or SQL Developer, depending on the data volumes and latency you have to play with.

Classic ELT processing can be achieved via Oracle Data Integrator (either the Cloud Service, a traditional on-premise implementation or a hybrid-model).

Ultimately, due care and attention needs to be taken when deciding on your data architecture as this will have a fundamental effect on the simplicity with which data can be accessed and interpreted, the query performance achieved and the data latency built into your analytics.

Data Flows Make For Modern Analytics Simplification

A while back, I wrote a post titled Enabling a Modern Analytics Platform in which I attempted to describe ways that Mode 1 (departmental) and Mode 2 (enterprise) analytics could be built out to support each other, as opposed to undermining one another. One of the key messages I made was the importance of having an effective mechanism for transitioning your Mode 1 outputs back into Mode 2 as seamlessly as possible. (The same is true in reverse for making enterprise data available as an Mode 1 input.)

One of the great things about OAC is how it serves to simplify this transition. Users are able to create analytic content based on data sourced from a broad range of locations: at the simplest level, Data Sets can be built from flat files or via one of the available Data Connectors to relational, NoSQL, proprietary database or Essbase sources. Moreover, enterprise curated metadata (via RPD lift-and-shift from an on-premise implementation) or analyst developed Subject Areas can be exposed. These sources can be ‘mashed’ together directly in a DV project or, for more complex or repeatable actions, Data Flows can be created to build Data Sets. Data Flows are pretty powerful, not only allowing users to join disparate data but also perform some useful data preparation activities, ranging from basic filtering, aggregation and data manipulation actions to more complex sentiment analysis, forecasting and even some machine learning modelling features. Importantly, Data Flows can be set to output their results to disk, either written to a Data Set or even to a database table and they can be scheduled for repetitive refresh.

For me, one of the most important things about the Data Flows feature is that it provides a clear and understandable interface which shows the sequencing of each of the data preparation stages, providing valuable information for any subsequent reverse engineering of the processing back into the enterprise data architecture.

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In summary, there are plenty of exciting and innovative things happening with Oracle Analytics in the cloud and as time marches on, the case for moving to the cloud in one shape or form will probably get more and more compelling. However, beyond a strategic decision to ‘Go Cloud’, there are many options and complexities that need to be addressed in order to make a successful start to your journey - some technical, some procedural and some organisational. Whilst a level of planning and research will undoubtedly smooth the path, the great thing about the cloud services is that they are comparatively cheap and easy to initiate, so getting on and building a prototype is always going to be a good, exploratory starting point.

Why DevOps Matters for Enterprise BI

Why DevOps Matters for Enterprise BI

Why are people frustrated with their existing enterprise BI tools such as OBIEE? My view is because it costs too much to produce relevant content. I think some of this is down to the tools themselves, and some of it is down to process.

Starting with the tools, they are not “bad” tools; the traditional licensing model can be expensive in today’s market, and traditional development methods are time-consuming and hence expensive. The vendor’s response is to move to the cloud and to highlight cost savings that can be made by having a managed platform. Oracle Analytics Cloud (OAC) is essentially OBIEE installed on Oracle’s servers in Oracle’s data centres with Oracle providing your system administration, coupled with the ability to flex your licensing on a monthly or annual basis.

Cloud does give organisations the potential for more agility. Provisioning servers can no longer hold up the start of a project, and if a system needs to increase capacity, then more CPUs or nodes can be added. This latter case is a bit murky due to the cost implications and the option to try and resolve performance issues through query efficiency on the database.

I don’t think this solves the problem. Tools that provide reports and dashboards are becoming more commoditised, up and coming vendors and platform providers are offering the service for a fraction of the cost of the traditional vendors. They may lack some of the enterprise features like open security models; however, these are an area that platform providers are continually improving. Over the last 10 years, Oracle's focus for OBIEE has been on more on integration than innovation. Oracle DV was a significant change; however, there is a danger that Oracle lost the first-mover advantage to tools such as Tableau and QlikView. Additionally, some critical features like lineage, software lifecycle development, versioning and process automation are not built in to OBIEE and worse still, the legacy design and architecture of the product often hinders these.

So this brings me back round to process. Defining “good” processes and having tools to support them is one of the best ways you can keep your BI tools relevant to the business by reducing the friction in generating content.

What is a “good” process? Put simply, a process that reduces the time between the identification of a business need and the realising it with zero impact on existing components of the system. Also, a “good” process should provide visibility of any design, development and testing, plus documentation of changes, typically including lineage in a modern BI system. Continuous integration is the Holy Grail.

This why DevOps matters. Using automated migration across environments, regression tests, automatically generated documentation in the form of lineage, native support for version control systems, supported merge processes and ideally a scripting interface or API to automate the generation of repetitive tasks such as changing the data type of a group of fields system-wide, can dramatically reduce the gap from idea to realisation.

So, I would recommend that when looking at your enterprise BI system, you not only consider the vendor, location and features but also focus on the potential for process optimisation and automation. Automation could be something that the vendor builds into the tool, or you may need to use accelerators or software provided by a third party. Over the next few weeks, we will be publishing some examples and case studies of how our BI and DI Developer Toolkits have helped clients and enabled them to automate some or all of the BI software development cycle, reducing the time to release new features and increasing the confidence and robustness of the system.

Spring into action with our new OBIEE 12c Systems Management & Security On Demand Training course

Rittman Mead are happy to release a new course to the On Demand Training platform.

The OBIEE 12c Systems Management & Security course is the essential learning tool for any developers or administrators who will be working on the maintenance & optimisation of their OBIEE platform.

Baseline Validation Tool

View lessons and live demos from our experts on the following subjects:

  • What's new in OBIEE 12c
  • Starting & Stopping Services
  • Managing Metadata
  • System Preferences
  • Troubleshooting Issues
  • Caching
  • Usage Tracking
  • Baseline Validation Tool
  • Direct Database Request
  • Write Back
  • LDAP Users & Groups
  • Application Roles
  • Permissions

Get hands on with the practical version of the course which comes with an OBIEE 12c training environment and 9 lab exercises.
System Preferences

Rittman Mead will also be releasing a theory version of the course. This will not include the lab exercises but gives each of the lessons and demos that you'd get as part of the practical course.

Course prices are as follows:

OBIEE 12c Systems Management & Security - PRACTICAL - $499

  • 30 days access to lessons & demos
  • 30 days access to OBIEE 12c training environment for lab exercises
  • 30 days access to Rittman Mead knowledge base for Q&A and lab support

OBIEE 12c Systems Management & Security - THEROY - $299

  • 30 days access to lessons & demos
  • 30 days access to Rittman Mead knowledge base for Q&A

To celebrate the changing of seasons we suggest you Spring into action with OBIEE 12c by receiving a 25% discount on both courses until 31st March 2018 using voucher code:

ODTSPRING18

Access both courses and the rest of our catalog at learn.rittmanmead.com

Confluent Partnership

Confluent

Here at Rittman Mead, we are continually broadening the scope and expertise of our services to help our customers keep pace with today's ever-changing technology landscape. One significant change we have seen over the last few years is the increased adoption of data streaming. These solutions can help solve a variety of problems, from real-time data analytics to forming the entire backbone of an organisation's data architecture. We have worked with a number of different technologies that can enable this, however, we often see that Kafka ticks the most boxes.

This is reflected by some of the recent blog posts you will have seen like Tom Underhill hooking up his gaming console to Kafka and Paul Shilling’s piece on collating sailing data. Both these posts try and use day to day or real-world examples to demonstrate some of the concepts behind Kafka.

In conjunction with these, we have been involved in more serious proofs of concepts and project with clients involving Kafka, which no doubt we will write about in time. To help us further our knowledge and also immerse ourselves in the developer community we have decided to become Confluent partners. Confluent was founded by the people who initially developed Kafka at LinkedIn and provides a managed and supported version of Kafka through their platform.

We chose Confluent as we saw them as the driving force behind Kafka, plus the additions they are making to the platform such as the streaming API and KSQL are opening a lot of doors for how streamed data can be used.

We look forward to growing our knowledge and experience in this area and the possibilities that working with both Kafka and Confluent will bring us.