Category Archives: Rittman Mead
Unify – An Insight Into the Product
Monday, 12 Jun saw the official release of Unify, Rittman Mead's very own connector between Tableau and OBIEE. It provides a simple but powerful integration between the two applications that allows you to execute queries through OBIEE and manipulate and render the datasets using Tableau.
Why We Made It
One of the first questions of course would be why we would want to do this in the first place. The excellent thing about OBI is that it acts as an abstraction layer on top of a database, allowing analysts to write efficient and secure reports without going into the detail of writing queries. As with any abstraction, it is a trade of simplicity for capability. Products like Tableau and Data Visualiser seek to reverse this trade, putting the power back in the hands of the report builder. However, without quoting Spiderman, care should be taken when doing this.
The result can be that users write inefficient queries, or worse still, incorrect ones. We know there will be some out there that use self service tools as purely a visualisation engine, simply dropping pre-made datasets into it. If you are looking to produce sustainable, scalable and accessible reporting systems, you need to tackle the problem both at the data acquisition stage as well as the communication stage at the end.
If you are already meeting both requirements, perhaps by using OBI with Data Visualiser (formerly Visual Analyser) or by other means then that's perfectly good. However, We know from experience that there are many of you out there that have already invested heavily into both OBI and Tableau as separate solutions. Rather than have them linger in a state of conflict, we'd rather we nurse them into a state of symbiosis.
The idea behind Unify is that it bridges this gap, allowing you to use your OBIEE system as an efficient data acquisition platform and Tableau as an intuitive playground for users who want to do a a bit more with their data. Unify works by using the Tableau Web Data Connector as a data source and then our customised software to act as an interface for creating OBIEE queries and them exporting them into Tableau.
How It Works
Unify uses Tableau's latest Web Data Connector data source to allow us to dynamically query OBIEE and extract data into Tableau. Once a dataset is extracted into Tableau, it can be used with Tableau as normal, taking advantages of all of the powerful features of Tableau. This native integration means you can add in OBIEE data sources just as you would add in any others - Excel files, SQL results etc. Then you can join the data sources using Tableau itself, even if the data sources don't join up together in the background.
First you open up Tableau and add a Web Data Connector source:
Then give the link to the Unify application, e.g. http://localhost:8080/unify. This will open up Unify and prompt you to login with your OBIEE credentials. This is important as Unify operates through the OBIEE server layer in order to maintain all security permissions that you've already defined.
Now that the application is open, you can make OBIEE queries using the interface provided. This is a bit like Answers and allows you to query from any of your available subject areas and presentation columns. The interface also allows you to use filtering, column formulae and OBIEE variables much in the same way as Answers does.
Alternatively, you can open up an existing report that you've made in OBIEE and then edit it at your leisure. Unify will display a preview of the dataset so you can tweak it until you are happy that is what you want to bring into Tableau.
Once you're happy with your dataset, click the Unify button in the top right and it will export the data into Tableau. From this point, it behaves exactly as Tableau does with any other data set. This means you can join your OBIEE dataset to external sources, or bring in queries from multiple subject areas from OBIEE and join them in Tableau. Then of course, take advantage of Tableau's powerful and interactive visualisation engine.
Unify Server
Unify comes in desktop and server flavours. The main difference between the two is that the server version allows you to upload Tableau workbooks with OBIEE data to Tableau Server and refresh them. With the desktop version, you will only be able to upload static workbooks that you've created, however with the server version of Unify, you can tell Tableau Server to refresh data from OBIEE in accordance with a schedule. This lets you produce production quality dashboards for your users, sourcing data from OBIEE as a well as any other source you choose.
Unify Your Data
In a nutshell, Unify allows you to combine the best aspects of two very powerful BI tools and will prevent the need for building all of your reporting artefacts from scratch if you already have a good, working system.
I hope you've found this brief introduction to Unify informative and if you have OBIEE and would like to try it with Tableau, I encourage you to register for a free desktop trial. If you have any questions, please don't hesitate to get in touch.
Unify – An Insight Into the Product
Monday, 12 Jun saw the official release of Unify, Rittman Mead's very own connector between Tableau and OBIEE. It provides a simple but powerful integration between the two applications that allows you to execute queries through OBIEE and manipulate and render the datasets using Tableau.
Why We Made It
One of the first questions of course would be why we would want to do this in the first place. The excellent thing about OBI is that it acts as an abstraction layer on top of a database, allowing analysts to write efficient and secure reports without going into the detail of writing queries. As with any abstraction, it is a trade of simplicity for capability. Products like Tableau and Data Visualiser seek to reverse this trade, putting the power back in the hands of the report builder. However, without quoting Spiderman, care should be taken when doing this.
The result can be that users write inefficient queries, or worse still, incorrect ones. We know there will be some out there that use self service tools as purely a visualisation engine, simply dropping pre-made datasets into it. If you are looking to produce sustainable, scalable and accessible reporting systems, you need to tackle the problem both at the data acquisition stage as well as the communication stage at the end.
If you are already meeting both requirements, perhaps by using OBI with Data Visualiser (formerly Visual Analyser) or by other means then that's perfectly good. However, We know from experience that there are many of you out there that have already invested heavily into both OBI and Tableau as separate solutions. Rather than have them linger in a state of conflict, we'd rather we nurse them into a state of symbiosis.
The idea behind Unify is that it bridges this gap, allowing you to use your OBIEE system as an efficient data acquisition platform and Tableau as an intuitive playground for users who want to do a a bit more with their data. Unify works by using the Tableau Web Data Connector as a data source and then our customised software to act as an interface for creating OBIEE queries and them exporting them into Tableau.
How It Works
Unify uses Tableau's latest Web Data Connector data source to allow us to dynamically query OBIEE and extract data into Tableau. Once a dataset is extracted into Tableau, it can be used with Tableau as normal, taking advantages of all of the powerful features of Tableau. This native integration means you can add in OBIEE data sources just as you would add in any others - Excel files, SQL results etc. Then you can join the data sources using Tableau itself, even if the data sources don't join up together in the background.
First you open up Tableau and add a Web Data Connector source:
Then give the link to the Unify application, e.g. http://localhost:8080/unify. This will open up Unify and prompt you to login with your OBIEE credentials. This is important as Unify operates through the OBIEE server layer in order to maintain all security permissions that you've already defined.
Now that the application is open, you can make OBIEE queries using the interface provided. This is a bit like Answers and allows you to query from any of your available subject areas and presentation columns. The interface also allows you to use filtering, column formulae and OBIEE variables much in the same way as Answers does.
Alternatively, you can open up an existing report that you've made in OBIEE and then edit it at your leisure. Unify will display a preview of the dataset so you can tweak it until you are happy that is what you want to bring into Tableau.
Once you're happy with your dataset, click the Unify button in the top right and it will export the data into Tableau. From this point, it behaves exactly as Tableau does with any other data set. This means you can join your OBIEE dataset to external sources, or bring in queries from multiple subject areas from OBIEE and join them in Tableau. Then of course, take advantage of Tableau's powerful and interactive visualisation engine.
Unify Server
Unify comes in desktop and server flavours. The main difference between the two is that the server version allows you to upload Tableau workbooks with OBIEE data to Tableau Server and refresh them. With the desktop version, you will only be able to upload static workbooks that you've created, however with the server version of Unify, you can tell Tableau Server to refresh data from OBIEE in accordance with a schedule. This lets you produce production quality dashboards for your users, sourcing data from OBIEE as a well as any other source you choose.
Unify Your Data
In a nutshell, Unify allows you to combine the best aspects of two very powerful BI tools and will prevent the need for building all of your reporting artefacts from scratch if you already have a good, working system.
I hope you've found this brief introduction to Unify informative and if you have OBIEE and would like to try it with Tableau, I encourage you to register for a free desktop trial. If you have any questions, please don't hesitate to get in touch.
Unify: See Your Data From Every Perspective
Ad hoc access to accurate and secured data has always been the goal of business intelligence platforms. Yet, most fall short of balancing the needs of business users with the concerns of IT.
Rittman Mead has worked with hundreds of organizations representing all points on the spectrum between agility and governance. Today we're excited to announce our new product, Unify, which allows Tableau users to directly connect to OBIEE, providing the best of both worlds.
Governed Data Discovery
Business users get Tableau's intuitive data discovery features and the agility they need to easily blend their departmental data without waiting on IT to incorporate it into a warehouse. IT gets peace of mind, knowing their mission-critical data is protected by OBIEE's semantic layer and row-level security.
Unify Essentials
Unify runs as a desktop app, making it easy for departmental Tableau users to connect to a central OBIEE server. Unify also has a server option that runs alongside OBIEE, for organizations with a large Tableau user base or those using Tableau Server.
Desktop installation and configuration is simple. Once installed, users can query OBIEE from within Tableau with just a few clicks. Have a look at these short videos demonstrating setup and use of Unify.
Available Today
Download your free 7-day trial of Unify Desktop here.
No Tableau Desktop license? No problem. Unify is compatible with Tableau Public.
OAC: Essbase and DVCS
Finally managed to get around to having a proper look at Essbase within Oracle Analytics Cloud Service (OAC) after a busy couple of months. This post focusses mainly on initial impressions on the ‘out of the box’ the Essbase side of this - which we will explore in more detail in future posts, as well as more detail on the use of Essbase with DVCS.
Using Essbase with DVCS
One of the features we are keen to explore more in this context is the integration of Essbase and the Data Visualisation Cloud Service (DVCS). One point that we found that we do not think is being expressed clearly anywhere else we have seen is how to configure this: In setting up our OAC instance, we were having difficulty coming up with a combination of configuration selections that enables Essbase and DV to work at the same time.
Oracle documentation (such as the price list) suggest that both should be available within Standard Edition OAC:
But Doc ID 2265410.1 on MoS suggests, by needing to add a security rule to the Essbase OAC, that two OAC instances are required. We could not find any reference to this requirement in Oracle documentation or blogs on the subject, but it transpires after checking with Oracle that this is indeed the case – Essbase and DV need to be on separate OAC instances.
Essbase
Looking purely at Essbase, my initial reaction is very positive…whilst the interface is different (I am sure tears will be shed for EAS & Studio in the foreseeable future…although given the way some stalwarts are still clinging on the last surviving copies of the Excel Add In, maybe not too imminently), once the surface of the new interface is scratched more...ahem…’seasoned’ developers will take comfort from being able to do a lot of the same things as they currently can. I am also confident it will fulfil one of the stated objectives in making it easier for non-experts to be able quickly and easily deploy cubes for analysis purposes.
Whilst the manual application and cube maintenance tools through the OAC front-end seem resilient and work effectively, I think some aspects will be difficult to use as the primary maintenance method in a production system - the ‘breadcrumb’ method afforded to dimension maintenance in particular will start to get fiddly to use with a dimension of any sort of volume. The application and cube Import (from a formatted Excel spreadsheet) facility is great - to my mind, a bit like a supercharged and easier-to-use Outline Load Utility in Hyperion Planning - and the ability to refresh the spreadsheet from a deployed cube is a good feature that shouldn’t have been taken for granted. I know Excel is regarded as the Devil’s work in some BI quarters…I personally don’t feel that way until it is being used as a database (or as some form of primary data storage)…but in this context, it is quick & easy to use, on most people’s desktops straightway, and is intuitive.
Still in the Excel corner, on the Smartview side, the addition of the Cube Designer extension (requiring Smartview 11.1.2.5.700) to be able to consider & change the more generic aspects (not members) of the ‘cube maintenance’ spreadsheets is a nice touch that makes this more straightforward and removes the need to pay strict attention to the spreadsheet layout. The ‘treeview’ style hierarchy viewer also helps make sense of the parent-child members that need to be detailed on the individual dimension tabs.
One issue that has flitted across my mind at this early stage is that of rules files. Whilst the Import facility creates these for you (as with creating a cube from Essbase Studio) which is welcome, and rules files created in an on-prem system can be uploaded (again, welcome), the on-board rules file editor is text based:
I’m not too sure how many people have created or edited rules files like this before (although I’d hazard a guess), but whilst the presence of any means to create, amend, or even tweak a file is good, it remains to be seen how usable this approach is. The alternative is to resubmit from the maintenance spreadsheet thus getting it created / amended for you or to maintain in on-prem system…but seeing as this platform is an alternative to (rather than an augmentation of) on prem for a lot of people, I’m not sure how practical this is.
Whilst the existing tools look really promising, I can’t help but think there will be occasions going forwards where it might be advantageous to be able to create a rules file to run an uploaded file outside of them: time will tell.
The Command Line Tool (downloadable from OAC-Essbase / Utilities) is a little limited at the moment, but goes some way towards filling the potential gap left by the absence of client-side EssMsh and can only grow with further releases: from the Oracle OAC documentation...
In conclusion, first impressions are very favourable. There are changes (eg Security), new features (eg Sandboxing), and I am sure there will be gaps for those considering moving from existing on-prem applications - for example, as I have seen someone else reference, there does not seem to be any reference to partitions in the front end or the import spreadsheet layout - so whilst there is a lot with which we will quite quickly feel familiar, there are also going to be new areas and new practices for us to get into step with: as above, we will look to explore some of these in future posts.
Overview of the new Cloudera Data Science Workbench
Recently Cloudera released a new product called Cloudera Data Science Workbench(CDSW)
Being a Cloudera Partner, we at Rittman Mead are always excited when something new comes along.
The CDSW is positioned as a collaborative platform for data scientists/engineers and analysts, enabling larger teams to work in a self-service manner through a web browser. This browser application is effectively an IDE for R, Python and Scala - all your favorite toys!
The CDSW is deployed onto edge nodes of your CDH cluster, providing easy access to your HDFS data and the Spark2 and Impala engines. This means that team members can immediately start working on their projects, accessing full datasets and share analysis and results. A CDSW Project can include reusable code and snippets, libraries etc helping your teams to collaborate. Oh, and these projects can be linked with Github repos to help keep version history.
The workbench is used to fire up user session with R, Python or Scala inside a dedicated Docker engines. These engines can be customised, or extended, like any other Docker images to include all your favourite R packages and Python libraries. Using HDFS, Hive, Spark2 or Impala the workload can then be distributed over to the CDH cluster, by use of your preferred methods, without having to configure anything. This engine (virtual machine, really) runs for as long as the analysis. Any logs or output files need to be saved in the project folder, which is mounted inside the engine and saved on the CDSW master node. The master node is a gateway node to the CDH cluster and can scale out to many worker nodes to distribute the Docker engines
And under the hood we also have Kubernetes to schedule user workload across the worker nodes and provide CPU and memory isolation
So far I find the IDE to be a bit too simple and lacking features compared to e.g. RStudio Server. But the ease of use and the fact that everything is automatically configured makes the CDSW an absolute must for any Cloudera customer with data science teams. Also, I'm convinced that future releases will add loads of cool functionality
I spent about two days building a new cluster on AWS and install the Cloudera Data Science Workbench, just an indication of how easy it is to get up and running. Btw, it also runs in the cloud (Iaas) ;)
Want to know more or see a live demo? Contact us at info@rittmanmead.com
